JP5522613B1 - Ticket issuing machine, station name enumeration program for ticket issuing machine, computer-readable recording medium in which station name enumeration program for ticket issuing machine is recorded, and station name enumeration method in ticket issuing machine - Google Patents

Abstract

The present invention provides a ticket issuing machine capable of searching for a station name including input characters and displaying the station name side by side for easy viewing by an operator.
SOLUTION: A reading kana classification device for classifying a frequent word (kana) and a first unknown word from candidate station names (kana), and any character of the frequent word (kana) and the first unknown word Character position analysis device that analyzes the position of the search character included in the column, candidate station name classification device that classifies frequent words, inclusion station name, second unknown word from candidate station name, and candidate station name based on the position of the search character Candidate station names are arranged based on the first condition that is the way of arrangement of, and the character string common to the candidate station names is detected and counted for each frequent word, inclusive station name, second unknown word, and between the candidate station names counted A ticket issuing machine is configured from an analyzer that arranges candidate station names based on a second condition that is a method of arranging candidate station names based on the number of common character strings.
[Selection] Figure 3

Description

  The present invention relates to a ticket issuing machine, a station name enumeration program for a ticket issuing machine, a computer-readable recording medium in which a station name enumeration program for a ticket issuing machine is recorded, and a station name enumerating method in the ticket issuing machine. is there.

  Conventionally, a commuter pass issuing device is used at, for example, a window of a transportation station such as a railway, and issues a commuter pass. An operator (an employee of a railway company) inputs a departure / arrival station and a transit station in a commuter pass issuing device in response to a request from a commuter pass purchaser. The commuter pass issuance device searches for a station name including the character and displays it on the display unit when a part of the character of the station name is input. If there are a plurality of station name candidates that include the input characters, the commuter pass issuing device displays them all (see Patent Document 1 below).

JP 2002-92660 A

  When a plurality of station names including the input characters are displayed, the operator can quickly perform the operation if the plurality of station names can be displayed in an easy-to-read manner for the operator.

  The present invention has been proposed in view of the above circumstances. In other words, the station name enumeration program, the station name enumeration program of the ticket issuing machine, and the station name enumeration program of the ticket issuing machine that can search the station name including the entered characters and display the station name side by side for easy viewing by the operator are recorded. Another object of the present invention is to provide a computer-readable recording medium and a station name enumeration method in a ticket issuing machine.

  In order to achieve the above object, a ticket issuing machine according to the present invention extracts a station name including input characters from a station name storage unit in which a station name is read and stored together with a pseudonym and displays it as a candidate station name. In the ticket-issuing machine, a frequent word storage unit in which frequently used words that are frequently used in the station name are stored together with a reading kana, and a frequent word storage unit that is used to generate the frequent occurrence from the reading kana of the candidate station name. A reading kana part for classifying the first kanji part of the word, the first unknown word part which is a reading kana part other than the reading kana part of the frequent word, and the reading kana of the frequent word divided by the reading kana classification part Among the part and the first unknown word part, using a character position analysis device that analyzes the position of the input character included in any character string, the frequent word storage unit and the station name storage unit, Previous From the candidate station name, the frequent word part, the inclusion station name part that is the station name included in the character string of the candidate station name, and the second unknown word part that is a character other than the frequent word part and the inclusion station name part, Candidate station name classifying device to classify, and the candidate station name based on the position of the input character analyzed by the character position analysis device as a first condition, the candidate station name is aligned based on the first condition, Character strings common to candidate station names are detected and counted for each frequent word part, inclusion station name part, and second unknown word part classified by the candidate station name classification device, and common to the counted candidate station names An analysis device that arranges the candidate station names based on the second condition while conforming to the first condition, with the arrangement of the candidate station names based on the number of character strings to be performed as a second condition It is characterized in that.

  In addition, the analysis device, as a third condition, the arrangement of the candidate station name based on the position in the candidate station name of the frequent word portion, the inclusion station name portion, and the second unknown word portion divided by the candidate station name classification device, In accordance with the second condition, the candidate station names are arranged based on the third condition.

  Further, the first condition is that the position of the input character is the head of any one of the reading kana part of the frequent word and the first unknown word part classified by the reading kana classification device The candidate station name is to be placed at the top of the arrangement, and the second condition is to place the candidate station name having a large number of character strings in common between the counted candidate station names at the top of the arrangement, The third condition is that the position of the candidate station name at the top of the candidate station name of the frequent word part, the inclusion station name part, and the second unknown word part classified by the candidate station name classification device, the candidate station name in the middle, the end The candidate station names are arranged in the order of the candidate station names.

  The station name enumeration program for a ticket-issuing machine according to the present invention uses a computer, a frequent word storage device in which frequent words that are frequently used in the station name are read and stored together with kana, and the frequent word storage unit. A reading kana classification device for classifying the reading kana part of the frequent word from the reading kana of the candidate station name, and a first unknown word part that is a reading kana part other than the reading kana part of the frequent word, A character position analysis device that analyzes the position of the input character included in any one of the reading kana part and the first unknown word part of the frequent word classified by the kana classification device, and the frequent occurrence Using the word storage unit and the station name storage unit, from the candidate station name, the frequent word part, the inclusion station name part which is the station name included in the character string of the candidate station name, and the frequent word part and the previous A candidate station name classifying device that classifies a second unknown word part that is a character other than the inclusion station name part, and a method of arranging the candidate station names based on the position of the input character analyzed by the character position analyzing device The candidate station names are arranged based on the first condition, and the character string common to the candidate station names is divided into the frequent word part, the inclusion station name part, and the second unknown word part that are classified by the candidate station name classification device. The candidate station name is arranged based on the number of character strings common to the candidate station names thus counted as a second condition, and the candidate is based on the second condition according to the first condition. It is characterized by functioning as an analyzer that arranges station names.

  The computer-readable recording medium according to the present invention is characterized in that a station name listing program for the above-mentioned ticket issuing machine is recorded.

  The station name enumeration method in the ticket issuing machine according to the present invention includes the frequent word storage unit in which the frequent words that are frequently used in the station name are stored together with the reading kana, and the frequent frequency for operating the computer. Using a word storage unit, from the reading kana of the candidate station name, the reading kana part for dividing the reading kana part of the frequent word and the first unknown word part that is a reading kana part other than the reading kana part of the frequent word Character position analysis for analyzing the position of the input character included in any one of the procedure and the first kanji part and the first unknown word part of the frequent words sorted by the kana classifier Using the procedure, the frequent word storage unit and the station name storage unit, from the candidate station name, the frequent word part, the included station name part which is the station name included in the character string of the candidate station name, and Candidate station name classification procedure for classifying a second unknown word part that is a character other than the outgoing word part and the inclusion station name part, and the candidate station name based on the position of the input character analyzed by the character position analysis device Based on the first condition, the candidate station names are arranged based on the first condition, and the character string common to the candidate station names is divided into the frequent word part, the inclusion station name part, and the second Detecting and counting for each unknown word part, the second condition is based on the first condition, the second condition is a way of arranging the candidate station names based on the number of character strings common to the counted candidate station names And an analysis procedure for arranging the candidate station names on the basis of

  The ticket issuing machine according to the present invention has the above-described configuration. That is, the reading kana classification device classifies the reading kana of frequent words from the reading kana of candidate station names. If the input characters match the frequently used word reading kana (or the top of the frequently used reading kana), the candidate station name is likely to be the station name desired by the operator. Similarly, if the input character matches the first unknown word (or the head of the first unknown word), the candidate station name is likely to be the station name desired by the operator. Based on this result, the analyzer arranges the candidate station names in the order of the possibility that the operator desires the station name. Therefore, the candidate station names can be displayed side by side for the operator.

