JPH0638260B2 - Character processing apparatus and method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Industrial application] The present invention relates to a character processing device for inputting a kanji / kana mixed sentence by kana / kanji conversion.

[Prior Art] Currently, a character processing device such as a Japanese word processor generally inputs kanji-kana mixed sentences using kana-kanji conversion.

Kana-Kanji conversion is to convert an input reading string into Kanji by referring to a dictionary. Part-of-speech information such as nouns, sahen nouns, adverbs, adjectives, and adjective verbs is described for each word in the dictionary, and Kana-Kanji conversion analyzes possible reading strings to create possible phrase candidates and combines them. The conversion candidates are determined and presented in order of likelihood. The operator selects a desired candidate from the presented conversion candidates.

For example, "Wood" for the reading line "Take yesterday"
"Tree""Yesterday""Function""Yesterday""Function""Brain"
Phrase candidates such as "brain", "other", "dense", "higher", "higher", "turtle", "bite" are created, and "higher yesterday" and "higher function" are combined in this order. Is displayed as.

The notation "Yesterday" looks unnatural, but in the dictionary, "Yesterday" is just a noun and is not wrong as a morpheme. In addition, "raise yesterday" is not wrong as a syntax. In general, “yesterday” is more frequently than “function”, so “increasing yesterday” is more likely to be converted as the first candidate.

As it is, the desired candidate is not converted as the first candidate, so a method called example conversion has been proposed in order to increase the conversion rate. The example conversion is a method in which a pair of co-occurring words (examples) is registered in the example dictionary in advance, the example dictionary is referred to during conversion, and the first candidate is changed according to the example in the example dictionary. For example, the pattern of "enhancing function / enhancement" is stored in the example dictionary, and "enhance function" is converted as the first candidate for the input of "taming yesterday".

[Problems to be Solved by the Invention] However, in the example conversion method, in order to correctly convert "function to", perfect conversion cannot be achieved unless the words that co-occur with "function" are covered. For example, "improve function"
"Add function", "delete function", "reduce function", etc. must be registered in the example dictionary. For this reason, a huge memory was required to store the example dictionary. In addition, it has been virtually impossible to cover such examples due to human labor.

Also, when "Kinoyu" was entered in a single phrase, the surrounding situation could not be seen, so the example conversion could not be applied, and an appropriate conversion could not be performed.

[Means (and Action) for Solving Problems] A character processing device according to the present invention includes an input means for inputting a kana character string, a word reading, a notation, a word likelihood, and a specification subsequent to the word. Dictionary means stored in association with the correction value of the word likelihood according to the particle, and conversion for converting the kana character string input by the input means into a notation candidate in phrase units with reference to the dictionary means Means and the independent word of the notation candidate of each clause converted by the conversion means is followed by a specific particle whose word likelihood correction value is stored in the dictionary means in association with the reading of the independent word. If there is, the correction means for correcting the word likelihood of the independent word by the correction value, and the priority of the notation candidate of each phrase is the word likelihood of the independent word in the notation candidate corrected by the correction means. And a determining means for determining based on the degree Display control means for displaying the notation candidates of each clause according to the determined priority.

Further, the character processing method of the present invention refers to a dictionary that stores a word reading in association with a notation, a word likelihood, and a correction value of the word likelihood according to a specific particle that follows the word. A conversion step of converting the kana character string input from the input section into notation candidates in phrase units, and associating the independent words of the notation candidates of each phrase converted by the conversion step with the reading of the independent word When a specific particle stored in the dictionary means is followed by a correction value of the word likelihood, a correction step of correcting the word likelihood of the independent word by the correction value, and a notation candidate for each clause Determining the priority of the phrase based on the word likelihood of the independent word in the notation candidate corrected by the correction step, and the notation candidates of each clause according to the priority determined by the determining step. And a display step for displaying on the display unit.

Thus, according to each invention of the present application, the notation of the bunsetsu is converted into a more appropriate notation according to the independent word in the bunsetsu and the particle following it.

EXAMPLES The present invention will be described in detail below 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, performs arithmetic operations for character processing, logical judgments, etc., and has an address bus AB, a control bus CB, and a data bus D.
Via B, each component connected to those buses is controlled.

The address bus AB transfers an address signal indicating a component to be controlled by the microprocessor CPU. Control bus CB is a microprocessor CPU
The control signals of the respective components to be controlled by are transferred and applied. The data bus DB transfers data between the constituent devices.

Next, the ROM is a fixed read-only memory.
Microprocessor CP described later with reference to FIGS. 2 to 15
The control procedure by U is stored.

