JPH01229364A - Character processor - Google Patents

Abstract

PURPOSE:To determine a first candidate word as making an individual word pair prior to a meaning classification coorigination pair when the multiplicity of meaning of the individual word pair and the meaning classification coorigination pair occurs by providing the inputting means of the reading of a word, a word dictionary and an example dictionary, etc. CONSTITUTION:When the reading of the word is inputted from the key YOMI of a keyboard KB, an interrupt signal is sent to a microprocessor CPU, and the CPU reads out various kinds of control signals in a ROM, and performs various kinds of control. Besides, the word dictionary DIC in a RAM is constituted of word information to be used for description conversion and example information for determining the first candidate word at the time of the description conversion. Then, in a case that the multiplicity of meaning of the individual word pair and the data of the meaning classification coorigination pair occurs, since the individual word pair can be considered to be information more detailed than the meaning classification coorgination pair, the first candidate word is determined as making the individual word pair prior to the meaning classification coorigination pair. Thus, conversion into a next candidate word comes to be unnecessary, and an efficient inputting work can be realized.

Description

DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention relates to a homophone polysemy determination method in a character processing device that converts character strings.

[Conventional technology]

BACKGROUND ART Conventionally, as a character processing device for converting character strings, for example, a kana-kanji conversion device in a Japanese word processor has been widely used. As a result of Kana-Kanji conversion, many homophone candidates have been used to resolve ambiguous homophones based on the grammatical characteristics of the parts of speech of individual words. Since no consideration was given to determining homophone pairs, erroneous conversions often occurred. Therefore, attempts have been made to use word-word pairs, that is, combinations in a word-pair dictionary that stores usage examples, to determine the first candidate. In addition to individual word pairs, it is also possible to give semantically categorized semantic classification codes to i-words and use co-occurrence information between individual words and semantic codes to determine first candidates. It's here.

(The problem that the invention is trying to solve) However,
It is not efficient or realistic to cover all combinations of individual words because the number of combinations is enormous. Furthermore, in order to determine homophones based solely on co-occurrence information between words and semantic codes, the classification of semantic codes must be made very detailed, and idiomatic expressions must be treated as exceptions. Therefore,
It is desirable to use both a dictionary for individual word pairs and a dictionary for semantic classification co-occurrence pairs, but if both data for individual word pairs and data for semantic classification co-occurrence pairs hold for the same homophone pair, It is ambiguous whether to adopt it.

(Means for Solving the Problems (and Effects)) An object of the present invention is to eliminate the drawbacks of the above-mentioned conventional example.

In other words, individual word pairs are considered to be more detailed information than semantic classification co-occurrence pairs, so if polysemy occurs between individual word pairs and semantic classification co-occurrence pairs, the individual word pairs are semantically classified. It is an object of the present invention to provide a character processing device that can determine a first candidate by giving priority to co-occurrence pairs.

To this end, the present invention includes the following means.

In other words, an input means for inputting the pronunciation of the word to be converted;
A word dictionary that stores semantic attributes that encode the semantic classification of individual words in association with pronunciations and spellings, and an example dictionary that stores co-occurrence pairs between individual words and co-occurrence pairs between individual words and semantic attributes. and a clause recognition means for analyzing readings into clauses,
A usage example retrieval means searches for examples that hold true for the analyzed phrase pair, determines whether the found usage examples are individual word usages or semantic classification usage examples, and prioritizes individual word usage over semantic classification usage examples to be the first candidate in homophone polysemy. The object of the present invention is to provide a conversion means capable of determining the .

As a result, for example, even if the semantic classification example ``r ``raises an animal'' is registered, if the individual word example ``r buys alcohol'' is registered, then r It can be converted into J who buys r sake instead of J who keeps salmon.

〔Example〕

Hereinafter, the present invention will be described in detail with reference to the drawings.

FIG. 1 is an example of the overall configuration of the present invention.

In the illustrated configuration, the CPU is a microprocessor that performs calculations, logical judgments, etc. for character processing, and uses an address bus AB, a control bus CB, and a data bus D.
B to control each component connected to those buses.

Address bus AB transfers address signals indicating the components to be controlled by the microprocessor CPU. The control bus CB transfers and applies control signals for each component to be controlled by the microprocessor CPU. The data bus DB transfers data between each component device.

