JPH01229365A - Character processor - Google Patents

Abstract

PURPOSE:To eliminate the unconditional application of a reversed example, and to evade the inadequate use of an example by deciding whether extraction in a reverse direction is possible or not in a case that the example of the reverse direction is extracted, and stopping the extraction when the extraction is impossible. CONSTITUTION:For the respective examples stored in example information in a word dictionary DIC provided in a RAM, information whether said example can be used by reversing it or not is stored. Then, in the case that the example of the reverse direction is extracted according to this information, it is decided whether the extraction of the reverse direction is possible or not, and only when the example of the reverse direction can be extracted, the example of the reverse direction is extracted, and at the time of the decision that the extraction is impossible, the extraction is stopped. Thus, the unconditionally reversed example is never applied for the example whose coorigination relation is broken if it is reversed, and since the inadequate use of the example can be evaded, erroneous conversion can be reduced.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to an old Japanese ambiguity determination 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-kanji conversion device in a Japanese word processor has been widely used. As a result of Kana-Kanji conversion, polysemy resolution of homophones has been carried out based on the grammatical characteristics of the part of speech of each individual word for the many homophone candidates.
In many cases, this alone was not enough. Therefore, by using combinations in an example dictionary that stores pairs of words that have a semantic co-occurrence relationship (i.e., usage examples) to determine the first candidate, ambiguous meanings of words from the same era have been resolved. . Furthermore, in order to achieve greater effect with less memory,
Examples have also been used in the opposite direction.

[Problem that the invention is trying to solve] However,
In conventional technology, examples are applied in the opposite direction, even to examples whose co-occurrence relationship would collapse if reversed, resulting in the problem of inappropriate examples being applied and causing erroneous conversion. Ta.

For example, if the idiom ``r horse fits j'' is reversed, the co-occurrence relationship will be broken (the meaning will be different), but if that idiom existed as an example, it would be used as ``a horse that matched the maggot.'' The example is also applied backwards to the input, giving
``Meeted'' is incorrectly translated as ``We met.''

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

That is, for each example stored in the example dictionary,
Stores information about whether the example can be used in reverse. Then, based on that information, when extracting examples in the opposite direction, it is determined whether or not it is possible to extract examples in the opposite direction, and only when examples in the reverse direction can be extracted, examples in the opposite direction are extracted. , if an example in the opposite direction cannot be extracted, the example in the opposite direction is not extracted.

By doing so, the reversed example will not be applied unconditionally to the example whose co-occurrence relationship would collapse if reversed, and the use of inappropriate examples can be avoided.

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;
An obscene dictionary that stores the pronunciations and spellings of words, a usage dictionary that stores co-occurrence pairs between words and information on whether or not the co-occurrence pairs can be used in reverse, and a usage dictionary that stores the pronunciations and spellings of words. forward example searching means for searching for examples that hold true in the forward direction for the analyzed pair of phrases; and backward example searching means for searching for examples that hold true in the opposite direction for the analyzed pair of phrases. [Embodiment] 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 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, in conjunction with DBP0OL, stores information on examples commonly used for homophone candidates.

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 adjunct words 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, 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 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, the 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, and when input from the keyboard KB is supplied, an interwoven signal is first sent to the microprocessor CP.
The microprocessor CPU reads various control signals stored in the ROM, and various controls are performed according to these control signals. It is a diagram showing the configuration.

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 includes the number of example information items that the heading word has, an example ID unique to the example, 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-buying J'' is distinguished by a consecutive number of 300, and the reading is ``r-ka'', and the notation is ``buying J''.
, has word information that the part of speech is r-verb J. There are two examples of individual word usage: ``And buy alcohol'' and ``Buy a fight.'' Furthermore, the vulgar word r-salmon J is distinguished by a consecutive number of 500, is pronounced as "sake," is written as "salmon J, part of speech is r-noun," and has the semantic attribute "r-animal." Since O is stored in the number of examples, no examples with "salmon" as a headword are 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. (C) 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 includes the bare homophone ID that stores the ID of the homophone that is the partner of the example, the example ID that stores the ID of the example being used, and the type of example that is being applied (individual example or semantic example). It consists of an example type to be stored and an example information pointer in which a pointer to another example information applied to the same candidate information is 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 adjunct word strings constituting a clause correspond to combination information in the example information held in 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 with the adjunct word string.

For example, the adjunct r is J, and the conjunction information ``is 1 or'' is 1.
In addition, even in the adjunct word string r, 1 can be replaced with the combination information "or 1" to determine the combination.

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. 86-2 when a 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 YOM 1 is pressed, and the code of the pressed reading is stored in the input reading buffer memory YBLJF.

56-4 is the process when the conversion key CON is pressed, and 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 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,
A process of learning the selected vulgar word is performed.

56-7 is a process performed when a key other than YOMI, 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. It is a process that
Since it is publicly known, it will not be specifically described.

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 into divided units, searches a word dictionary for analysis, and outputs only candidates that are recognized as phrases to a homophone pool. This is a process that is commonly performed in the industry and is well known, so it will not be specifically described.

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,
From the candidates that have been learned, select 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.

Figure 'fJ8 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 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.

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 there is a subsequent clause, proceed to 5IO-2; if not, proceed to S10
-Go 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 a continuous form, the process proceeds to 5IO-3, and if it is not a continuous form, the process proceeds to 310-7.

