JPH0640328B2 - Character processor - 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, for the reading line "Gyu ni ou ni ou kyou de ka", "milk""cow""in""at the ranch""I"
"On", "emotion", "up", "emotion", "out", "keep", "buy"
Phrase candidates such as “section”, “mosquito”, and “u” are created, and a combination of these, “Keep milk at the ranch” and “Buy milk at the ranch” is output and displayed as conversion candidates. It cannot be said that either “buy” or “keep” has a higher frequency, so that “buying milk at the ranch” is not always converted as the first candidate.

Therefore, in order to increase the conversion rate, a method called example conversion has been proposed. 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 “buy milk / buy” is stored in the example dictionary, and “buy milk” is converted as the first candidate in response to an input “was milky”.

Although the above example is an example of an individual example in which the relationship between words is described, a semantic example in which the relationship with a semantic classification is described is also proposed. For example, in the context of “to ni,” when “to” represents a place,
The notation is plausible, and when "~" represents a human, the notation "said" seems plausible. In order to perform this conversion correctly, the meaning examples of "to / to say" to "human" / to / to "place" are registered in advance in the example dictionary. Note that “human”, “place”, etc. are semantic classifications, and all words in the dictionary describe “human”, “place”, or not. With this mechanism, it is possible to correctly convert "I told the mayor,""I went to the city hall," etc.

[Problems to be Solved by the Invention] However, the conventional example conversion technique does not particularly specify which example should be prioritized when a plurality of examples are applicable, that is, when examples conflict. It was Even if there was a slight guideline to give priority to individual examples when there was a conflict between individual examples and meaning examples, it was not clear which example should be given priority in other situations. Therefore, since the first candidate was appropriately decided by adopting an appropriate example, the conversion rate in a delicate situation was not good.

[Means (and Action) for Solving the Problem] A character processing device of the present invention is a word in which an input means for inputting a kana character string, a word reading, a notation and a semantic classification are associated and stored. The dictionary means, the word notation, the example notifying means for storing the notation or the meaning classification of the word used in combination with the word in association with each example with the priority of the example, and the word dictionary. With reference to the means, a candidate creating means for creating a candidate for a notation in which the kana character string input from the input means is read, and a notation candidate for the same reading created by the candidate creating means, When there is an example to which the semantic classification of words to be used together corresponds, by an operation using the priority of the example and the number of phrases between the phrases including each of the words corresponding to the example,
Based on the display priority determined by determining the likelihood of the example and determining the display priority of the notation candidate based on the likelihood, and the display priority determined by the display priority determination means, By providing the candidate display means for displaying the notation candidates created by the candidate creating means, when a plurality of examples compete with each other, the priority of the example itself and the phrase containing each of the words corresponding to the example The priority of the notation candidate is determined by an operation using the phrase count.

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 19
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. DIC is a dictionary for Kana-Kanji conversion. YDIC is an example dictionary that describes a co-occurrence relationship between words that have not been classified. BCTBL is a phrase candidate table that stores phrase candidates that are in the process of conversion. TBUF
Is a text buffer in which text data being input and edited is stored. DOBUF is a homophone buffer,
When a homophone candidate exists for the character stored in the text buffer TBUF, the candidate is stored.

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

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

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 a pattern of data to be displayed. When displaying the content of the text, a display pattern is created in the DBUF in accordance with the content of the text buffer TBUF, so that it is 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 "Bokujo deka" is entered, it is converted to "Keep at the farm" as the first candidate. This is because the example "Branch / keep" is registered in the example dictionary.
If there is no such example conversion mechanism, there are homophones such as "keep", "buy", "cross" for "Kau".
It will not always be converted correctly.

The following example is for the case of entering "Gyu ni Uwa". As in the first example, since the example of “buying milk / buying” is registered in the example dictionary, “buying milk” should be correct.
Is converted to.