Next, ROM is a read-only fixed memory.

Further, the RAM is a writable random access memory having a configuration of 1 word and 16 bits, and is used for temporary storage of various data from each component.

TBUF is a document buffer, which is a memory for storing document information input from the keyboard KB.

YBUF is an input reading buffer memory that stores readings input from the keyboard KB.

DIC is a word dictionary for performing kana-kanji conversion.

DBP0OL is a homophone candidate storage memory that stores information obtained by analyzing and converting the pronunciation of YBUF into phrases.

YJPOOL is a memory that stores information on examples applied to homophone candidates in conjunction with DBP0OL.

LRNDAT is a learning data storage memory that stores the learning status of individual words and examples.

FZTBL is an attached AI71 column conversion table for making attached words correspond to combination information stored in DIC.

KB is a keyboard, which includes alphabet keys, hiragana keys, katakana keys, and other character/symbol keys-1;
It is equipped with various function keys such as a conversion key for instructing conversion.

In the figure, YOM I is the key for inputting the reading;
CON is the conversion instruction key to convert the input reading,
NXT is a next candidate conversion instruction key for changing a conversion candidate to the next candidate, and SEL is a selection key for determining the current homophone display candidate and at the same time instructing to learn the candidate notation.

The DISK stores documents created in a memory for storing standard documents, and the stored documents can be called up when necessary by instructions from the keyboard.

CR is a cursor register. The CPU can read and write the contents of the cursor register. A CRT controller and controller CRTC, which will be described later, displays a cursor at the position on the display device CRT corresponding to the address stored here.

DBUF is a display buffer memory that stores patterns such as document information stored in TBUF.

CRTC is cursor register CR and buffer DBUF
It plays the role of displaying the contents stored in the CRT on the display device CRT.

Further, a CRT is a display device using a cathode ray tube or the like, and the display of a dot configuration pattern and a cursor on the display device CRT is controlled by a CRT controller. Furthermore, CG is a character generator that stores patterns of characters and symbols to be displayed on the display device CRT.

The character processing device of the present invention, which is composed of each of these components, operates in response to various inputs from the keyboard KB. When input from the keyboard KB is supplied, an interwoven signal is first sent to the microprocessor 1. C
is sent to the PU, and the microprocessor CPU is sent to the ROM
Various control signals stored in the memory are read out, and various controls are performed according to these control signals. FIG. 2 is a diagram showing the structure of the word dictionary DIC used for kana-kanji conversion.

Dictionaries are used to store word information used in orthographic conversion and the first information used in orthographic conversion.
It consists of example information for determining candidates. The word information consists of a word ID specific to the header word, a pronunciation of the word, a notation that stores the notation of the word, a part of speech that stores the part of speech of the word, and a meaning that stores the semantic attribute of the word. The word ID is a consecutive number starting with 1 for the first word. The example information is the number of example information that the heading word has, and the example specific to the example! D. A type that describes the example type, whether the example is an individual example or a semantic example;
If the example type is an individual example, it stores the word ID of the word that is the partner of the example, and if it is a semantic example, it stores the semantic attribute that is the partner of the example, and the example is established among the attached words attached to the partner word of the example. It consists of combination information that stores typical adjuncts that satisfy the conditions for the usage, and reversal information that stores information about whether the example can be used even if it is reversed. For words that do not have any examples, 0 is stored in the number of example information. Like the word ID, the example ID is a consecutive number with the first example being 1.

In the diagram, for example, the word ``r-buy'' is distinguished by a consecutive number of 300, and the reading is ``r-ka-1'' and the notation is ``r-buy''.
, has word information that the part of speech is rrJl) word j.

It has two individual word examples: ``buy alcohol'' and ``buy a fight.'' In addition, the word r salmon 1 is distinguished by a consecutive number of 500, and the reading is ``r salmon'' and the notation is ``salmon j.''
, is a word whose part of speech is r noun 1 and whose semantic attribute is r animal j. Since 0 is stored in the number of examples, no example with r-salmon 1 as a headword is registered.

