JPH01229367A - character processing device - Google Patents

Abstract

PURPOSE:To realize the correct application of an example, and to improve the efficiency of inputting work by storing a coupling condition necessitated for a word and a word to cooriginate in a meaning in an example dictionary, and applying the example only when the coupling condition is satisfied. CONSTITUTION:A word dictionary DIC to be used for KANA (Japanese syllabary)- KANJI (Chinese character) conversion is provided in a RAM, and is constituted of word information to be used for description conversion and example information (example dictionary) for determining a first candidate at the time of the description conversion. This example information is provided with coupling information in which a representative adjunctive word to satisfy a condition to realize the example among the adjunctive words to be attached to the word of the other party of the example is stored. Then, an adjunctive word train to constitute a clause is made to be corresponding coupling information corresponding to the coupling information through an adjunctive word train conversion table FZTBL. Namely, even for a homonym with multiplicity of meaning, the example can be applied correctly by deciding the coupling condition when the example is applied, and the first candidate of the homonym can be correctly determined. Thus, conversion into a next candidate comes unnecessary, and the efficient inputting work can be realized.

Description

DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention relates to a homophone polysemy F-1 definition method in a character processing device that converts character strings.

(Prior Art) Conventionally, as a character processing device for converting character strings, for example, a kana-to-ji conversion device in a Japanese word processor has been widely used. As a result of Kana Asagaku conversion, the many homophone candidates that appear have been deambiguated based on the grammatical characteristics of the parts of speech of individual words. Since no consideration was given to determining word pairs, there were many cases of incorrect conversion. Therefore, we stored word-word pairs, that is, usage examples!
# Combinations in word pair dictionaries have been used to determine first candidates. In addition, not only individual word pairs but also words can be given a semantic classification code that is categorized semantically.
Co-occurrence information between individual i-words and meaning coats has also been used to determine the first candidate.

(The invention or the problem the problem is being solved) However,
Conventional techniques do not take into account the bonding conditions necessary for words to co-occur semantically (mainly, the dependency between words is the condition of the particle that governs the relationship).
There was a problem of applying the wrong example.

For example, if there is an example of ``cooking - warm,'' and the input pronunciation is ``I put the rice bowl in the warm room.'', the sentence ``I put the rice bowl in the warm room.'' has no semantic co-occurrence relationship. The example is applied to the part, resulting in "the food is warm." The correct sentence should be "I brought the food to a warm room."

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

In other words, the combination conditions necessary for the semantic co-occurrence of Jli words and words are stored in the usage example dictionary, and when using the example, the example is applied only when the combination condition is satisfied. The object of the present invention is to provide a character processing device that does not apply the example when the combination condition is not satisfied.

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 word pronunciations and spellings in correspondence, a usage dictionary that stores co-occurrence pairs of words and combination conditions for establishing the co-occurrence relationship, and clause recognition means that analyzes the pronunciations into clauses; The present invention includes example retrieval means for searching for an example that holds true in the analyzed phrase pair, and combination condition determining means for determining a combination condition as a means for determining whether the example holds true. In the previous example, this would result in "
If there is an example such as ``The food is warm,'' and ``The food is warm,'' the connection condition r does not satisfy 1, so the example is not applied to that part.

In addition, when there are examples such as "F won a fight" and "R bought a fight", the input "started a fight" will be changed to "won the fight" and the input "started a fight". In contrast, you can use the correct example to differentiate between the words, ``I bought a fight.''

〔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 D B POOL.

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

FZTBL is an adjunct word string conversion table for making an adjunct 1 word correspond to combination information stored in the DIC.

KB is a keyboard with character symbols such as alphabet keys, hiragana keys, katakana keys, etc.
It is equipped with various function keys such as a conversion key for instructing conversion.

In the figure, YOMI is the key for inputting the reading, C
ON is the conversion instruction key to convert the input reading, N
XT 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 CRTC, which will be described later, displays a cursor at a position on the display device CRT corresponding to the address stored here.