  The candidate station name classification device classifies frequently used words and inclusive station names from candidate station names. The analysis device counts common character strings in the frequent words, the inclusive station names, and the second unknown words. It is highly possible that candidate station names including frequently used words with common character strings are related station names through frequently used words. Based on this result, the analysis apparatus arranges candidate station names that are highly likely to be related station names. Therefore, the candidate station names can be displayed side by side for the operator.

  The analyzer sorts the positions of the frequent words, the inclusion station names, and the second unknown word candidate station names into the top, middle, and end in order. If the frequent word or the like matches the character string of the candidate station name (or the head of the candidate station name), the candidate station name is likely to be the station name desired by the operator. Based on this result, the analyzer arranges the candidate station names in the order of the possibility that the operator desires the station name. Therefore, the candidate station names can be displayed side by side for the operator.

  The station name enumeration program of the ticket issuing machine according to the present invention, the computer-readable recording medium in which the station name enumeration program of the ticket issuing machine is recorded, and the station name enumeration method in the ticket issuing machine have the same effects as described above There is an effect.

1 is a schematic configuration diagram of a ticket issuing machine according to an embodiment of the present invention. It is a hardware block diagram of the ticket issuing machine which concerns on embodiment of this invention. It is a flowchart which shows the flow of the process by the ticket issuing machine which concerns on embodiment of this invention. It is a flowchart which shows the flow of a process of the character input into the ticket issuing machine which concerns on embodiment of this invention. It is a flowchart which shows the outline of the flow of a process by the reading kana classification device of the ticket issuing machine which concerns on embodiment of this invention. It is a flowchart which shows the flow of a process by the reading kana classification device of the ticket issuing machine which concerns on embodiment of this invention. It is a flowchart which shows the flow of a process by the character position analyzer of the ticket issuing machine which concerns on embodiment of this invention. It is a flowchart which shows the outline of the flow of the process by the candidate station name classification | category apparatus of the ticket issuing machine which concerns on embodiment of this invention. It is a flowchart which shows the flow of a process by the candidate station name classification | category apparatus of the ticket issuing machine which concerns on embodiment of this invention. It is a flowchart which shows the flow of a process by the analyzer of the ticket issuing machine which concerns on embodiment of this invention. It is a flowchart which shows the flow of a process by the analyzer of the ticket issuing machine which concerns on embodiment of this invention. It is a flowchart which shows the flow of a process by the analyzer of the ticket issuing machine which concerns on embodiment of this invention. It is a flowchart which shows the flow of a process by the analyzer of the ticket issuing machine which concerns on embodiment of this invention. It is explanatory drawing which shows a part of process result of the 1st Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates the process result of the input character. It is explanatory drawing which shows a part of process result of the 1st Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates the process result by a reading name classification device. It is explanatory drawing which shows a part of process result of the 1st Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates the process result by a character position analyzer. It is explanatory drawing which shows a part of process result of the 1st Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates the process result by a candidate station name classification | category apparatus. It is explanatory drawing which shows a part of process result of the 1st Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates a part of process result by an analyzer. It is explanatory drawing which shows a part of process result of the 1st Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates a part of process result by an analyzer. It is explanatory drawing which shows a part of process result of the 1st Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates a part of process result by an analyzer. It is explanatory drawing which shows a part of process result of the 1st Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates a part of process result by an analyzer. It is a figure which shows the process result of the 1st Example of the ticket issuing machine which concerns on embodiment of this invention. It is explanatory drawing explaining the specific example of the process in the reading kana classification device of the ticket issuing machine which concerns on embodiment of this invention. It is explanatory drawing explaining the specific example of the process in the reading kana classification device of the ticket issuing machine which concerns on embodiment of this invention. It is explanatory drawing explaining the specific example of the process in the candidate station name classification | category apparatus of the ticket issuing machine which concerns on embodiment of this invention. It is explanatory drawing explaining the specific example of the process in the candidate station name classification | category apparatus of the ticket issuing machine which concerns on embodiment of this invention. It is explanatory drawing explaining the specific example of the process in the candidate station name classification | category apparatus of the ticket issuing machine which concerns on embodiment of this invention. It is explanatory drawing explaining the specific example of the process in the candidate station name classification | category apparatus of the ticket issuing machine which concerns on embodiment of this invention. It is explanatory drawing which shows a part of process result of the 2nd Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates the process result of the input character. It is explanatory drawing which shows a part of process result of the 2nd Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates the process result by a reading name classification device. It is explanatory drawing which shows a part of process result of the 2nd Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates the process result by a character position analyzer. It is explanatory drawing which shows a part of process result of the 2nd Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates the process result by a character position analyzer. It is explanatory drawing which shows a part of process result of the 2nd Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates a part of process result by an analyzer. It is explanatory drawing which shows a part of process result of the 2nd Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates a part of process result by an analyzer. It is explanatory drawing which shows a part of process result of the 2nd Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates a part of process result by an analyzer. It is explanatory drawing which shows a part of process result of the 2nd Example of the ticket issuing machine which concerns on embodiment of this invention, and demonstrates a part of process result by an analyzer. It is a figure which shows the process result of the 2nd Example of the ticket issuing machine which concerns on embodiment of this invention. It is the figure which showed an example of the frequent word.

  Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 shows a schematic configuration of a ticket issuing machine 10 according to the present embodiment, and FIG. 2 shows a hardware configuration of the ticket issuing machine 10 according to the present embodiment.

  In FIG. 1, the ticket issuing machine 10 includes a processing unit 20, an operation unit 30, an output unit 40, and a control unit 50. As options, for example, a customer display, a power supply control device, a LAN board, etc. are added as appropriate.

  The processing unit 20 includes a credit card reader 21 that reads information stored in a credit card, and a password input pad 22 into which a password for confirming the owner of the credit card is input. The operation unit 30 is a touch panel 31 on which an operator inputs information, and may include a keyboard and a mouse. The output unit 40 includes a CRT display or a liquid crystal display 41 on which a station name searched by the control unit 50 is displayed, a ticket (for example, a coupon ticket, a commuter pass, etc.) and a ticket issuing printer 42 and a receipt printer 43 for issuing a receipt thereof, an IC It consists of an IC card printer 44 for updating the information stored in the card type ticket.

  The control unit 50 controls the processing unit 20, the operation unit 30, and the output unit 40 described above. The control unit 50 includes a station name storage unit 51, a frequent word storage unit 52, a reading kana classification device 53, a character position analysis device 54, a candidate station name classification device 55, and an analysis device 56.

  The control unit 50 detects input information from the processing unit 20 and the operation unit 30 and stores a part of an OS (Operating System) program and application programs to be executed by the CPU 1 in accordance with the program stored in the ROM 3. The respective devices 53 to 56 are controlled using the RAM 2, and the processing result of the input information is output to the output unit 40.