FIG. 3 is a diagram showing the configuration of the homophone pool DBP0OL and the example information pool YJPOOL. (a) shows the overall configuration of the homophone pool. The homophone pool is composed of each homophone information created by the kana-kanji conversion process. (b) is a diagram showing the structure of homophone information. The homophone information includes the homophone ID, which is a sequential number to distinguish the homophone from other homophones, the data length representing the memory size used for the homophone information, and the number of stored homophone candidates. The number of candidates for storing homophone candidates, candidate information for storing information about homophone candidates, and the priority number for storing the number of priority candidates (described later) among the candidate information stored in the homophones. There is. In such a homophone configuration, the candidate information at the beginning of the candidate information string becomes the first candidate to be output as a conversion result, and the number of candidate information indicated by the priority number from the beginning of the string is as described below. This will be the preferred candidate. (e) is a diagram showing the structure of candidate information. Candidate information includes the notation of the candidate and its word ID.
, a clause-final conjugation that stores the conjugation at the end of a clause, and an example information pointer that stores a pointer to example information to be described later.

If the example is not applied to the candidate information and no example information exists, an invalid value -1 is stored in the example information pointer. (d) shows the overall configuration of the example information pool YJPOOL. The example information pool is composed of example information that stores information on examples applied to each homophone candidate. (e) is a diagram showing the structure of example information. The example information is the bare homophone ID, J, which stores the ID of the homophone that is the counterpart of the example.
An example ID that stores the ID of the used example, an example type that stores the type of applied example (individual example or semantic example), and a pointer to another example information that is commonly used for the same candidate information. It is composed of example information pointers in which . In this way, the example information has a list structure. If the next example information does not exist, an invalid value -1 is stored in the example information pointer.

In the configuration of the homophone pool and example information pool as explained above, when an example is applied to a certain candidate for a certain homophone, the example information is stored in the candidate information, and the example information is stored in the candidate information for the homophone. It is assumed that example information containing information on the same example is also stored as candidate information for the candidate.

FIG. 4 is a diagram showing the structure of the adjunct word string conversion table FZTBL. The adjunct word string conversion table is a correspondence conversion table for making adjunct word strings constituting a clause correspond to combination information in the example information held by the word dictionary DIC. The adjunct word reading represents the pronunciation of the adjunct word string, and the corresponding combination information represents the combination information in the example information of the dictionary that can be replaced by the adjunct word string.

For example, in the adjunct "r koso", the linking information r is 1 or r is 5.
In addition, the adjunct word string ``saemonoga'' can be replaced with the connection information ``1'' to determine the connection.

FIG. 5 is a diagram showing the structure of learning data LRNDAT. (a) shows the overall structure of the learning data. The learning data consists of two parts: word learning data and example learning data. The pointer to word learning stores the index to the word learning data within the learning data, and the pointer to example learning stores the index to the beginning of the example learning data string within the learning data. (b) is a diagram showing the structure of a word learning data string. The word learning data string is composed of the usage size and each word learning data. The word learning data usage size stores the usage size of the word learning data string. The word learning data corresponds to the ID of each word, and 1 is stored for words that have been learned, and 0 is stored for words that have not been learned. (C) is a diagram showing the structure of an example learning data string. The example learning data string is composed of the usage size and each example learning data. The example learning data usage size stores the usage size of the example learning data string. The example learning data corresponds to each example ID, and is set to 1 for prohibited usage examples that are suppressed, and O for unsuppressed usage or brain usage examples.
is stored.

The operation of the above embodiment will be explained according to the flow.

FIG. 6 is a flowchart showing the operation of the character processing device of the present invention.

At step 56-1, a key is pressed on the keyboard and a wait is made for an interrupt to occur. 36-2 when the key is input
56-3.5 depending on the type of key.
Branch to any of steps 6-4.56-5.56-6.56-7.

56-3 is a process when the reading key YOM I is pressed, and the code of the pressed reading is stored in the input reading buffer memory YBUF.

56-4 is the process when the conversion key CON is pressed, $6-3 is input and stored in YBUF.
Converts the character string subject to Kana-Kanji conversion into Kanji and outputs it to the output buffer. When converting to kanji, use the word dictionary D
A first candidate for a homophone is determined using example information existing in the IC.

56-5 is a process when the next candidate conversion key NXT is pressed, and another candidate for the homophone in the output buffer output by 56-4 is displayed.