D B'U F 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 an anode ray tube or the like, and the display of a dot pattern and a cursor on the display ff1cRT 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 comprising these components operates in response to various inputs from the keyboard KB, and when an input from the keyboard KB is supplied, an interwoven signal is first output. Microprocessor C
PU and its microprocessor CPU h)
Various control signals stored in the ROM are read out, and various controls are performed according to these control signals.

FIG. 2 is a diagram showing the configuration of the word world @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 a type that describes the number of examples/information that the heading word has, the example ID unique to the example, the example type of whether the example is an individual example or a semantic example, and the example type if the example type is an individual example. If it is a semantic example, it stores the semantic attribute of the other word of the example, and representative adjunct words attached to the other word of the example that satisfy the conditions for the example to hold. It consists of combination information that stores the example, and reversal that stores information about whether it can be used even if the example is reversed. For words that do not have any examples, O is stored in the number of example 1 blue papers. Like the word ID, the example ID is a consecutive number with the first example being 1.

In the figure, the example is ``The word ``Kurai J'' is distinguished by consecutive numbers 300, and is pronounced ``R KAJ'' and written ``R KARA''.
, has the word information that the part of speech is "verb". There are two individual word examples: ``J to buy T sake'' and ``T to buy quarrel.'' Further, the word r-salmon J is distinguished by a serial number of 500, has the pronunciation "sake J, written as r-salmon", the part of speech is r-noun j, and has the semantic attribute "animal". Since O 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 from the beginning of the string (the number of candidate information indicated by the priority number) is (C) is a diagram showing the structure of the candidate information.The candidate information includes the notation of the candidate, its word ID, the clause-final conjugation that stores the clause-final conjugation, and It consists of an example information pointer that stores a pointer to example information, which will be described later.

If the example is not applied to the candidate information and the example information does not exist, 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 includes a pair homophone ID that stores the ID of the homophone that is the partner of the example;
An example ID that stores the ID of the applied 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 applied to the same candidate information. It consists of example information pointers that are in the process of being stored. 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 the adjunct word string constituting a clause correspond to the combination information in the example information having the idiom 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 replace the adjunct word string.

For example, the adjunct ``koso'' has binding information r that is 1 or r.''
In addition, the adjunct word string r Zaemo ga J can be replaced with the combination information r ``, respectively, 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 words: one@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) (This is a diagram showing the structure of the 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 are being learned and 0 is stored for words that have not been learned. (C) shows the structure of the example learning data string. It is a diagram showing the 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. 1 is stored for suppressed prohibited usage examples, and 0 is stored for unsuppressed usage or brain usage examples.

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 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 reader key YOMI is pressed, and the code of the pressed reading is stored in the input reading buffer memory YBUF.

36-4 is the process when the conversion key CON is pressed, which is input in 56-3 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 when the selection key SEL is pressed, which is displayed on the screen, determines the homophone in the output buffer at tl, and outputs the determined character string into the document. Furthermore, processing for learning the selected words is performed.

56-7 is a process when a key other than YOMI, CON, NXT, or SEL is pressed (for example, a key used for document editing such as a cursor movement key), and is This is a process that is currently being carried out and is well known, so 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 process 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.

57-2 checks whether there is an example pattern stored in the word dictionary based on the analysis result output to the homophone pool in 57-1, and if the example pattern exists, it is Homophone candidates to be used as examples are picked up as priority candidates.

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

57-4 is a process for displaying the output of the kana-kanji conversion stored in the output buffer, 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 flowchart 1 which details the process of 57-2.
It is.

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.

5a-t extracts candidate information in homophones.
.

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 by 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.

When the processing in 58-5 is completed, the processing in 58-1 is performed 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 step 59-2, it is determined in step 39-1 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.

At 510-1, 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 7.

In S10-2, it is checked whether the conjugated form at the end of the target clause is an adnominal form. If it is an adjoint form, the process proceeds to S10-3; if it is not an adjoint form, the process proceeds to SIO-7.

At 310-3, candidate information is retrieved one by one from the homophones for the following clauses.

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

In S10-5, it is determined whether the word of the candidate information obtained in S10-3 can be used as an example. In other words, if the type of example is an individual example, the word ID and S I stored in the example item of the example information in the dictionary are
If the Togo IDs of the candidate information obtained in O-3 match, they will be used as examples, and if they do not match, they will not be used as examples. If the type of example is a semantic example, it is stored in the example item of example information in the dictionary.

If the semantic attribute and the semantic attribute obtained from the word ID of the candidate information obtained in 5IO-3 match, the word will be used as an example, and if they do not match, it will not be used as the example. S 10-6 if it is determined that the person will be the other party of the example.
Proceed to SI
Proceed to O-3.

At 510-6, the return that the example holds;
Set the code.

In SIO-7, a return code indicating that the example does not hold is set. .

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

511-1, the word I stored in the candidate information
By using D, information on the usage examples that exist for the word is retrieved from the dictionary one by one.

In 5it-2, it is determined in Sl 1-1 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, proceed to 5ll-3, and check if the example does not exist.
If all the examples in the dictionary have been extracted, the forward example extraction process is completed and the process returns.

At 5tt-3, it is determined whether or not the input reading can be established when the extracted example is applied in the forward direction. If it can be established, proceed to 5ll-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 example information does not exist in the target homophone candidate information, the newly created example information is stored in the usage information pointer. Stores a pointer to. 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 target homophone.

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

At S+2-1, it is checked whether there is a clause before the target clause. If the previous phrase exists, the process proceeds to 512-2; otherwise, the process proceeds to S12-8.

In S I 2-2, candidate information is obtained one by one from the homophones for the previous phrase.

At 512-3, it is determined whether candidate information has been retrieved at 312-2. S if you can get candidate information
The process proceeds to S12-4, and if all the candidate information has been obtained, and no candidate information has been obtained, the process proceeds to S12-8.

In S12-4, it is determined whether the word of the candidate information obtained in 512-2 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 312-2
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 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 512-2 must match. If it does, it will be the partner of the example, and if it does not, it will not be the partner of the example. If it is determined that the user will be the target of the example, the process advances to 512-5, and if it is determined that the target is not the target of the example, the process advances to S12-2.

In S12-5, the remaining pronunciations (adjunct word strings) other than the independent part of the candidate information obtained in S12-2 are converted into combined information using the adjunct word string conversion table.

At 312-6, it is determined whether the homophone candidate of the previous clause satisfies the example combination condition. That is, in step 512-5, if it cannot be converted into join information using the adjunct word string conversion table, it is determined that the join condition is not satisfied. In addition, the connection information obtained in 512-5,
If there is matching information with 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, it is assumed that the combination condition is not satisfied. If the connection condition is satisfied, the process proceeds to S + 2-7; if the connection condition is not satisfied, the process proceeds 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 that the example does not hold.

(Next time π umbrella “white”) Shi---(.

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 priority candidates, among the candidate information of the homophone, 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 moved to the top.All. When the pick-up of priority candidates is completed for the candidate information, the process proceeds to S13-2 and a process of excluding the suppressed examples from the priority candidates is performed.For the candidate information for the priority number from the beginning of the candidate information, the following is performed. By performing the process described above, suppression examples are prioritized and excluded.First, the example information pointer in the candidate information is traced, and the example ID in the example information is extracted.Whether the example learning data indicated by the extracted example ID is in a suppressed state. Until the example information pointer reaches the end, that is, the pointer value is “-1”.
As a result of checking whether or not the example IDs up to the point where the example ID indicates the candidate ++' have been suppressed, only if the example information pointer does not indicate the candidate ++' is suppressed, that candidate information is selected from the priority candidates. exclude. In other words, reduce the priority number by 1 and select Sort the i'li information immediately after the end of the priority candidate.

In 513-3, as a result of 313-2, it is determined whether or not there are any priority candidates remaining, depending on whether the number of priority candidates is 0 or not, and (I
'If there is no prior candidate, the process branches to 513-5, and if one or more priority candidates remain, the process branches to 313-4.

Among the candidate information left as priority candidates in 513-4,
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, follow the example information pointer in the candidate information, check the type of example for the example information indicated by the example information pointer, and if there is at least one individual word example, it is determined that there is an individual word example. until the example information pointer reaches the end, that is, the pointer value is "-1".

If the types of examples are examined and all 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 313-6, and if there is no example of an individual word, the process branches to 313-7.

In step 513-6, as a result of the individual word example search in 813-5, candidate information that is all semantic attribute examples is excluded from the priority candidates. That is, the priority number is decreased by 1, and the candidate information is rearranged immediately after the Iyc supplementary end.

As a result of the above processing, it is determined in step 313-7 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, S 13
Proceed to -9 and determine the priority candidate as the first candidate. If the priority number is not 1, it has not yet been narrowed down to one, and the candidates are narrowed down by word learning in step 513-8.

513-8 extracts the word ID of the priority candidate, iI+,
Based on the word learning data indicated by the word ID, if a word-learning candidate exists, the candidate whose word is likely to be learned is determined as the first candidate in 513-11. -! If there are a plurality of candidates that have been studied in the Ii language, the previously stored candidate information is set as the first candidate. If none of the priority candidates have words learned, 51
In step 3-10, select the candidate stored at the beginning of the priority candidates as the first
Decided as a candidate.

If it is determined in step 513-3 that there is no priority candidate, it is determined in step S13-5 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 4i-word learning candidate is determined as the first candidate in 313-11. If there are multiple word-learned 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 5I3-12.

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

In S+4-1, check whether there is an example in the stored homophone for the candidate whose selection/confirmation was instructed by the selection key SEL, and refer to the example information pointer for all candidate information in the homophone. 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 the candidate information, the process branches to S14-3, and if there is at least one example, the process branches to S14-2.

In step S14-2, it is determined as follows whether or not there is an example established by the word selected in the example information.

First, the example information pointer in the candidate/information of the selected emotion word is followed, and the pair homophone ID and example ID are extracted. Follow the example information pointer of the first candidate information in the homophone indicated by the extracted pair homophone ID, and find the bare homophone I.
D is an existing homophone of the selected word! Search to see if there is something that is D. If such example information is not retrieved, it is determined that the example does not hold true. If the bare homophone ID or the example information that matches the homophone ID of the selected word is retrieved, take out the example ID and check whether 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. If it is determined that the example holds true, 5
The process advances to step 14-6, and if it is determined that the example does not hold, a similar determination is made for the next example information of the bare homophone. If the usage example does not hold through the usage example information of the paired homophone, the same determination process is performed for the next usage example information of the selected word. Usage example of the selected word [If there is no blue report, it is determined that there is no valid example, and 514-
Branch to 5.

SI 4-4 is a process for canceling example suppression. 514-
The example learning data indicated by the example ID of the example determined to be a valid example in step 2 is made usable.

In step S14-3, if there is no example of the word selected in step 514-1, the existence of examples is determined 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 in the 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 put into the learning state, and the unselected words in the same phonetic speech are displayed. A process is performed to cancel the learning state of the 乍word learning data indicated by the J\word ID of the in information.

At step 514-7, the selected notation is confirmed in the document. Confirmation processing 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.

1. "Mihoko" (Explanation of Effect) As is clear from the above explanation, the present invention allows the use of synonyms even for ambiguous homophones by determining the concatenation condition when applying the example. can be applied correctly and the first candidate for the homophone can be determined accurately, so there is no need to convert to the next candidate, and efficient input work 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. 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 the character processing device of the present invention. . Figure 4: Reference of attached story sequence conversion table Figure 7: C-yon Figure 8: Section 9: Figure 10: Figure 11: Figure 12

Claims (1)

[Claims] An input means for inputting the pronunciation of a word, a word dictionary storing correspondence between the pronunciation and the notation of the word, a pair of words having a co-occurrence relationship, and a word dictionary having a co-occurrence relationship. a usage dictionary that stores combination conditions for generation of phrases; a phrase recognition means for recognizing the pronunciation inputted by the input means as a phrase by searching the word dictionary; and a phrase recognized by the phrase recognition means. and example search means for searching the example dictionary and extracting valid examples for the pair, wherein the example search means determines whether the examples are valid for the pair, and the example search means uses the examples stored in the example dictionary as a means for determining whether the examples are valid. 1. A character processing device comprising a combination condition determining means for determining a combination condition.