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

In 310-4, it is determined whether candidate information has been retrieved in S10-3. If candidate information is missing, the process advances to 5IO-5, and if all the candidate information has been acquired and no candidate information has been acquired, the process proceeds to 5IO-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 the S I O
If the word IDs of the candidate information obtained in step -3 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,
If the semantic attribute stored in the example item of the example information in the dictionary matches the semantic attribute obtained from the word ID of the candidate information obtained in SIO-3, the example is used. , and if it does not match, it will not be the target of the example. If it is determined that it will be the partner of the example, proceed to 5IO-6, and if it is determined that it will not be the partner of the example, proceed to S10-3.
Proceed to.

In 5IO-6, the return that the example holds true is
Set the court.

At 310-7, a return code indicating that the example does not hold is set.

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

In st t-i, information on examples existing in the word is retrieved from the dictionary one by one based on the word ID stored in the candidate information.

In 5ll-2, Sl! -1, it is determined whether there is an example in the dictionary for the word in 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 5ll-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 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 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. 12 is a detailed flowchart of the processing of 5it-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, S
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.

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 S1'
If the word IDs of the candidate information obtained in 2-2 match, the word IDs will be used as examples, and if they do not match, the words 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 it will be the partner of the example, the process proceeds to 512-5, and if it is determined that it is not the partner of the example, the process goes to S I 2-2.
Proceed to.

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

In S12-6, 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. Furthermore, if there is matching information between the combination information obtained in 512-5 and the combination information of example information in the dictionary, the combination condition is satisfied, and if there is no matching information, the combination condition is satisfied. It is assumed that the If the combination condition is satisfied, the process proceeds to 512-7; if the combination condition is not satisfied, the process proceeds to 512-2.

In 5IO-7, the return that the example holds true is
Set the code.

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 313-1, 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 S13-2, where a process is performed to exclude the suppressed usage examples 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, the example information pointer in the candidate information is followed and the example ID in the example information is extracted. It is checked 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 `` 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. In other words, The number of priority items is decreased by 1, and the candidate information is rearranged immediately after the end of the priority candidates.

In 513-3, 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, and one or more priority candidates remain. If so, it branches to 313-4.

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 candidate information is followed, and the type of example is determined for all the example information indicated by the example information pointer. If there is even one individual word example, it is determined that there is an "individual word example". 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 313-6, and if there is no example of an individual word, the process branches to 313-7.

As a result of the individual word example search for 313-5 in S 13-6,
The candidate information that is an example of the semantic attribute is then excluded from the replacement candidates. In other words, the number of priority items is decreased 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 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, 513-
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,
In S13-8, candidates are narrowed down by word learning.

513-8 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 513-3 that there are no priority candidates, then in 313-5 it is determined whether the first candidate is determined by word learning. Extract the word ID of the candidate information, and
The word learning data indicated by is examined, and if there is a candidate whose word has been learned, the candidate whose word has been learned is determined as the first candidate in step 513-11. If there are a plurality of word-learning candidates, the previously stored candidate information is set as the first candidate. If there is no learned word among all the candidate information, the first candidate information is determined as the first candidate in 513-12.

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

By referring to the example information pointer for all the candidate information in the homophone, it is possible to check whether an example exists in the homophone in which the candidate whose selection/confirmation was instructed by the selection key SEL in 5I4-1 is stored. judge. 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 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, 51
Proceed to 4-6 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. Unless all examples are established for the example information of the bare homophone, 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 the process branches to S14-5.

514-4 is a process for canceling usage suppression. In step 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.

If no example exists for the word selected in 514-1, step 514-3 performs an example existence determination to suppress examples of other homophones that were not selected.

For this purpose, it is determined whether or not example information exists in all example information other than the selected candidate information among the homophones in which the selected word exists, and if the example exists, it is determined in 314-5. The example learning data indicated by the example ID is disabled from use.

514-6 performs word learning processing. The word learning data indicated by the word ID of the selected notation is put into a learning state, and processing is performed to cancel the learning condition and deafness of the word learning data indicated by the word ID of the unselected candidate information among the homophones.

At step 514-7, 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 car-like dictionary structure in which word information and usage example information are written under the same headword, but it can also be processed in the same way as each dictionary table with word information and usage example information separated. be able to. 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 explanation, the examples were applied only between adjacent bunsetsu, but it is also possible to easily apply the examples 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 into clauses while taking into account the existence of usage examples (
(Explanation of effect) As is clear from the above explanation, information on whether or not an example can be used in the opposite direction is stored in the example dictionary, and when extracting an example in the reverse direction, the example in the reverse direction is extracted from that information. By determining whether extraction is possible, inappropriate usage can be avoided and erroneous conversions can be reduced.

[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. . Fig. 7 Fig. 8 E A-B Section 9 Section 10 Fig. 11 Section 12 Fig. 14

Claims (1)

[Claims] An input means for inputting the pronunciation of a word, a word dictionary that stores correspondence between the pronunciation and the notation of the word, a pair (example) of words having a co-occurrence relationship, and the following: an example dictionary storing information as to whether the example can be used in a backward direction; a phrase recognition means for recognizing the pronunciation inputted by the input means as a phrase by searching the word dictionary; and a phrase recognition means. forward direction example retrieval means for searching the example dictionary for the pair of phrases recognized by the above and extracting examples that hold true in the positive direction; and searching the example dictionary for the pair of phrases recognized by the phrase recognition means. A character processing device comprising: backward example search means for extracting examples that hold true in the opposite direction; and backward example extraction determination means that determines whether to extract examples that hold true in the opposite direction. .