The third example is the conversion in the case of inputting "Gyu ni ou boku jo ka ka". In this case, the two examples of "keeping on the ranch" and "buying milk" are applicable, and each requires a contradictory conversion for the "kau" part. That is, the conversion result of "kau" changes depending on which example is believed. For such a case, the present invention has priority information in each example and performs conversion based on an example with a high priority. In this case, the example of "buying milk / buying" is set to have a higher priority than the example of "being on the farm / raising", and the example of "buying milk /
The "buy" example has been applied and converted to "buy milk on the ranch."

FIG. 3 is a diagram showing an example of learning an example by the device of the present invention. 3-1 shows the initial screen. 3-
Reference numeral 2 shows a screen when the reading column "Ushio-dokujo-deka" is entered. The cursor is displayed next to the input reading row. When the conversion key is pressed here, the screen of 3-3 appears. In 3-3, the reading column "Ushio bokujo de ka" is converted to "keep cows on the farm." In this situation, there are examples of “cattle / keeping”, “on the farm / keeping”, and “cattle / buying”, but the highest priority “cattle / keeping” is applied and converted. If the operator desires the conversion of "buy / buy", the next candidate key is pressed, and the screen of 3-4 is displayed. Here, the conversion candidates of "KAU" are displayed. The first candidate is “keep” and the second candidate is “buy”. The current candidate is the second candidate, "buy",
2 is highlighted. If you hit the select key here,
The screen becomes 3-5. The second candidate “buy” is confirmed and stored in the text data, and at the same time, the example “cow / buy” is learned and the priority is improved. If you enter "Ushijo Bokujou" again, the screen 3-6 appears.
If you press the conversion key further, the screen of 3-7 appears, and this time, the example "Buy cow / buy" has been learned, so it is applied preferentially, and "Buy cow at the farm" is displayed as the first candidate. ing.

FIG. 4 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. 5 is a diagram showing the structure of the dictionary DIC.

It is composed of fields of "reading", "notation", "part of speech", "word likelihood", and "semantic classification".

"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 1 to 5. The likelihood value 5 means that it is most likely, and the smaller the value, the more suspicious. A likelihood value of 0 does not exist as a word likelihood value because it means that it cannot be considered at all.

In the “semantic classification”, a plurality of semantic classifications of the word are generally described as “place”, “animal”, “food”, etc. However, semantic classification is described only when the word is a noun.

FIG. 6 is a diagram showing the configuration of the example dictionary YDIC.
It is composed of fields of "first word", "second word", "particle" and "priority".

A pair of co-occurring words is described in “first word” and “second word”. If it is not a specific example but a semantic example, a semantic classification is described instead of a word.

The "particle" describes a particle that connects a pair of words.

Information indicating the likelihood of the example is 1 to "priority".
It is stored as a value of 5. The likelihood value 5 means that it is most likely, and the smaller the value, the more suspicious. Likelihood value 0 means that it cannot be considered at all, so it does not exist as a priority value.

That is, the example shown in the figure is "at the ranch / keep" (priority 4) "buy / buy milk" (priority 5) "beef / keep"
(Priority 5) "buy / buy cow" (priority 4) "// keep animal" (priority 2).

Since the third example is a meaning example, the priority is kept low. By doing so, the principle that "individual examples take precedence over semantic examples" naturally holds.

FIG. 7 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.

At the beginning of the input reading string "Gyu ni wo wo ku jo desu ka", there are interpretations such as "milk" and "cow", which are sequentially linked by a younger brother pointer (vertical line).

The phrase following "milk" is considered "at the ranch" and is linked by the child pointer.

When such a phrase candidate table is completed, "buy milk on the ranch", "keep milk on the ranch", "buy milk on my own terms"
It is easy to create a phrase candidate sequence such as "Keep Uriiri at the ranch" or "Buy Uyuiri for me".

FIG. 8 is a diagram showing a specific configuration of the phrase candidate table BCTBL.

The "independent word" stores a pointer at the head position in the dictionary where the independent word of the bunsetsu candidate exists.