56-6 is a process performed when the selection key SEL is pressed, in which the homophones in the output buffer displayed on the screen are determined, and the determined character strings are output into the document. moreover,
Performs processing to learn the selected words.

56-7 is a process that is performed when a key other than YOM I, CON, NXT, or SEL (for example, a key used for document editing such as a cursor movement key) is pressed, and is generally performed in character processing devices of the same type. Since this is a well-known process, it will not be described in particular.

56-8 is a display process for displaying the changed portion as a result of the above process. This is a commonly used method in which a single character of data in a document is read, developed into a pattern, and output to a display buffer.

FIG. 7 is a detailed flowchart of the process 56-4.

57-1 is a process of analyzing a character string to be subjected to kana-kanji conversion, which is input in segments, and outputting candidates for output of kana-kanji conversion to a homophone pool. A process that sequentially extracts a character string in divided units, searches a word dictionary for analysis, and outputs only candidates that are recognized as phrases to a homophone pool. Since this is a commonly performed process and is well known, it will not be described in particular.

S7-2 HaS? -1, the analysis result output to the homophone pool is checked to see if there is an example pattern stored in the word dictionary, and if an example pattern exists, it becomes the target of that example. Pick up homophone candidates as priority candidates.

57-3 is the priority candidate picked up by 57-2,
From the candidates that have been learned, select the first one for kana-kanji conversion.
Decide on candidates.

57-4 is the kana-kanji-hen 1l stored in the output buffer
II (This is a process for displaying q output, and is a process commonly performed in character processing devices of the same type, and is well known, so it will not be described in particular.

FIG. 8 is a detailed flowchart of the process 57-2.

Homophones to be extracted as priority candidates are sequentially extracted from the beginning, and the following process is performed on all homophones until there are no more target homophones.

At step 58-1, candidate information in the homophone is extracted.

At 58-2, it is determined whether all candidate information has been processed, and if all candidate information has been processed, priority candidate extraction is ended. If the next candidate information is retrieved, it is determined in 58-3 whether or not the example information exists in the candidate information. Whether or not example information exists can be determined based on the example information pointer in the candidate information. That is, if the value of the example/information pointer is "-1", no example information exists. Otherwise, example information exists in the area indicated by the example information pointer. If the example information does not exist, the priority candidate extraction process ends.

If example information exists, backward example information is extracted at 58-4, and forward example information is extracted at 58-5.

After completing the processing at 58-5, the process loops to 58-1 to process the next candidate information.

FIG. 9 is a detailed flowchart of the process 58-4.

In 59-1, the word ID stored in the candidate information
information on the examples that exist for the word is retrieved one by one from the dictionary.

In 59-2, it is determined in 59-1 whether an example exists in the dictionary for the word of the candidate information and whether all examples in the dictionary have been taken. If the example exists, the process advances to 59-3; if the example does not exist or all the examples in the dictionary have been taken, the backward example extraction process is completed and the process returns.

At step 59-3, it is checked whether the retrieved example can be reversed and applied. That information is stored in a dictionary. If reversal is possible, 59-
Go to 4, and if you can't turn it around, go to 59-1.

In step 59-4, it is determined whether or not the input reading can be established when the retrieved example is reversed and applied. If it can be established, the process proceeds to 59-5, and if it cannot be established, the process proceeds to 59-1.

In step 59-5, information on valid examples is stored in example information. In other words, example information is stored as example information in the example information pool, and if no example information exists in the target homophone candidate information, a pointer to the newly created example information is set in the example information pointer. Store. If example information exists in the candidate information, the newly created example information is added to the end of the example information list. In addition, example information is stored not only for the target homophone, but also for the homophone that is the counterpart of the example.

FIG. 10 is a detailed flowchart of the process 59-4.

5IO-1, it is checked whether there is a clause following the target clause. If the following clause exists, proceed to 5IO-2, otherwise proceed to 5IO-
Proceed to step 7.

5IO-2, it is checked whether the conjugated form at the end of the target clause is an adnominal form. If it is a continuous form, proceed to 5IO-3, and if it is a continuous form, proceed to 5IO-7.

In 5IO-3, candidate information is taken one by one from the homophones for the following clauses.