  Each of the devices 53 to 56 constituting the control unit 50 can be realized by a hardware configuration, a DSP (Digital Signal Processor), an MPU (Micro Processing Unit), and software. For example, when realized by software, it can be realized by operating a program stored in the RAM or ROM by the ticket issuing machine 10 provided with a CPU, MPU, RAM, ROM or the like.

  Therefore, a program that causes the ticket issuing machine 10 to operate so as to perform the functions of the devices 53 to 56 described above can be realized by recording the program on a recording medium such as a CD-ROM and causing the ticket issuing machine 10 to read the program. . As a recording medium for recording the program, for example, a flexible disk, an optical disk or the like can be used in addition to the CD-ROM. It can also be realized by downloading the program to the ticket issuing machine 10 via a network such as the Internet.

  The station name storage unit 51 and the frequent word storage unit 52 are stored in the ROM 3, RAM 2, HDD 4, etc. included in the control unit 50.

  The station name storage unit 51 stores the station names of railway companies in the whole country or a part of the area together with the reading pseudonym. The frequent word storage unit 52 stores frequently used words used as station names together with reading kana.

  Here, a frequent word refers to a character that is frequently used for a station name among characters included in the station name. Frequent words include, for example, words related to directions such as “east, west, south, north, new, middle, book, center, front”, old country names such as “Kii, Yamato”, words related to city names, place names, “ This includes words related to routes provided by railway companies such as “Odakyu, Nishitetsu” (see FIG. 38).

  Station names and frequent words are generally written in kanji, kana characters, or alphabets, but since most of them are written in kanji, the following are commonly used station names and frequently used character strings: “Station name (kanji)” “Frequently used words (kanji)”. On the other hand, the character string of the reading kana is written as “station name (reading kana)” “frequent word (reading kana)”.

  The reading kana sorting device 53, the character position analyzing device 54, the candidate station name sorting device 55, and the analyzing device 56 analyze the candidate station names. Here, a processing procedure by the control unit 50 using the devices 53 to 56 will be described with reference to the drawings. FIG. 3 shows the flow of processing by the control unit 50 from step S1 to step S9. 4 to 13 show the details of the processing flow, FIGS. 14 to 22 and FIGS. 27 to 37 show the results of the processing, and FIGS. 23 to 28 show specific examples of the processing. .

  In step S1, the control unit 50 recognizes a kana character input to the touch panel 31 by the operator. Hereinafter, “input kana characters” are referred to as “search characters”.

  In step S2, the control unit 50 extracts a station name (reading pseudonym) including a search character from the station name storage unit 51 by partial match search (see FIG. 4). The extracted plurality of station names (reading pseudonyms) are candidate station names (reading pseudonyms) that are candidates for the station name desired by the operator. The search character may be one or more characters, and may be the second character, the third character, etc. in addition to the initial character of the desired station name. That is, the control unit 50 extracts all of the station name having the initial character as the search character, the station name included in the middle, and the station name having the end.

  In step S <b> 3, the reading kana sorting device 53 uses the frequent word storage unit 52 to match the character string of the candidate station name (reading kana) with the frequent word (reading kana) and the first unknown word. (See FIG. 5). Here, the first unknown word means a reading kana of a candidate station name (reading kana) excluding a frequently used word (reading kana). Hereinafter, of the classified candidate station names (reading kana), a portion that coincides with the frequent word (reading kana) is referred to as [A], and a portion that becomes the first unknown word is referred to as [C].

  The reading-kana classification device 53 analyzes and processes the syntax of the candidate station name (reading-kana) based on the longest match method (see FIG. 6). The position at which the analysis is started is a fictitious head character provided for convenience before the initial character of the candidate station name (reading kana) character string (step S3-1). The reading-kana classification device 53 adds the initial character, the second character, the third character,. It is determined whether or not the column matches a frequent word (reading kana) (steps S3-2 to S3-6). As a result of the determination, if there is a character string that matches the frequent word (reading kana) among these character strings, the character string is set to [A] (step S3-4), and the character string that does not match is set to [C]. (Step S3-5). The reading-kana classification device 53 determines all character strings that start with the first letter of the candidate station name (reading-kana).

  The reading-a-kana sorting device 53 uses a character string starting with the initial character of the candidate station name (a reading kana) (to a character string having a common starting position in the character string of the candidate station name (a reading kana)), and [A] is It is determined whether it is included (step S3-7). As a result of the determination, if [A] is included in the candidate station name (reading pseudonym), step S3-8 is processed, and if not included, step S3-12 is processed.

  The reading kana sorting device 53 uses a plurality of [A as a character string starting with an initial letter of a candidate station name (a reading kana) (to a character string having a common analysis start position in the character string of the candidate station name (a reading kana)). ] Is included, the character string is segmented by adopting [A] having the largest number of characters (step S3-8). The reading-kana classification device 53 moves the starting position of the analysis to the first letter of the character string after [A] that is divided among the character strings of the candidate station names (reading kana) (step S3-9).

  On the other hand, if [A] is not included in the character string starting from the initial letter of the candidate station name (reading kana) (in the character string of the candidate station name (reading kana) where the analysis start position is common), The reading kana sorting device 53 is the first character string in a character string starting from the initial character of the candidate station name (reading kana) (character string having the same starting position in the character string of the candidate station name (reading kana)). A certain character is adopted as [C] (step S3-12). The reading kana sorting device 53 moves the position to start analysis to the next character after [C], which is adopted in the character string of the candidate station name (reading kana) (step S3-13).

  The reading-kana classification device 53 determines whether or not the analysis start position is a fictitious end character provided for convenience after the last character of the candidate station name (reading kana) (step S3-10). As a result of the determination, if the analysis start position is the fictitious end character, step S3-11 is processed, and if it is not a fictitious end character, the above-described processing is repeated.

  If the adopted [C] continues, the reading-kana classification device 53 collects [C] into one and ends the process (step S3-11).

  Specifically, in FIG. 23, it is assumed that the candidate station name (reading kana) is “Kaiyamato” and the frequent words (reading kana) are “Kai” and “Yamato”. Candidate station names (reading pseudonyms) are processed in order from an imaginary head character, which is an acronym of “Kaiyamato”, to “K”, “Kai”, “Kaiya”... “Kaiyamato” (see FIG. 23A). The acronym “K” is [C] because it does not match a frequent word (reading kana). “Chi” composed of the initial character “K” and the second character “I” matches the frequent word (reading kana) and is therefore classified as [A]. This process is repeated until “Kaiyamato”.

  The position where the analysis is started moves to “Y”, which is the next character after “K”, which is [A]. Since “YA” is not the last position at which to start the analysis, processing is performed in order from “YA” to “YAMA” and “YAMATO”, which are the initial characters of the character string after “KAI” (see FIG. 23B). Since “Ya” and “Yama” do not match frequently-occurring words (reading kana), both are [C]. “Yamato” is classified as [A] because it coincides with a frequent word (reading kana). When the analysis start position is moved to the fictitious end character that is the next character after “Yamato”, the process ends because the analysis start position is last.