The RAM is a writable random access memory having a structure of 1 word 16 bits, and is used for temporary storage of various data from each constituent element. IBUF is an input buffer that stores key data input by a key input.
UF is an output buffer for temporarily storing the result of Kana-Kanji conversion. BCTBL is a phrase candidate table that stores phrase candidates that are in the process of conversion. DIC is a dictionary for Kana-Kanji conversion. FTBL is an adjunct word table in which adjuncts are stored in association with adjunct likelihoods and serial numbers. CTBL is an adjunct word connection table that describes connection relationships between adjunct words.

KB is a keyboard, which is a character / symbol input key such as an alphabet key, a hiragana key, or a katakana key, and
It is provided with various function keys such as a conversion key, a next candidate key, a candidate display key, and a confirm key for instructing various functions for the character processing apparatus.

DISK is an external memory for storing the document data, stores the document created in the text buffer TBUF, and the stored document is called when necessary by the instruction of the keyboard.

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

DBUF is a display buffer memory that stores data to be displayed.

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

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

The character processing device of the present invention comprising the above-described components operates in response to various inputs from the keyboard KB. When an input from the keyboard KB is supplied, first, an interrupt signal is sent to the microprocessor CP.
The control signal is sent to U, the microprocessor CPU reads out various control signals stored in the ROM, and various controls are performed in accordance with these control signals.

FIG. 2 is a diagram showing a conversion example by the device of the present invention. In the figure, "" means that the conversion key is pressed there.

When "Saikin" is entered, "Recent" is converted as the first candidate and "Bacteria" is converted as the second candidate, but when "Sainin" is entered, "Bacteria" is the first candidate. Becomes Since "recently" is not converted, there is no second candidate or lower.

If you enter "yes", "yes yesterday" will be converted as the first candidate and "function" will be converted as the second candidate. If "yes" is entered, "function" will be the first candidate. .

If you enter "shuyou", "main" will be converted as the first candidate and "tumor" will be converted as the second candidate, but if you enter "shuyou", "tumor" will be the first candidate. . Since “main” is not converted, there is no second or lower candidate.

If you enter "Kikan", "Term" is converted as the first candidate and "Institution" is converted as the second candidate, but if you enter "Kikan Sankyo", "Kikan" is converted to "Industry". "
Is used as a prefix, and the first candidate becomes the “key”.

If you enter "shichoo", "mayor" will be converted as the first candidate and "city hall" as the second candidate.
If you enter "Shichode", "At City Hall" will be the first candidate. "By mayor" is the second candidate.

FIG. 3 shows an input buffer IBUF and an output buffer OBUF.
It is a figure showing the composition of.

Both IBUF and OBUF have the same configuration. The first 2 bytes are the size information of each buffer, and the numerical value obtained by doubling the value obtained by subtracting 1 from the number of characters stored in the buffer is entered. The "" at the end of the input buffer means that the conversion key was pressed there. Each character consists of 2 bytes per character and is stored in JIS X 0208 code or the like.

FIG. 4 is a diagram showing the structure of the dictionary DIC. "reading"
It is composed of "notation", "part of speech", "word likelihood", and "particle connection likelihood".

"Reading" means reading a word, "writing" means writing a word,
The "part of speech" stores the part of speech of the word.

In the "word likelihood", information indicating the likelihood of the word itself such as frequency information is stored as a value of 0-5. A likelihood value of 0 means that it is most likely, and is interpreted as suspicious as the value increases. Likelihood value 6 means that it cannot be considered at all, and therefore does not exist as a word likelihood value. The particle connection likelihood is stored as described in detail in FIG. 5, but when the case particle of the word follows, information indicating how to correct the word likelihood is stored.

In the figure, it means that the word likelihood should be −5 when used as a prefix for “basic”, and the corrected word likelihood of “basic” is 0 when used as a prefix. Becomes That is, it seems more plausible.

For "yesterday", it means that the word likelihood should be +4 when the particle "wo" is connected, and the corrected word likelihood of "yesterday is" is 5. That is, it is more suspicious.

For "recent", it means that the word likelihood should be +5 when the particle "ga" is connected, and the corrected word likelihood of "recent" is 6 and such notation is no longer possible. There is no.

With respect to "city hall", it means that the word likelihood should be -3 when the particle "de" is connected, and the corrected word likelihood of "at the city hall" is 0. That is, it seems more plausible.

FIG. 5 is a diagram for explaining the structure of the particle connection likelihood of FIG. 4 in more detail.

The particle connection likelihood is divided in units of 4 bits from the beginning, and each 4 bits stores a correction value of the word likelihood corresponding to a specific particle. For example, the connection likelihood corresponding to “ga” stores a value to be corrected to the word likelihood when the particle “ga” follows the word.