In S10-4, it is determined whether candidate information has been retrieved in S10-3. Once the candidate information has been collected, proceed to 5IO-5, and after all the candidate information has been collected,
If candidate information is not retrieved, the process advances to 510-7.

In S10-5, it is determined whether the word of the candidate information obtained in 5IO-3 can be used as an example. That is, if the type of example is an individual example, the word ID stored in the example item of the example information in the dictionary and 5IO-3
If the word IDs of the candidate information obtained in (2) match, the words will be used as examples, and if they do not match, they will not be used as examples. If the type of the example is a semantic example, the semantic attribute stored in the example item of the example information in the dictionary and the semantic attribute obtained from the word ID of the candidate information obtained in S10-3 must match. If it does, it will be the target of the example, and if it does not, it will not be the target of the example.

If it is determined that the user will be the person to use, proceed to S10-6,
If it is determined that the user is not the person to be used, the process advances to S10-3.

In SIO-6, a return code indicating that the example is true is set.

At St O-7, a return code indicating that the example does not hold is set.

FIG. 11 is a detailed flowchart of the process 58-5.

In 5ll-1, the word I stored in the candidate information
Using D, information on the examples of the word is retrieved from the dictionary one by one.

In Sit~2, it is determined in 5ll-1 whether an example exists in the dictionary for the word of the candidate information and whether all examples in the dictionary have been taken. If the example exists, the process advances to 5ll-3; if the example does not exist or all the examples in the dictionary have been taken, the forward example extraction process is completed and the process returns.

In step 5ll-3, it is determined whether or not the input reading can be established if the extracted example is passed in the forward direction. If it can be established, proceed to 311-4,
If it cannot be established, proceed to 5ll-1.

In 5ll-4, information on valid examples is stored in example information. In other words, example information is stored as example information in the example information pool, and if no example information exists in the target homophone candidate information, a pointer to the newly created example information is set in the example information pointer. Store. If example information exists in the candidate information, the newly created example information is added to the end of the list of example information. Also,
In the same way, example information is stored not only for candidate information of the target homophone, but also for candidate information of the homophone that is the counterpart of the example.

FIG. 12 is a detailed flowchart of the processing of 5ll-3.

At step 512-1, it is checked whether a clause before the target clause exists. If the previous phrase exists, the process advances to 512-2; if it does not exist, the process advances to 512-8.

At 512-2, candidate information is retrieved one by one from the homophones for the previous phrase.

In 512-3, it is determined whether candidate information has been retrieved in 512-2. If candidate information is obtained, 5
The process advances to S12-4, and if all candidate information has been retrieved, and no candidate information has been retrieved, the process advances to S12-8.

At 512-4, it is determined whether the word of the candidate information obtained at 512-2 can be used as an example. In other words, if the type of the example is an individual example, if the word ID stored in the example item of the example information in the dictionary matches the word 10 of the candidate information obtained in 312-2, then the partner of the example If they do not match, they will not be used in the example. If the example type is a semantic example, the semantic attribute stored in the example item of the example information in the dictionary,
If the semantic attributes obtained from the word ID of the candidate information obtained in step 512-2 match, the word will be used as an example.
- If not, it will not be used as an example. If it is determined that the user will be the partner of the example, the process advances to 512-5, and if it is determined that the user is not the partner of the example, the process advances to 512-2.

In 512-5, the remaining readings (adjunct word strings) other than the independent parts of the candidate information obtained in 512-2 are converted into a combined information axis using an adjunct word string conversion table.

At 512-6, it is determined whether the homophone candidate of the previous clause satisfies the example combination condition. That is, in S12-5, if the adjunct word string conversion table cannot convert the information into join information, it is determined that the join condition is not satisfied. Furthermore, if there is matching information between the combination information obtained in S12-5 and the combination information of the example information in the dictionary, it is assumed that the combination condition is satisfied, and if there is no matching information, the combination condition is satisfied. shall not be satisfied.

If the binding condition is satisfied, the process proceeds to S12-7, and if the binding condition is not satisfied.

If so, proceed to 512-2.

At S10-7, a return code indicating that the example is true is set.

At 512-8, a return code is set indicating that the example does not hold.

FIG. 13 is a detailed flowchart of the process 57-3.