  In this way, “Kaiyamato” is divided into two parts “Kai” and “Yamato” that coincide with the frequent words (Yomikana), and Yomiana is assumed to have a syntax composed of [A] and [A]. Analysis is performed by the sorting device 53 (see FIG. 23C).

  According to another specific example, in FIG. 24, it is assumed that the candidate station name (reading kana) is “Yamato Futami” and the frequent word (reading kana) is “Yamato”. Candidate station names (reading pseudonyms) are sequentially processed from the fictitious first character, which is the initial of “Yamato Futami”, to “Ya”, “Yama”, “Yamato”... “Yamato Futami” (see FIG. 24A). Since “Ya” and “Yama” do not match frequently-occurring words (reading kana), both are [C]. “Yamato” is classified as [A] because it coincides with a frequent word (reading kana).

  The position where the analysis is started moves to “F”, which is the next character of “Yamato” which is [A]. Since “F” is not the last position to start analysis, “F”, “Fut” and “Futami”, which are initial characters of the character string after “Yamato”, are processed in order (see FIG. 24B). Since these do not coincide with frequent words (reading kana), both are [C]. Since these character strings do not include [A], the first character string “F” among them is adopted as [C]. The analysis start position is moved to “T”, which is the next character after “F”, and “T” and “Tami” are sequentially processed (see FIG. 24C). Since these character strings do not coincide with the frequent words (yana), both are [C]. Since none of these character strings includes [A], the first character string “Ta” among them is adopted as [C]. The analysis start position is moved to “mi”, which is the next character after “ta”, and “mi” is processed (see FIG. 24D). “M”, which is a character string to be analyzed, is [C]. When the analysis start position is moved to the fictitious end character, the analysis start position is the last, and since [C] adopted continues, “F”, “TA” and “MI” are combined into one. The process ends.

  In this way, “Yamato Futami” is divided into “Yamato” that matches the frequent word (reading kana) and “Futami” that is the first unknown word that is the other character string, and [A] and [C ] Is analyzed by the kana classifier 53 (see FIG. 24E).

  In step S4, the character position analyzer 54 analyzes the position of the search character in [A] or [C] of the candidate station name (reading pseudonym) (see FIG. 7).

  The character position analysis device 54 determines whether the search character is the head of the character string [A] or [C] of the candidate station name (reading kana) (matches the character string [A] or [C]). Is determined (step S4-1). If the result of determination is that the search character is the head of the character string [A] or [C] of the candidate station name (reading pseudonym) (if it matches the character string [A] or [C]), the character position analysis device 54, the accuracy of the candidate name (reading pseudonym) is set to “high” (step S4-2). On the other hand, if the search character is not the head of the character string [A] or [C] of the candidate station name (reading pseudonym) (if it does not match the character string [A] or [C]), the accuracy is “low”. (Step S4-3). Here, the accuracy means the degree of possibility that the candidate station name matches the station name desired by the operator.

  Specifically, referring to FIG. 16 (see item 8), the search character is “Yamato”, the candidate station name (reading pseudonym) is “Yamato Futami”, among which “Yamato” is [A], “Futami” Is [C]. The search character “Yamato” is analyzed in which position of [A] or [C] that categorizes “Yamato Futami”. Since the search character “Yamato” is the head of [A] (because it matches [A]), the character position analyzer 54 sets the accuracy of “Yamato Futami” to “high”.

  On the other hand, in FIG. 16 (see item number 1), the search character is “Yamato”, the candidate station name (reading pseudonym) is “Asahiyama Dobutsuen”, and this character string is [C]. Since the search character “Yamato” is not at the beginning of [C] and does not match [C], the character position analyzer 54 sets the accuracy of “Asahiyama Dobutsuen” to “low”.

  In step S5, the candidate station name classification device 55 converts the candidate station name (kanji) into a character string of the candidate station name (kanji), a portion that matches the candidate station name (kanji) based on the frequent word storage unit 52 and the station name storage unit 51. It is divided into a part that matches the included station name (kanji), which is an included station name (kanji), and a part that becomes the second unknown word (see FIG. 8). Here, the candidate station name (kanji) character string classification target by the candidate station name classification device 55 is a character string of the candidate station name itself that is generally written, unlike the reading kana classification device 53. It is a character or a character string in alphabetical notation.

  The inclusive station name (kanji) means that a station name (kanji) is formed by a part of the character string of the candidate station name (kanji) and does not include the candidate station name (kanji) itself. For example, if the candidate station name (kanji) is “Yamato Koizumi”, “Yamato” is the inclusion station name, and “Yamato Koizumi”, which is the candidate station name (kanji) itself, is not included in the inclusion station name (kanji).

  The second unknown word refers to the character of the candidate station name (kanji) excluding the frequently used word (kanji) part and the included station name (kanji) part. Hereinafter, among the classified candidate station names (kanji), the part that matches the frequent word (kanji) [A], the part that matches the inclusion station name (kanji) [B], and the part that becomes the second unknown word [ C].

  The candidate station name classification device 55 analyzes and processes the syntax of the candidate station name (kanji) based on the longest match method (see FIG. 9). The position where the analysis is started is an imaginary head character provided for convenience before the initial character of the character string of the candidate station name (kanji) (step S5-1). The candidate station name classification device 55 accumulates the initial character of the candidate station name (kanji), the second character, the third character,... It is determined whether or not matches a frequent word (kanji) or a station name (kanji) (steps S5-2 to S5-8). The station name (kanji) does not include the candidate station name (kanji) itself (step S5-5). As a result of the determination, if there is a character string that matches the frequent word (kanji) among these character strings, the character string is set to [A] (step S5-4), and if it matches the station name (kanji), that character string. [B] (step S5-6), and if the frequent word (kanji) and the station name (kanji) do not match, the character string is set to [C] (step S5-7). If it matches the frequent word (kanji) and the station name (kanji), the frequent word (kanji) is prioritized (step S5-3). The candidate station name classification device 55 determines all character strings starting from the first letter of the character string of the candidate station name (kanji).

  The candidate station name classification device 55 includes [A] in the character string starting from the initial character of the candidate station name (kanji) (in the character string of the candidate station name (kanji) where the analysis start position is common). It is determined whether or not [B] is included (steps S5-9 and S5-12). As a result of the determination, if [A] is included in the candidate station name (kanji), step S5-10 is processed, and if [B] is included, step S5-13 is processed, and [A] [B]. If neither is included, step S5-15 is processed.

  The candidate station name classifying device 55 has a character string starting with an initial letter of the candidate station name (kanji) (a character string having a common position to start analysis in the character string of the candidate station name (kanji)), and a plurality of [A] s. If it is included, the character string is segmented by adopting [A] having the largest number of characters (step S5-10). The candidate station name classification device 55 moves the position where the analysis is started to the first letter of the character string after [A], which is adopted in the character string of the candidate station name (kanji) (step S5-11).

  If the character string starting with the initial letter of the candidate station name (kanji) (in the character string of the candidate station name (kanji), the character string having the same starting position for analysis) and multiple [B] are included, the number of characters is the largest. A large number [B] is adopted to divide the character string (step S5-13). The candidate station name classification device 55 moves the position where the analysis is started to the first letter of the character string after [B], which is classified among the character strings of the candidate station name (kanji) (step S5-14).