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 12 in the second byte. When there is no adjunct word string, it is indicated by "φ" in the figure.

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.

Note that the link means that it terminates when the value is 0.

For example, the younger brother link of the phrase candidate 0 (milk) is 50, and the phrase candidate 50 (cow) is linked. The child link of the phrase candidate 0 (milk) is 100, and the phrase candidate 100 (at the ranch) is linked. The child link of the phrase candidate 100 is 40
It is 0, and the phrase candidate 400 (buy) is linked. The child link of the phrase candidate 400 is 0, and it can be seen that the input reading ends there.

FIG. 9 is a diagram showing the structure of the text buffer TBUF.

The text buffer is composed of a list of characters, and each character is composed of 2 bytes. The MSB of each character is a homophone word flag, 0 means a normal character, and 1 means a homophone word. The remaining 15 bits represent a character code in the case of a normal character and represent a homophone number in the case of a homophone. For the character code, for example, JIS X 0208 code is used. Homophone number is tenth
It is a number indicating which homonym is in the homophone word buffer DOBUF shown in the figure.

FIG. 10 is a diagram showing the structure of the homophone word buffer DOBUF. Each homophone has a homophone number, which identifies it.

Each homonym is "yomi""total number of candidates""current candidate number""i-th
Candidate information ”.

“Yomi” stores the reading of the homonym.

The “total number of candidates” stores the total number of candidates included in the same sound.

The "current candidate number" stores the currently displayed candidate number of the homonym. Since the first candidate is displayed in the initial value immediately after conversion, "1" is stored.

The "i-th candidate information" stores the notation of each candidate and the number of the example applied to the candidate. Usually, there are a plurality of examples applied to the candidate, so a plurality of application example numbers are entered.

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 larger the value, the more likely it is.

The sentence likelihood is calculated by adding all the sums of the phrase likelihoods, the sums of the inter-phrase likelihoods, and the sums of the example likelihoods.

The phrase likelihood expresses the likelihood of each phrase candidate, and the larger the value, the more likely it is. As the phrase likelihood, the word likelihood described in the dictionary DIC of the independent word of the phrase is used.

The inter-phrase likelihood expresses the likelihood of connection between adjacent phrase candidates, and is fixed at a value of -20 in this embodiment. If the sentence is composed of n bunsetsu, there is (n-1) between the bunsetsus, so the sum of the inter-bunsetsu likelihoods is always -20.
(N-1).

The example likelihood is applied to the sentence likelihood for each example, which is applied between clauses. The example likelihood is four times the priority described in each example, minus two times the number of clauses skipped when applying the example.

According to FIG. 11, the sentence likelihood of “milk / raising / raising” is −18, and the sentence likelihood of “milk / raising / buying” is −16. It can be seen that the example 2 of “large milk / at the farm / buy” is converted to the first place.

The reason for such correct conversion is that while the example "Appare on the farm / Keep" has a priority of 4 applied to Example 1, Example 2
This is because the priority of the example "buying milk / buying" applied to is 5 and the sentence to which the higher priority example is applied is more favorably converted.

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 it is the next candidate key, the next candidate process detailed in FIG. 18 is performed in step 12-4. If it is the select key, step 1
In 2-5, the selection process detailed in FIG. 19 is performed.
If the key is any other key, the process branches to step 12-6 to perform other processes such as insertion and deletion which are carried out in a normal character processing device. 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, the phrase candidate table BCTBL is created by performing the phrase candidate creation process described in detail in FIG.

In step 13-2, the first candidate determination process described in detail in FIG. 15 is performed.

In step 13-3, a conversion result is created and output based on the determined first candidate.

FIG. 14 is a detailed flowchart of the “segment candidate creating process” 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, a morphological analysis process is performed to analyze the attached word string connected to the found word candidate. 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, a child link and a younger brother link are set for a phrase candidate in which this phrase candidate is a child or a younger brother. 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 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 process” in step 13-2.

In step 15-1, the maximum likelihood sentence likelihood is initialized to the minimum value allowed in processing, for example, -32767.

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

In step 15-3, as will be described in detail with reference to FIG. 16, an example applicable to the extracted phrase candidate sequence is searched, and the “example likelihood sum” that is the sum of example likelihoods is calculated.

In step 15-4, the sentence likelihood of the phrase candidate sequence is set to the first value.
It is calculated as shown in FIG.

In step 15-5, it is determined whether the calculated sentence likelihood is more likely than the maximum likelihood sentence likelihood, specifically, larger,
When it is larger, the maximum likelihood sentence likelihood is updated to the calculated sentence likelihood in step 15-6. In step 15-7, the current phrase candidate sequence is stored as the phrase candidate sequence corresponding to the maximum likelihood sentence likelihood.

In step 15-8, 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.

FIG. 16 is a detailed flowchart of “calculation of example likelihood sum” in step 15-3.

In step 16-1, the example likelihood sum is initially initialized to 0.

In step 16-2, one phrase is extracted as a focused phrase from the phrase candidate sequence. In the following process, the likelihood of the example related to this focused phrase will be obtained.

In step 16-3, the maximum likelihood example likelihood is initialized to 0.

In step 16-4, a phrase paired with the focused phrase is extracted from the phrase candidate string.

In step 16-5, when the pair phrase of the target phrase cannot be extracted, the process of the further target phrase is abandoned, and the process branches to step 16-8.

In step 16-6, as detailed in FIG. 17, it is checked whether or not the example between the target phrase and the pair phrase is applied, and the example likelihood is set according to the application situation.

In step 16-7, it is checked whether the obtained example likelihood is more likely (that is, larger) than the maximum likelihood example likelihood. To. Then step 16
-4 and obtain the example likelihood for another pair.

In step 16-8, since the processing of the target clause has been completed, the processing moves to the next target clause, but it is determined whether or not there is an unprocessed clause. If not, the process returns as it is, but if it remains, the process branches to step 16-9 to add the maximum likelihood example likelihood obtained last time to the example likelihood sum. After that, the process branches to step 16-2, and the next focused phrase is extracted.

FIG. 17 is a detailed flow chart of "setting the value of the example likelihood" in step 16-6.

In step 17-1, it is determined whether or not there is an example applied between the two clauses of the target clause and the pair clause.

If there is no application example, the process branches to step 17-2, 0 is assigned to the example likelihood, and the process returns.

If an application example exists, branch to step 17-3,
As the example likelihood, a value obtained by multiplying the priority of the example by 4 is substituted.

In step 17-4, the distance between two clauses, that is,
The number of bunsetsus to be skipped is obtained, and the value is multiplied by 2 and subtracted from the example likelihood. If there are two adjacent clauses, the jumping clause is 0, so 0 is subtracted from the example likelihood.

In step 17-5, it is judged whether or not the value of the calculated example likelihood is negative, and if negative, the value is corrected to 0 in step 17-6, and the process returns. When it is not negative, it returns without changing the value.

FIG. 18 is a detailed flowchart of the "next candidate process" in step 12-4.

In step 18-1, the homophone number of the homophone to see the next candidate is obtained from the text buffer TBUF.

At step 18-2, the position of the homophone word buffer is obtained from the homophone word number and the current candidate number is counted up.

In step 18-3, the candidate list is displayed.

FIG. 19 is a detailed flowchart of the "selection process" in step 12-5.

In step 19-1, the homophone number of the homophone to be selected is obtained from the text buffer TBUF.

In step 19-2, the position of the homophone word buffer is obtained from the homophone word number, the notation is extracted from the candidate information indicated by the current candidate number, and is set as the fixed character in the text buffer TBUF.

Similarly, in step 19-3, the application example number is obtained from the candidate information indicated by the current candidate number, and the priority of the example is counted up.

In step 19-4, similarly, the application example number is obtained from the candidate information indicated by the first candidate, and the priority of the application example is counted down.

[Other Embodiments] In the above description, the example dictionary is described as a pair of co-occurring two words (or classifications), but the same processing is performed even if it is a set of three or more generally n sets. can do.

Further, the calculation of the likelihood is merely an example, and if the priority given to the example is configured to be considered in the process of the likelihood calculation, even if another calculation method is used, the calculation of It does not spoil the purpose.

Moreover, the learning mechanism of the examples is configured to directly operate the priority described in each example. However, other than this method, even if the learning bit corresponding to each example is provided and the learning bit is processed, the gist of the present invention is not particularly impaired.

[Effects of the Invention] As described above, according to the present invention, when a plurality of meaning examples are applicable in the determination of a notation candidate, the priority of the meaning example itself and the number of clauses existing between the examples. Since the likelihood of the example is obtained by the calculation using, and the priority of the notation candidate is determined based on this likelihood, it is possible to preferentially perform conversion to a more appropriate candidate. .

In addition, since the priority information of the meaning classification can be changed by learning, it is possible for an operator who uses a word for a special meaning to improve the conversion to his or her preference.

This makes it possible to realize a comfortable character processing device with a high conversion rate.

[Brief description of drawings]

FIG. 1 is a block diagram of the overall configuration of the present invention, FIG. 2 is a diagram showing a conversion example of kana-kanji conversion in the present invention, and FIG. 3 is a diagram showing a priority learning effect of an example in the present invention, FIG. 4 is a diagram showing a configuration of an input buffer IBUF and an output buffer OBUF in the present invention, FIG. 5 is a diagram showing a configuration of a kana-kanji conversion dictionary DIC in the present invention, and FIG. 6 is an example dictionary in the present invention. The figure which showed the structure of YDIC, FIG. 7 is the figure which showed notionally the memory content of the phrase candidate table BCTBL in this invention, FIG. 8 is the figure which showed the structure of the phrase candidate table BCTBL in this invention, 9th. The figure shows the structure of the text buffer TBUF in the present invention, FIG. 10 shows the structure of the homophone word buffer DOBUF in the present invention, and FIG. 11 shows the likelihood calculation method in the present invention. The figure which showed the formula, FIGS. 12-19 is a flowchart which shows operation | movement of the character processing apparatus of this invention. DISK ... External storage unit CPU ... Microprocessor ROM ... Read-only memory RAM ... Random access memory IBUF ... Input buffer OBUF ... Output buffer DIC ... Kana-kanji conversion dictionary YDIC ... Example dictionary BCTBL ... Candidate table TBUF ... Text buffer DOBUF ... Homophone buffer

Claims (2)

[Claims] 1. An input means for inputting a kana character string, a word dictionary means for storing a reading of a word, a notation and a semantic classification in association with each other, a word notation, and a word used in combination with the word. As an example in association with the notation or the semantic classification, the example dictionary means for storing each example with the priority of the example, and the kana character string input by the input means with reference to the word dictionary means When there is an example in which the meaning classification of a word used in combination with a candidate creation unit that creates a candidate for a notation that reads, and a notation candidate for the same reading created by the candidate creation unit is applicable, The likelihood of the example is obtained by an operation using the priority of the example and the number of phrases between the phrases including each of the words corresponding to the example, and based on the likelihood, display priority of the notation candidate is given. Display to determine the degree A character processing device comprising: priority determination means; and candidate display means for displaying the notation candidates created by the candidate creation means based on the display priority determined by the display priority determination means. . 2. A selecting means for selecting one of the candidates displayed by the candidate displaying means, and a selecting means for selecting one of the candidates other than the candidate displayed with the highest priority by the selecting means. Regarding the example related to the selected candidate or the candidate displayed with the highest priority,
2. The character processing device according to claim 1, further comprising a priority updating unit that updates the priority of the example stored in the example dictionary unit.