The homophones for which the first candidate determination must be made are sequentially extracted from the beginning, and the following process is performed on all homophones until there are no target homophones left.

In step 513-1, priority candidates are picked up from candidate information within the homophone. To pick up the priority candidates, among the candidate information of the homophones, the number of items whose example information pointer is not "-1" is set as the priority number, and the candidate information counted in the priority number is sequentially rearranged to the top. When the priority candidates have been picked up for all candidate information, the process proceeds to 313-2, where processing is performed to exclude the suppressed usage example' from the priority candidates. By performing the following process on the priority number of candidate information starting from the beginning of the candidate information, suppression examples are preferentially excluded. First, follow the example information pointer in the candidate information and find the example ID in the example information.
, and checks whether the example learning data indicated by the retrieved example ID is in a suppressed state. Until the example information pointer reaches the end, that is, the pointer value is "-"
As a result of checking whether or not the example IDs up to 1" have been suppressed, only if all the example information indicated by the example information pointer of the candidate information has been suppressed, that candidate information is excluded from the priority candidates. That is. , the priority number is 1
The candidate information is sorted immediately after the end of the priority candidate.

In S13-3, as a result of 313-2, it is determined whether or not there are any priority candidates remaining, based on whether or not the number of priority candidates is O. If there is no priority candidate, the process goes to 513-5;
If there are more than one remaining, the process branches to 513-4.

Among the candidate information left as priority candidates in 313-4,
Check if there are any candidates with individual word examples. The existence check for individual word examples is performed by performing the following process on the priority number of candidate information starting from the beginning of the candidate information. First, the example information pointer in the cash supplement information is followed, and the type of example is checked for all the example information indicated by the example information pointer. If there is even one example of an individual word, it is determined that there is an example of an individual word. The types of examples are checked until the example information pointer reaches the end, that is, until the pointer value indicates "-1", and if all examples are semantic attribute examples, it is determined that there is no individual word example.

As a result of the determination, if there is an example of an individual word, the process branches to 513-6, and if there is no example of an individual word, the process branches to S13-7.

As a result of searching for individual word examples in 513-6 and 513-5, candidate information that is all semantic attribute examples is excluded from the light-receiving candidates. In other words, the number of priority items is reduced by 1, and the candidate information is rearranged immediately after the end of the priority candidates. .

As a result of the above processing, it is determined in step 513-7 whether or not the priority candidates have been narrowed down to one. That is, it is determined whether the priority number is 1 or not. If the priority number is 1, then 313-
Proceed to step 9 and determine the priority candidate as the first candidate. If the priority number is not 1, it is not narrowed down to one yet,
At 513-8, candidates are narrowed down by word learning.

313-8 extracts the word ID of the priority candidate and uses the word ID
The word learning data indicated by is examined, and if a word-learning candidate exists, the word-learning candidate is determined as the first candidate in 513-11. If there are a plurality of word-learning candidates, the previously stored candidate information is set as the first candidate. If none of the candidates among the priority candidates have been learned words, the candidate stored at the beginning of the priority candidates is determined as the first candidate in 513-10.

If it is determined in S13-3 that there are no priority candidates, then in S13-5 it is determined whether the first candidate is determined by word learning. The word ID of the candidate information is extracted, the word learning data indicated by the word ID is checked, and if a word-learning candidate exists, the word-learning candidate is determined as the first candidate in 313-11. If there are a plurality of word-learning candidates, the previously stored candidate information is set as the first candidate. If no word has been learned among all the candidate information, the first candidate/information is determined as the first candidate in 313-12.

FIG. 14 is a detailed flowchart of the process 56-6.

In step 514-1, the example information pointer is referred to for all candidate information in the homophone to determine whether an example exists in the homophone in which the candidate whose selection/confirmation is instructed by the selection key SEL is stored. Judgment is made by That is, if the value of the example information pointer is "-1", it is determined that there is no example. If there is no example for all candidate information, the process branches to 514-3, and if there is at least one example, the process branches to 514-2.

In step 514-2, it is determined as follows whether or not an example established by the selected word exists in the example information.