  If the character string starting with the initial character of the candidate station name (kanji) (in the character string of the candidate station name (kanji), the character string having the same position to start analysis) does not contain [A] [B] The candidate station name classification device 55 is a character that is the first character string in a character string starting from the initial character of the candidate station name (kanji) (a character string having a common start position in the character string of the candidate station name (kanji)). Is adopted as [C] (step S5-15). The candidate station name classification device 55 moves the position to start the analysis to the next character after [C] that is adopted in the character string of the candidate station name (kanji) (step S5-16).

  The candidate station name classification device 55 determines whether or not the analysis start position is a fictitious end character provided for convenience after the last character of the candidate station name (kanji) (step S5-17). As a result of the determination, if the analysis start position is the fictitious end character, step S5-18 is processed, and if it is not a fictitious end character, the above-described processing is repeated.

  If the adopted [C] continues, the candidate station name classification device 55 combines [C] into one (step S5-18), and determines whether the candidate station name (kanji) is classified into only [C]. (Step S5-19). As a result of the determination, if only [C] is classified, [C] is changed to [B], and the character string of the candidate station name (kanji) is reclassified (step S5-20).

  Specifically, in FIG. 25, it is assumed that the candidate station name (kanji) is “Inage Kaigan” and the station name (kanji) is “Inage”. Candidate station names (kanji) are processed in the order of “rice”, “Inage”, “Inage Sea”, and “Inage Coast” from the fictitious first character that is an initial of “Inage Coast” (see FIG. 25A). The acronym “rice” is “C” because it does not match a frequent word (kanji) nor a station name (kanji). Since “Inage” matches the station name (kanji), it is classified as [B]. “Inage Sea” is [C] like “rice”. “Inage Coast” is a candidate station name (kanji) itself, so it is [C] instead of [B].

  The position at which the analysis is started moves to “sea”, which is the next character after “Inage”, which is [B]. Since “sea” is not the last position to start the analysis, “sea” and “coast”, which are the initials of the character string after “Inage”, are sequentially processed (see FIG. 25B). Since these do not coincide with frequent words (kanji), both are [C]. Since these character strings do not include [A], the first character string “sea” is adopted as [C]. The analysis start position is moved to “shore”, which is the next character of “sea”, and “shore” is processed (see FIG. 25C). “Kishi”, which is a character string to be analyzed, is [C]. When the analysis start position is moved to the fictitious end character, the analysis start position is the last, and since the adopted [C] continues, “sea” and “shore” are combined into one, and the process ends. .

  In this way, “Inage Coast” is divided into “Inage” that matches the station name (kanji) and “Coast” that is the second unknown word that is the other character string, and [B] and [C]. Are analyzed by the candidate station name classification device 55 (see FIG. 25D).

  According to another specific example, in FIG. 26, the candidate station name (kanji) is “Yamato Futami”, the frequent word (kanji) is “Yamato”, and the station name (kanji) is “Yamato”. Candidate station names (kanji) are processed in the order of “Dai”, “Yamato”, “Daiwaji”, and “Daiwa Futami” from the fictitious first character that is an acronym for “Daiwa Futami” (see FIG. 26A). The acronym “Large” is [C] because it does not match a frequent word (kanji) nor a station name (kanji). “Yamato” matches the frequent word (kanji) and the station name (kanji), but is frequently classified as [A] because the frequent word (kanji) has priority. “Yamatoji” is [C] like “Daiwa”. Since “Yamato Futami” is the candidate station name (kanji) itself, it is [C] instead of [B].

  The position where the analysis is started moves to “2” which is the next character after “Yamato” which is [A]. Since “2” is not the last position where the analysis is started, “2” and “Futami” which are initials of the character string after “Yamato” are processed in order (see FIG. 26B). Since these do not coincide with frequent words (kanji), both are [C]. Since these character strings do not include [A], the first character string “two” among them is adopted as [C]. The position at which the analysis is started is moved to “see”, which is the next character after “two”, and “see” is processed (see FIG. 26C). The “look” that is the character string to be analyzed is [C]. When the analysis start position is moved to the fictitious end character, the analysis start position is the last, and since the adopted [C] continues, “two” and “see” are combined into one, and the process ends. .

  In this way, “Yamato Futami” is divided into “Yamato” that matches frequently-occurring words (kanji) and “Futami” that is the second unknown word that is a character string other than that, and [A] and [C] The candidate station name classification device 55 analyzes that the syntax is composed of (see FIG. 26D).

  According to another specific example, in FIG. 27, the candidate station name is “center south”, and the frequent word (kanji) is “south”. Candidate station names (kanji characters) are processed in order from an imaginary first character that is an acronym for “center south” to “se”, “sen”... “Center south” (see FIG. 27A). Since these character strings do not coincide with frequent words (kanji) or station names (kanji), and “center south” is a candidate station name (kanji) itself, both are [C].

  Since these character strings include neither [A] nor [B], the first character string “S” among them is adopted as [C]. The position at which the analysis is started is moved to “n”, which is the next character of “C”, and is processed in order from “enter” to “inter” and “inter south” (see FIG. 27B). Since none of these character strings includes [A] or [B], the first character string “N” among them is adopted as [C]. Similarly, the analysis start position is moved character by character, and “South” is processed last (see FIG. 27D). “South” is classified as [A] because it coincides with frequent words (kanji). When the analysis start position is moved to the fictitious end character, the analysis start position is the last, and since [C] adopted is continuous, “C”, “N”, “T”, and “-” are combined into one. The processing is completed after being collected.

  In this way, “center south” is divided into “center”, which is the second unknown word, and “south” that coincides with frequent words (kanji), and is composed of [C] and [A]. Is analyzed by the candidate station name classification device 55 (see FIG. 27E).

  According to another specific example, in FIG. 28, the candidate station name (kanji) is assumed to be “Yamato”. Candidate station names (kanji) are processed in the order of "mountain" and "mountain capital" from the fictitious first character that is an acronym for "mountain capital" (see FIG. 28A). Since these character strings do not match frequent words (kanji) and station names (kanji), and “Santo” is a candidate station name (kanji) itself, both are [C].

  Since these character strings include neither [A] nor [B], the first character string “mountain” among them is adopted as [C]. The position where the analysis is started is moved to “Miyako”, which is the next character after “Mountain”, and “Miyako” is processed (see FIG. 28B). “C”, which is a character string for starting analysis, is [C]. When the analysis start position is moved to the fictitious end character, the analysis start position is the last, and since [C] adopted is continuous, “mountain” and “city” are combined into one. Here, since the candidate station name (kanji) is divided only into [C], it is re-classified into [B].

  In this way, “Santo” is analyzed by the candidate station name classification device 55 as having the syntax composed of [B] (see FIG. 28C).

  In step S6, the analysis device 56 disassembles and arranges the candidate station names (kanji characters) that the character position analysis device 54 has divided into frequent words (kanji characters), inclusion station names (kanji characters), and second unknown words (see FIG. 10). ). At that time, each of the decomposed frequent words (kanji), the inclusive station name (kanji), and the second unknown word, the candidate station name (kanji) before being decomposed, and the accuracy determined by the character position analysis device 54 are shown. Associate. Hereinafter, “frequent word (kanji), candidate station name (kanji), and second unknown word” of decomposed candidate station names (kanji) will be referred to as “decomposed words”.

  To explain in more detail with a specific example, it is assumed that the candidate station name (kanji) is “Yamato Futami” in FIG. 18 (see item number 8). “Yamato Futami” is divided into “Yamato”, which is a frequent word (kanji), and “Futami”, the second unknown word that is a character string other than that. Disassembled and arranged. At that time, “Yamato” and “Futami” are each associated with “Yamato Futami”, which is the original candidate station name (kanji) before being decomposed, and the accuracy “high” determined by the character position analysis device 54 is obtained. Associated with.

  In step S7, the analysis device 56 uses a first condition (step S7-1) as a way of arranging candidate station names (reading pseudonyms), a second condition (step S7-3) as a way of arranging candidate station names (kanji), and a character code. Based on (Step S7-4), candidate station names (kanji) are arranged (see FIG. 11). Here, the first condition is the candidate station name (reading) where the position of the search character is the first in either the frequent word (reading kana) or the first unknown word contained in the character string of the candidate station name (reading kana). This means that the pseudonym) is at the top of the lineup. The second condition is to detect and count a character string common to candidate station names (kanji) for each frequent word (kanji), including station name (kanji), and second unknown word, and select candidate station name (kanji). Candidate station names (kanji) with a large number of character strings that are common to each other are placed at the top of the arrangement. The character code sequence is in ascending order.

  According to the first condition, the candidate station name (kanji) including the decomposed word whose accuracy is “high” by the character position analyzer 54 is higher, and the candidate station name (kanji) including the decomposed word whose accuracy is “low”. ) Is the lower order (step S7-1). The analysis device 56 counts common decomposed words (step S7-2). According to the second condition, as a result of counting, a candidate station name (kanji) including a decomposed word having a large number becomes higher, and a candidate station name (kanji) including a decomposed word having a small number becomes lower (step S7-3). According to the character code, the arrangement is in ascending order (step S7-4). The first condition has priority over the second condition, and the second condition has priority over the character code.

  Specifically, in FIG. 19, “Asahiyama Zoo” with the accuracy of “low” based on the first condition is subordinate. Based on the second condition, among the decomposed words common to the candidate station names (kanji), there are five “Yamato”, so that each decomposed word “Yamato” is associated with “5”. Candidate station names (Kanji) including “Yamato” are higher rank. Since there is only one “Futami” among the decomposed words, “1” is associated. Candidate station names (Kanji) including this “Futami” are lower. In the second condition, candidate station names (kanji characters) that are peers are in ascending order of character codes.

  In step S8, the analyzer 56 arranges the candidate station names (kanji characters) based on the third condition, which is how the candidate station names (kanji characters) are arranged (see FIG. 12). Here, the third condition refers to the position of the frequent words (kanji), inclusion station name (kanji), and second unknown words classified by the candidate station name classification device 55 for each candidate station name (kanji), Candidate station names (kanji) are arranged in the order of the candidate station name (kanji) whose position is the head, the candidate station name (kanji) in the middle, and the candidate station name (kanji) that is the tail.

  The analysis device 56 determines in which position of the character string of the candidate station name (kanji) the decomposed word is located (step S8-1). If the decomposed word is the beginning of the character string of the candidate station name (kanji) (if it matches the character string of the candidate station name (kanji)), it is set to “before” (step S8-2), and if it is at the end, “after” ( Step S8-3), otherwise “middle” (step S8-4). This process is repeated for all decomposed words. Candidate station names (kanji characters) including the decomposed words “before” are ranked higher, and then candidate station names (kanji characters) including the decomposed words “middle” and “after” are arranged (step S8-5).

  Specifically, in FIG. 20, the candidate station name (kanji) is “Yamato Koizumi”, and the decomposed words are “Yamato” (frequent word (kanji)) and “Koizumi” (including station name (kanji)). Suppose that The position of “Yamato” is “Previous” because it is the beginning of the character string “Yamato Koizumi”. The position of “Koizumi” is “after” because it is the end of the character string “Yamato Koizumi”. “Yamato Koizumi” where “Yamato” is “front” is higher, and “Yamato Koizumi” where “Koizumi” is “rear” is lower.

  In step S9, the analysis apparatus 56 deletes the lower one of the overlapping candidate station names (kanji) (see FIG. 13).

  As described above, the control unit 50 analyzes and arranges candidate station names (kanji characters) using the reading kana sorting device 53, the character position analyzing device 54, the candidate station name sorting device 55, and the analyzing device 56.

  Next, the effect of this embodiment will be described.

  As described above, according to the present embodiment, the reading kana sorting device 53 sorts frequent words (reading kana) from candidate station names (reading kana). If the input character matches the frequent word (reading kana) (or the top of the frequent word (reading kana)), the candidate station name is likely to be a station name desired by the operator. Similarly, if the input character matches the first unknown word (or the beginning of the first unknown word), the candidate station name (reading pseudonym) is likely to be the station name desired by the operator. For such a candidate station name (reading pseudonym), the character position analysis device 54 sets the accuracy to “high”. Based on this result, the analyzer 56 arranges the candidate station names in the order of the high possibility that the station name is desired by the operator. Therefore, the candidate station names can be displayed side by side for the operator.

  The candidate station name classifying device 55 classifies frequently used words (kanji characters) and inclusion station names (kanji characters) from candidate station names (kanji characters). The analysis device 56 counts common character strings in the frequent words (kanji), the inclusive station name (kanji), and the second unknown word. Candidate station names that include frequent words with common character strings are likely to be related station names through frequent words (kanji). Based on this result, the analyzer 56 arranges candidate station names that are highly likely to be related station names. Therefore, the candidate station names can be displayed side by side for the operator.

  The analysis device 56 sorts the positions of the frequent words (kanji), the inclusive station name (kanji), and the candidate station name (kanji) of the second unknown word into the top, middle, and end in order. The position is “front” if the frequent word (kanji) or the like is the head of the candidate station name (kanji), “back” if it is the end, and “middle” otherwise. If a frequent word (kanji) or the like matches the character string of the candidate station name (kanji) (or the head of the candidate station name (kanji)), the candidate station name is likely to be a station name desired by the operator. Based on this result, the candidate station names (kanji) are arranged in descending order of possibility of the station name desired by the operator. Therefore, the candidate station names can be displayed side by side for the operator.

  The station name enumeration program of the ticket issuing machine according to the present invention, the computer-readable recording medium in which the station name enumeration program of the ticket issuing machine is recorded, and the station name enumeration method in the ticket issuing machine have the same effects as described above There is an effect.

  Next, embodiments of the present invention will be described with reference to the drawings. 29 to 37 show a first embodiment of the present invention, and FIGS. 14 to 22 show a second embodiment of the present invention.

<First Example>
In FIG. 29, in the first embodiment, the kana character (search character) input by the operator is “Yokohama” (step S2). Candidate station names include “Yokohama”, “Shin Yokohama”, “Mutsu Yokohama” and “Yokohama”.

  In FIG. 30, “Shin Yokohama” is classified as [A], “Shin”, which coincides with a frequent word (reading kana), and “Yokohama” is classified as [C] as the first unknown word (step). S3). As for “Mutsukohama”, “Mutsu” that coincides with frequent words (reading kana) is classified as [A], and “Yokohama” is classified as [C] as described above. Similarly, “Yokohama” is classified as [C].

  In FIG. 31, “ShinYokohama” matches “Y” as the search character (“Yokohama”) (since “Yokohama” as the search character is the head of [C]), the accuracy is “high”. (Step S4). Similarly, “Mutokohama” has a high accuracy because “C” matches “Yokohama” as the search character. Similarly, “Yokohama” has a high accuracy.

  In FIG. 32, “Shin-Yokohama” is divided into “A” for “New” that matches frequently-occurring words (Kanji), and “Yokohama” that matches with the station name is divided into [B] (Step S5). In “Mutsu-Yokohama”, “Mutsu”, which coincides with frequent words (kanji), is classified as [A], and “Yokohama” is classified as [B] as described above. “Yokohama” is [C] but is reclassified as [B].

  In FIG. 33, each candidate station name (kanji) is divided into “Shin-Yokohama” into “New” and “Yokohama”, and “Mutsu-Yokohama” is separated into “Mutsu-ku” and “Yokohama” (step S6). ).

  In FIG. 34, all candidate station names (kanji) have the same accuracy with respect to the first condition because the accuracy is “high”. The common decomposed word is “Yokohama” (step S7). The decomposed words are three “Yokohama” and one “New” and “Mutsu”. Therefore, regarding the second condition, “Shin-Yokohama” including “Yokohama” which is the decomposition word and “Yokohama” which is the candidate station name (kanji) are higher rank, and “Shin-Yokohama” and “Mutsu” including “New”. "Mutsu Yokohama" including is subordinate. The decomposed words are in the order of “new” and “mutsu” in relation to the character code.

  In FIG. 35, since the decomposition word “Yokohama” matches “Yokohama” which is a candidate station name (kanji) (because it is the head), the position is “front” (step S8). Since the breakdown word “Yokohama” is the end of “Shin-Yokohama”, the position is “after”. Since the breakdown word “Yokohama” is the end of “Mutsu Yokohama”, the position is “back”. Since the new word “new” is the head of “Shin-Yokohama”, the position is “front”. Since the decomposition word “Miku” is the head of “Miku”, the position is “front”. Candidate station names (kanji) that share the breakdown word “Yokohama” with respect to the third condition are “Yokohama” with the position “Front”, and “Shin Yokohama” and “Mutsu Yokohama” with the position “Back” "Is the lower order (see FIG. 36).

  In FIG. 36, those that are lower and have duplicate item numbers are deleted (step S9), and in FIG. 37, candidate station names (kanji) are arranged in the order of “Yokohama”, “Shin Yokohama”, and “Mutsu Yokohama”.

<Second Example>
In FIG. 14, in the second embodiment, the kana character (search character) input by the operator is “Yamato” (step S2). Candidate station names include “Yamato” “Asahiyama Dobutsuen” “Kaiyamato” “Shimoyamato” “Yamato” “Yamato” “Yamato Koizumi” ) ”“ Yamato Shinjo ”and“ Yamato Futami ”.

  In FIG. 15, “Asahiyama Doubsbuen” does not match the frequent word (reading kana), so it becomes the first unknown word and is classified as [C] (step S3). “Kai Yamato” is divided into [A] each of “Kai” and “Yamato” that coincide with frequent words (reading kana). In “Simo Yamato”, “Simo” and “Yamato” that coincide with frequent words (reading kana) are each divided into [A]. “Yamato” is classified as [A] because it coincides with a frequent word (reading kana). In “Yamato Koizumi”, “Yamato”, which is a frequent word (reading pseudonym), is classified as [A], and “Koizumi” is classified as [C] as the first unknown word. In “Yamato Shinjo”, “Yamato” that coincides with a frequent word (reading kana) is classified as [A], and “Shinjo” is classified as [C] as the first unknown word. In “Yamato Futami”, “Yamato” that coincides with the frequent words (reading kana) is classified as [A], and “Futami” is classified as [C] as the first unknown word.

  In FIG. 16, “Asahiyama Dobutsuen” does not match the search character “Yamato” at the beginning, so the accuracy is “low” (step S4). The accuracy of “Kiyamato” is “high” because [A] matches the search character “Yamato”. “Shiyamato” has a high accuracy because “A” matches “Yamato” as a search character. “Yamato” has a high accuracy because [A] matches the search character “Yamato”. Since “Yamato Koizumi” matches “Yamato”, which is the character searched for [A], the accuracy is “high”. Since “Yamato Shinjo” matches “Yamato”, which is the searched character, [A] has a high accuracy. Since “Yamato Futami” matches “Yamato” which is the searched character, [A] has a high accuracy.

  In FIG. 17, “Asahiyama Zoo” is [C], but is reclassified to [B] (step S5). In “Kai Yamato”, “Kai” and “Yamato” that coincide with frequent words (Kanji) are classified into [A]. In “Shimoyamamon”, “bottom” that coincides with a frequently used word (kanji) is classified as [A], and “yamamon” as the second unknown word is classified as [C]. “Santo” is [C] but is reclassified to [B]. “Yamato” is classified as [A] because it matches a frequent word (kanji). As for “Yamato Koizumi”, “Yamato” that matches the frequent word (kanji) is classified as [A], and “Koizumi” that matches the station name (kanji) is classified as [B]. In “Yamato Shinjo”, “Yamato” that matches the frequent word (kanji) is classified as [A], and “Shinjo” that matches the station name (kanji) is classified as [B]. In “Yamato Futami”, “Yamato” that coincides with frequent words (kanji) is classified as [A], and “Koizumi” as the second unknown word is classified as [C].

  In FIG. 18, “Asahiyama Zoo” is not disassembled. “Kai Yamato” is divided into “Kai” and “Yamato” and arranged (step S6). "Shimoyamamon" is broken down into "bottom" and "yamamon". “Santo” and “Yamato” are not disassembled. “Yamato Koizumi” is broken down into “Yamato” and “Koizumi”. “Yamato Shinjo” is broken down into “Yamato” and “Shinjo”. “Yamato Futami” is broken down into “Yamato” and “Futami”.

  In FIG. 19, “Asahiyama Zoo” has a low accuracy, so it is low in terms of the first condition, and the other candidate station names (kanji) have a high accuracy, so that they are high (step S7). . The common decomposed word is “Yamato”. The decomposed words are five “Yamato”, and one each of “below”, “armor”, “mountain”, “Sanmon”, “Koizumi”, “Shinjo”, “Futami” and “Asahiyama Zoo”. Therefore, regarding the second condition, “Kai Yamato”, “Yamato”, “Yamato Koizumi”, “Yamato Shinjo”, and “Yamato Futami” including the dissociation word “Yamato” are higher, and the others are lower. The decomposed words are in the order of “lower”, “armor”, “mountain”, “yamamon”, “Koizumi”, “Shinjo”, “Futami”, “Asahiyama Zoo”.

  In FIG. 20, the decomposed word “Yamato” matches the candidate station name “Yamato” (because it is the head), so the position is “Previous”. Since the decomposition word “Yamato” is the head of “Yamato Koizumi”, the position is “Previous”. Since the disassembled word “Yamato” is the head of “Yamato Shinjo”, the position is “Previous”. Since the decomposition word “Yamato” is the head of “Yamato Futami”, the position is “Previous”. Since the decomposed word “Yamato” is the end of “Kai Yamato”, the position is “after”. Since the decomposition word “below” is the head of “Shimoyamamon”, the position is “front”. Since the disassembled word “Kai” is the head of “Kai Yamato”, the position is “Previous”. Since the disassembled word “Santo” matches the candidate station name (Kanji) “Santo” (because it is the head), the position is “Previous”. Since the disassembled word “Sanmon” is the end of “Shimoyamamon”, the position is “After”. Since the decomposition word “Koizumi” is the end of “Yamato Koizumi”, the position is “after”. Since the decomposition word “Shinjo” is the end of “Yamato Shinjo”, the position is “after”. Since the disassembled word “Futami” is the end of “Yamato Futami”, the position is “After”. Since the decomposed word “Asahiyama Zoo” matches the candidate station name (Kanji) “Asahiyama Zoo” (because it is the head), the position is “front” (see FIG. 21).

  In FIG. 21, subordinate items with overlapping item numbers are deleted. In FIG. 22, candidate station names (kanji) are “Yamato”, “Yamato Koizumi”, “Yamato Shinjo”, “Yamato Futami”, “Kai Yamato”, “Shimasumon”, “Mountain” They are arranged in the order of “City” and “Asahiyama Zoo”.

  As mentioned above, although embodiment of this invention was explained in full detail, this invention is not limited to the said embodiment. The present invention can be modified in various ways without departing from the scope of the claims.

1 CPU
2 RAM
3 ROM
4 HDD
DESCRIPTION OF SYMBOLS 10 Ticket issuing machine 20 Processing part 21 Credit card reader 22 PIN input pad 30 Operation part 31 Touch panel 40 Output part 41 Display 42 Ticket issuing printer 43 Receipt printer 44 IC card printer 50 Control part 51 Station name memory | storage part 52 Frequent word memory | storage part 53 Reading Kana classification device 54 Character position analysis device 55 Candidate station name classification device 56 Analysis device

Claims (6)

In the ticket issuing machine that extracts the station name including the input characters and displays it as a candidate station name from the station name storage unit in which the station name is read and stored together with the pseudonym,
A frequent word storage unit in which frequent words that are frequently used in the station name are stored together with reading kana,
Using the frequent word storage unit, a reading kana part of the candidate station name is used to distinguish a reading kana part of the frequent word and a first unknown word part that is a reading kana part other than the reading kana part of the frequent word. A kana sorting device;
A character position analysis device for analyzing the position of the input character included in any one of the character strings of the frequently used word and the first unknown word portion classified by the reading character classification device;
Using the frequent word storage unit and the station name storage unit, from the candidate station name, the frequent word part, an included station name part that is the station name included in a character string of the candidate station name, and the frequent word part and the inclusion Candidate station name classifying device that classifies a second unknown word part that is a character part other than the station name part,
Arranging the candidate station names based on the first condition based on the input character position analyzed by the character position analysis device, the candidate station names based on the first condition,
A common character string between the candidate station names is detected and counted for each frequent word part, inclusion station name part, and second unknown word part classified by the candidate station name classification device. An analysis device that arranges the candidate station names based on the second condition while following the first condition, with the second condition being a way of arranging the candidate station names based on the number of common character strings,
Composed of,
A ticket issuing machine characterized by that. The analysis apparatus uses the second condition word line arrangement method based on the position of the frequent station part, the included station name part, and the second unknown word part classified by the candidate station name classification apparatus in the candidate station name as the second condition. Arranging the candidate station names based on the third condition according to the conditions,
The ticket issuing machine according to claim 1. The first condition is that the position of the input character is the candidate station name that is at the head of either the reading kana part of the frequent word or the first unknown word part classified by the reading kana classification device To the top of the lineup,
The second condition is to place the candidate station names having a large number of character strings in common between the counted candidate station names in the order of arrangement,
The third condition is that the candidate station name at the top of the candidate station name at the position of the candidate station name of the frequent word part, inclusion station name part, and second unknown word part classified by the candidate station name classification device, The candidate station names are arranged in the order of the candidate station names at the end.
The ticket issuing machine according to claim 2, wherein: In the station name enumeration program of the ticket issuing machine that extracts the station name including the input characters and displays it as a candidate station name from the station name storage unit in which the station name is stored together with the reading pseudonym,
Computer
A frequent word storage device in which frequent words that are frequently used in the station name are stored together with reading kana,
Using the frequent word storage unit, a reading kana part of the candidate station name is used to distinguish a reading kana part of the frequent word and a first unknown word part that is a reading kana part other than the reading kana part of the frequent word. A kana sorting device;
A character position analysis device for analyzing the position of the input character included in any one of the character strings of the frequently used word and the first unknown word portion classified by the reading character classification device;
Using the frequent word storage unit and the station name storage unit, from the candidate station name, the frequent word part, an included station name part that is the station name included in a character string of the candidate station name, and the frequent word part and the inclusion Candidate station name classifying device that classifies a second unknown word part that is a character part other than the station name part,
Arranging the candidate station names based on the first condition based on the input character position analyzed by the character position analysis device, the candidate station names based on the first condition,
A common character string between the candidate station names is detected and counted for each frequent word part, inclusion station name part, and second unknown word part classified by the candidate station name classification device. An analysis device that arranges the candidate station names based on the second condition while following the first condition, with the second condition being a way of arranging the candidate station names based on the number of common character strings,
Make it work,
Station name enumeration program for ticket-issuing machines. The station name enumeration program of the ticket issuing machine according to claim 4 is recorded,
A computer-readable recording medium characterized by the above. In the station name enumeration method in the ticket issuing machine that extracts the station name including the input characters and displays it as a candidate station name from the station name storage unit in which the station name is stored together with the reading pseudonym,
A frequent word storage unit in which frequent words that are frequently used in the station name are stored together with reading kana,
To operate the computer,
Using the frequent word storage unit, a reading kana part of the candidate station name is used to distinguish a reading kana part of the frequent word and a first unknown word part that is a reading kana part other than the reading kana part of the frequent word. Kana classification procedure;
Character position analysis procedure for analyzing the position of the input character included in any one of the character strings of the frequently used words and the first unknown word part classified by the reading kana classification device;
Using the frequent word storage unit and the station name storage unit, from the candidate station name, the frequent word part, an included station name part that is the station name included in a character string of the candidate station name, and the frequent word part and the inclusion Candidate station name classification procedure for classifying the second unknown word part, which is a character part other than the station name part,
Arranging the candidate station names based on the first condition based on the input character position analyzed by the character position analysis device, the candidate station names based on the first condition,
A common character string between the candidate station names is detected and counted for each frequent word part, inclusion station name part, and second unknown word part classified by the candidate station name classification device. An analysis procedure for arranging the candidate station names based on the second condition while following the first condition, with the second condition being the arrangement of the candidate station names based on the number of common character strings,
Having
The station name enumeration method in the ticket issuing machine characterized by the above-mentioned.