The correction value is stored in the range of -5 to 5. Other values (-8, -7, 7) are invalid values. In particular, the value 0 is stored when the word likelihood should not be corrected.

The likelihood as a prefix component of a compound word and the likelihood as a suffix component of a compound word each store a value to be corrected to the word likelihood when the word is used as a prefix or suffix. For example, “core” when used as “core industry” is used as a prefix, and “period” when used as “sales period” is used as a suffix. In such a case, it is described if it is necessary to correct the word likelihood.

FIG. 6 is a diagram showing the concept of the phrase candidate table BCTBL. The phrase candidate table is a binary phrase representing possible phrase candidates as a result of analysis of input reading. In the figure, a horizontal line means a child pointer, and a vertical line means a younger brother pointer. The younger brother pointer links other bunsetsu candidates (usually shorter ones) starting from a certain reading position, and the child pointer links bunsetsu candidates that follow the bunsetsu. In addition, the words enclosed in parentheses mean homophone relations.

For example, the phrase “(recently, in bacteria)” means a homophonic relationship, which means that there are two possible interpretations, “recently” and “in bacteria”.

There are interpretations such as “(recently, bacteria) in”, “(recently, bacteria) in”, “(recently, bacteria)”, etc. at the beginning of the input reading string “Saiki de ha sainga ...” Linked with a pointer (vertical line).

The phrase following "(Recently Bacteria)" is thought to be "Bacteria" and is linked by a child pointer. "Bacteria"
"Not found" is considered as the clause following "," which is also linked by a child pointer.

When such a phrase candidate table is completed, it is easy to create a phrase candidate string such as "Recently / Bacteria / Not found""Recently / Bacteria / Picture / From honey / No".

FIG. 7 is a diagram showing a specific configuration of the phrase candidate table BCTBL.

The "independent word" is composed of 2 bytes each, and links the independent word (existing in the dictionary) of the bunsetsu candidate.

The "adjunct word string" is composed of 2 bytes, and is an area for specifying the adjunct word string following the independent word of the bunsetsu candidate. The first 1 byte indexes the first character of the auxiliary word string on the input buffer, and the next 1 byte indexes the last character of the auxiliary word string on the input buffer. For example, in the case of the input buffer shown in FIG.
Set 10 in the second byte.

The “phrase likelihood” stores a value indicating the likelihood of the phrase candidate calculated as shown in FIG.

"Homophone link" links bunsetsu candidates in which the independent word of the bunsetsu candidate is changed to another homophone.

The younger brother link links another phrase candidate starting from the same reading position as the phrase candidate.

The child link links a bunsetsu candidate following the bunsetsu candidate.

For example, the homophone link of the phrase candidate 0 is 1, and the phrase candidate 1 (for bacteria) is linked. Also, phrase candidate 1
It can be seen that the homophone link of is 0 and there are no more homophone links.

The younger brother link of the phrase candidate 0 is 50, and the phrase candidate 50 (recently) is linked. The child link is 100, and the phrase candidate 100 (recently) is linked. Furthermore, the phrase candidate 10
The child link of 0 is 200, and the phrase candidate 200 (not found) is linked. It can be seen that the child link is 0, where the input reading ends.

FIG. 8 is a diagram showing the structure of the attached word table FTBL.

"Yomi" stores the reading of each attached word. The “adjunct word likelihood” stores an adjunct word likelihood indicating the likelihood of each adjunct word. A value of 0 to 3 is stored in the attached word likelihood, and 0 means the most likely, and the larger the value, the more suspicious an attached word. Each attached word is managed by a serial number (how many attached words from the beginning).

For example, the attached word of the serial number 10 is “ga”, and the attached word likelihood thereof is 0. The attached word of the serial number 50 is "ja",
The attached word likelihood is 3.

FIG. 9 is a diagram showing the structure of the attached word connection table CTBL.

The adjunct word connection table is a table describing the connection relation between adjunct words. The preceding adjuncts are shown in the left part of the table, and the following adjuncts are shown in the upper part. Whether or not the adjunct connection is allowed is shown at the intersection of the adjunct shown on the left and the adjunct shown at the top.

The value of the intersection is -1, 0, 1, 2, or 3, and -1 means that they cannot be connected. 0 to 3 means that connection is possible, and the value indicates the likelihood between attached words (likelihood of connection). 0 is a plausible connection, and the larger the value, the more suspicious the connection.

For example, the intersection of the left part 50 and the upper part 130 may be read to determine whether or not the adjunct word connection “ja / n” is recognized. Since a value of 0 is obtained, it can be seen that connection can be performed with an inter-word likelihood of 0.

Further, when the intersection of the left part 50 and the upper part 180 is read, it is -1, so it can be seen that the connection "ja / ha" is not recognized.

In addition, when the intersection of the left part 150 and the upper part 180 is read, it is 3. Therefore, it can be seen that the connection “no / ha” is recognized with the inter-adjunct word likelihood 3.

FIG. 10 is a diagram showing a calculation example of the phrase likelihood. The phrase likelihood expresses the likelihood of each phrase candidate, and the smaller the value, the more likely it is. Phrase likelihood is calculated by adding α times the independent word likelihood and β times the adjunct likelihood. Here, the independent word likelihood is obtained by adding the particle likelihood to the word likelihood according to the analysis of the adjunct word string of the bunsetsu (what particle is following). The adjunct likelihood is obtained by adding the sum of the likelihoods of individual adjuncts of the adjuncts of the adjunct word following the independent word of the bunsetsu and the sum of the likelihoods between the adjuncts. The smaller the clause likelihood, the more likely it is. In this example, the values of α and β are α = 2 and β =
It is 5.

According to FIG. 10, the clause likelihood of "function" is 4,
The phrase likelihood of "Yesterday" is (the connection likelihood of "Function / Wo" is +
Since it is 10 (since it is 4), it can be seen that “function” is converted to the first place. Also, the phrase likelihood of "yesterday's ha" is 17 since the inter-subject likelihood of "no / ha" is 3, and it can be seen that it is more difficult to exchange than the ordinary phrase.

FIG. 11 is a diagram showing an example of sentence likelihood calculation. The sentence likelihood expresses the likelihood of a sentence that is a phrase candidate sequence, and the smaller the value, the more likely it is. The sentence likelihood is calculated by adding (the number of phrases-1) × inter-phrase likelihood to the sum of the phrase likelihoods. The inter-section likelihood is 20 here.

According to FIG. 11, the sentence likelihood of “recently / bacteria / not found” is 50, and the sentence likelihood of “recently / bacteria / from honey / absent” is 80. It can be seen that "/ bacteria / not found" is converted to the first place.

Also, the sentence likelihood of "yesterday / enhancement" is 32, and the sentence likelihood of "function / enhancement" is 26.
It can be seen that “enhance” is converted to the first place.

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

FIG. 12 is a flow chart of a part for receiving a key input and performing a process.

Step 12-1 is a process for fetching data from the keyboard. The type of the key fetched in step 12-2 is determined, and the process branches to the processing routine for each key.

If it is a conversion key, the process branches to step 12-3, and the conversion process of kana-kanji conversion is performed in step 12-3 as described in detail in FIG. If the key is any other key, the process branches to step 12-4 to perform other processes such as insertion and deletion which are carried out in a normal character processing device.

In step 12-5, the conversion result or the processing result is displayed. After that, the process branches to step 12-1.

FIG. 13 is a detailed flowchart of the “conversion process” in step 12-3.

In step 13-1, dictionary search, morphological analysis,
Parse the input reading string by performing syntactic analysis, etc., and create phrase candidates. In step 13-2, the likelihood of each phrase candidate is calculated, which phrase is most likely to be converted is determined, and the phrase is determined as the first candidate. Step 13-3
In, a conversion result is created based on the determined first candidate and is output.

FIG. 14 is a detailed flowchart of the “conversion candidate creation” in step 13-1.

In step 14-1, the input buffer index i and the phrase candidate table index j are initialized to 0.

In step 14-2, the dictionary is searched for word candidates based on the reading in the input buffer indicated by i.

In step 14-3, the attached word string connected to the found word candidate is analyzed. As a result, bunsetsu candidates are obtained.

The phrase candidates obtained in step 14-4 are stored in the phrase candidate table. When storing, it is stored in the (j + 1) th entry. Also, set the necessary information. For example, the phrase likelihood of this phrase candidate is calculated and stored as shown in FIG. Alternatively, a child link or a younger brother link is set for a phrase candidate whose child or brother is this phrase candidate. After storing, the value of j is incremented.

In step 14-5, a bunsetsu candidate that has not been terminated, that is, a bunsetsu candidate for which a child link has not yet been determined is found from the bunsetsu candidate table, and the next reading position is set to i.
To.

In step 14-6, it is determined whether or not the child links of all the bunsetsu candidates have been decided, and if there is any that has not been decided, the process branches to step 14-2. Otherwise it returns.

FIG. 15 is a detailed flowchart of the “first candidate determination” in step 13-2.

In step 15-1, the maximum likelihood sentence likelihood is initialized to the maximum value allowed in processing.

In step 15-2, one phrase candidate string is extracted from the phrase candidate table.

In step 15-2, the sentence likelihood of the extracted phrase candidate sequence is calculated as shown in FIG.

In step 15-4, it is determined whether the calculated sentence likelihood is more likely than the maximum likelihood sentence likelihood, specifically, smaller.
When it is smaller, the maximum likelihood sentence likelihood is updated to the calculated sentence likelihood in step 15-5.

In step 15-6, it is determined whether another phrase candidate string can be extracted from the phrase candidate table, and if it can be extracted, the process branches to step 15-2. If not, return.

As a result, the phrase candidate sequence from which the maximum likelihood sentence likelihood is calculated is determined as the first candidate.

[Other Embodiments] In the above description, a field is previously defined for each case particle as a storage format of particle connection information so as to have likelihood information. However, in this method, 0 is stored in most cases. Therefore, the connection likelihood may be stored together with the necessary particles.

Further, since the likelihood calculation is merely an example, another calculation method does not particularly impair the gist of the present invention.

EFFECTS OF THE INVENTION As is clear from the above description, according to the present invention, the word likelihood and the correction value of the word likelihood according to a specific particle following the word are stored for each word. In addition, by correcting the word likelihood of the independent word in the phrase with the correction value according to the subsequent particle, and determining the priority of the phrase based on the corrected word likelihood, the particle connection Since the conversion of unnatural bunsetsu is suppressed compared to normal bunsetsu, an accurate bunsetsu is converted to a higher rank, the operator's burden in selecting a candidate can be reduced, and a character processing device with high operability can be realized.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram of the overall configuration of the present invention, FIG. 2 is a diagram showing an example of kana-kanji conversion output in the present invention, and FIG. 3 is an input buffer and output in the present invention. FIG. 4 is a diagram showing a structure of a buffer, FIG. 4 is a diagram showing a structure of a kana-kanji conversion dictionary according to the present invention, FIG. 5 is a diagram showing a detailed structure of particle connection likelihood, and FIG. 6 is according to the present invention. FIG. 7 is a diagram conceptually showing stored contents of a phrase candidate table, FIG. 7 is a diagram showing a configuration of a phrase candidate table in the present invention, FIG. 8 is a diagram showing a configuration of an adjunct word table in the present invention, and FIG. FIG. 10 is a diagram showing the structure of an adjunct word connection table in the present invention, FIG. 10 is a diagram showing an example of calculation of phrase likelihood in the present invention, and FIG. 11 is a diagram showing an example of calculation of sentence likelihood in the present invention. FIGS. 12 to 15 show the character processing apparatus of the present invention. Is a flow chart showing the work. DISK ... External memory CPU ... Microprocessor ROM ... Read-only memory RAM ... Random access memory IBUF ... Input buffer OBUF ... Output buffer BCTBL ... Lecture candidate table DIC ... Kana-Kanji conversion dictionary FTBL ... Attached Word table CTBL …… Adjective word connection table

Claims (2)

[Claims] 1. An input unit for inputting a kana character string, and a word reading is stored in association with a notation, a word likelihood, and a correction value of the word likelihood according to a specific particle following the word. Dictionary means, a conversion means for converting the kana character string input by the input means to a notation candidate in phrase units with reference to the dictionary means, and an independence of the notation candidates of each clause converted by the conversion means When a word is followed by a specific particle stored in the dictionary means with a correction value of the word likelihood associated with the reading of the independent word, the word likelihood of the independent word is determined by the correction value. A correction means for correcting, a determination means for determining the priority of the notation candidates of each bunsetsu based on the word likelihood of the independent word in the notation candidates corrected by the correction means, and the determination means. Display notation candidates for each phrase according to priority Character processing apparatus characterized by having a that display control means. 2. A character processing device comprising an input unit for inputting a kana character string and a display unit for displaying characters, wherein the reading of a word is represented by notation, word likelihood and a specific particle following the word. A conversion step of converting the kana character string input from the input unit into a notation candidate in phrase units by referring to a dictionary stored in association with the correction value of the word likelihood according to In the case where the independent word of the notation candidate of each phrase is followed by a specific particle stored in the dictionary means with a correction value of the word likelihood associated with the reading of the independent word, the independent word Correction step for correcting the word likelihood of the phrase with the correction value, and a determination step for determining the priority of the notation candidates of each clause based on the word likelihood of the independent word in the notation candidate corrected by the correction step. According to the priority determined by the determination step And a display step of displaying the notation candidates of each clause on the display unit.