First, the example information pointer in the candidate information of the selected word is followed, and the bare homophone ID and example ID are extracted. Follow the example information pointer of the head candidate information in the homophone indicated by the extracted bare homophone ID, and find the bare homophone ID.
is a homophone ID of the selected word. If such example information is not retrieved, it is determined that the example does not hold true. When example information whose bare homophone ID matches the homophone ID of the selected word is retrieved, the example ID is retrieved and checked to see if it matches the example ID in the example information of the selected word. . If the example IDs match, it is determined that a valid example exists. If they do not match, it is determined that the example does not hold true. If it is determined that the example holds true, S
Proceeding to step 14-6, if it is determined that the example does not hold, a similar determination is made for the next usage/example information of the bare homophone. If all the examples of the bare homophone example information hold true or not, the same determination process is performed for the next example information of the selected word. If there is no usage example information for the selected word, it is determined that there is no valid usage example, and 514-
Branch to 5.

S14-4 is a process for canceling usage suppression. 514-2
The example learning data indicated by the example ID of the example determined to be a valid example is made available for use.

In step S14-3, if there is no example for the word selected in step 514-1, a determination is made as to the existence of an example in order to suppress examples of other homophones that were not selected.

To do this, it is determined whether or not example information exists in all example information other than the selected candidate information among homophones in which the selected word exists, and if examples exist,
In S14-5, the example learning data indicated by the example ID is disabled.

514-6 performs word learning processing. The word learning data indicated by the word ID of the selected notation is made into a learning letter, and the learning state of the word learning data indicated by the unselected candidate information in the homophone is canceled.

At step 314-7, the selected notation is confirmed in the document. The confirmation process transfers the confirmed character code to the document buffer TBUF.
This is a process that is normally performed in character processing devices of the same type and is well known, so it will not be described in detail.

(Other Examples) The dictionary used in the above explanation has a single dictionary structure in which word information and usage example information are described under the same headword, but each dictionary table has separate word information and usage example information. It can be processed in the same way as . Furthermore, dictionaries are configured for each type of usage, and each dictionary has its own ID.
Processing may be performed by giving priority to the examples based on the dictionary ID in which the examples exist.

Furthermore, in the above description, examples are applied only between adjacent bunsetsu, but examples can also be easily applied between non-adjacent bunsetsu. In this case, by performing a syntactic analysis process between clauses (an analysis process for dependencies), incorrect application of the example can be avoided.

In the above explanation, we have described the case where usage examples are applied between clauses input in separate writing, but processing for automatically dividing into clauses can also be added to reading sequences input in solid writing. By doing so, you can apply the example in the same way. It is also possible to divide sentences into clauses while taking into account the existence of usage examples.

(Explanation of Effects) As is clear from the above explanation, according to the present invention, ambiguity determination information for both @ word usage examples and semantic attribute usage examples can be used efficiently even for polymorphic homophones. Since it is possible to determine the first homophone candidate, there is no need to convert to the next complement, and efficient input composing can be performed.

[Brief explanation of the drawing]

FIG. 1 is a block diagram of the overall configuration of the present invention, FIG. 2 is a diagram showing an example of the dictionary configuration of the present invention, FIG. 3 is a diagram showing the configuration of the homophone pool and the example information pool of the present invention, and FIG. FIG. 5 is a diagram showing the structure of the adjunct word conversion table of the present invention, FIG. 5 is a diagram showing the structure of learning data of the present invention, and FIGS. 6 to 14 are flowcharts showing the operation of character processing installation of the present invention. . District 7 Figure 10 Figure 11 Figure 12 Figure 14 Figure 0, -□□------

Claims (1)

[Scope of Claims] An input means for inputting the pronunciation of a word, a word dictionary that stores the semantic attributes of the word while making correspondence between the pronunciation and the notation of the word, and a pair of words having a co-occurrence relationship. an example dictionary storing individual word usage examples that are, and semantic attribute usage examples that are pairs of words and semantic attributes; and a phrase whose reading inputted by the input means is recognized as a phrase by searching the word dictionary. a recognition means; an example retrieval means for searching the example dictionary for pairs of phrases recognized by the phrase recognition means and extracting valid individual word examples or semantic attribute examples; If the individual word examples and the semantic attribute examples are retrieved at the same time, the conversion means determines the notation of the first candidate by giving priority to the word pairs constituting the individual word examples over the word pairs constituting the semantic attribute examples. A character processing device characterized by: