JP2675912B2 - Character processor - Google Patents

Description

Description: TECHNICAL FIELD The present invention relates to a character processing device for inputting a kana-kana mixed sentence by kana-kanji conversion.

[Related Art] At present, a character processing apparatus such as a Japanese word processor generally inputs a sentence mixed with kanji and kana using kana-kanji conversion.

The kana-kanji conversion converts an input reading sequence into kanji by referring to a dictionary. The dictionary describes part-of-speech information such as nouns, sa-nouns, adverbs, adjectives, and adjective verbs for each word, and kana-kanji conversion analyzes the reading sequence to create possible phrase candidates and combines them. Conversion candidates, and present the conversion candidates in plausible order. Then, a candidate desired by the operator is selected from the presented conversion candidates.

For example, for the reading column “Impossible”, “Use”, “Specification”, “Use”, “Specification”, “Death”, “Bad”, “Sum”
Clause candidates such as "class", "side", "image", "use", and "use" are created, and these are combined to make "bad use", "poor specifications", "use side class", "use Japanese", etc. Is output and displayed as a conversion candidate. The operator selects and confirms his / her desired “bad specification” from the conversion candidates presented in this way.

By the way, the operation is too complicated if you select "poor specifications" every time you input "Impossible", so there is a learning function that will be converted as it is from the next time if you select once. It is generally provided.

The simplest of this learning function is to prepare a learning flag for each word for one beat, turn on the learning flag when a word is selected, and give priority to the word for which the learning flag is turned on when converting Kana to Kanji. It can be realized by the mechanism of converting. However, with only this, when two words in the conversion candidates are used alternately, the learning flags of both words are turned ON. As a result, only one word is always converted to the first place, and the learning effect is not obtained for the other word. Therefore, the learning flag of a certain word is turned on, and at the same time, an alternative candidate (conversion with a higher priority than the selected word) Candidates)
The process of turning off the learning flag is generally performed.

[Problem to be solved by the invention] However, if the selected word and the alternative candidate have different characteristics,
In some cases, learning OFF of conflict candidates is unnecessary. Conventionally, the learning of the contending candidate is uniformly turned off, so if the learning does not need to be turned off, the learning of the contending candidate is unnecessarily erased.

For example, consider the case where "Let's do it" is input.
There are conversion candidates such as "Let's operate" and "Operation specifications"
"Let's operate" is converted as a pattern of independent words + auxiliary words, and "operation specification" is converted as a pattern of independent words + independent words (that is, a compound word). In general, the adjunct word is converted with priority over the independent word, so that “Let's operate” is converted as the first candidate in a state where nothing is learned. But for some reason (probably "specification"
If the learning flag of "specifications" is ON, "operation specifications" will be converted with priority. If you change "Specification" to "Let's" at this time, "Let's"
The learning flag of "Specification" turns off at the same time that the learning flag of turns on. As a result, if "Let's say" is input next time, it can be converted to "Let's operate" as expected, but if you just type "Let's say", the learning flag is turned off. Are not converted in the first candidate. Normally, "use", which has a higher standard frequency, is converted. Note that the adjunct "shou" is not converted as a single phrase.

[Means (and Action) for Solving the Problem] According to the present invention, the character processing device stores the input means for inputting the reading, the reading of the word, the notation, and the learning data in association with each other. Means, an example storage means for storing a set of a plurality of words having a co-occurrence relationship as an example, and a reading input from the input means, with reference to the storage means and the example storage means, application of the example First candidate display means for deciding the first candidate word and displaying the notation of the first candidate word by prioritizing the word having a high priority represented by the selected word and the learning data, and the first candidate. Next candidate display means for displaying the notation of the subordinate candidates, selection means for selecting one candidate from the candidates whose notation is displayed by the next candidate display means or the first candidate display means, and the selection means. The priority of the word As described above, learning means for updating the learning data of the word, and determining whether or not the example is applied to each of the selected word and each candidate word above the selected word If the example is applied to the selected word based on the determination means and the result of the determination by the determination means, of the words of the higher-ranked candidates, the word determined to be the example is applied. By lowering the priority and suppressing the learning data of the word determined not to be applied to the example so as to be invariant, by improving the learning data of the selected candidate, it is also selected. If the example is applied to the selected word, the priority of the word that is determined to be the example among the candidate words higher than the selected word is lowered, and it is determined that the example is not applied. It Word of the learning data unchanged were, to prevent changing the learning data candidates other than the selected candidate unnecessarily.

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

FIG. 1 is an example of the overall configuration of a character processing device according to the present invention.

In the configuration shown in the figure, a CPU is a microprocessor, performs calculations for character processing, performs logical judgment, and the like, and is connected to those components via an address bus AB, a control bus CB, and a data bus DB. Control elements.

The address bus AB transfers an address signal indicating a component to be controlled by the microprocessor CPU.
The control bus CB transfers and applies a control signal of each component to be controlled by the microprocessor CPU. The data bus DB transfers data between the components.

Next, the ROM is a fixed read-only memory.
The procedure of control by the microprocessor CPU, which will be described later with reference to FIGS. To 27, is stored.

The RAM is a writable random access memory having a structure of one word and 16 bits, and is used for temporarily storing various data from each component. IBUF is an input buffer for storing keyed key data, and OBUF is an output buffer for temporarily storing the result of Kana-Kanji conversion. DIC is a dictionary for performing kana-kanji conversion. Y
DIC is an example dictionary that describes co-occurrence relationships between words. BCT
BL is a phrase candidate table that stores phrase candidates during conversion. CTBL1 is a phrase connection matrix that stores a connection rule between a phrase and a subsequent attached word. CTBL2 is a phrase connection matrix that stores the connection rules between a phrase and a suffix following it. CTBL3 is a phrase connection matrix that stores the connection rules between the prefix and the subsequent phrases. TBUF is a text buffer in which text data being input and edited is stored. DOBUF is a homophone word buffer, and when a homophone word candidate exists for the character stored in the text buffer TBUF, the candidate is stored.

KB is a keyboard, and character symbol input keys such as alphabet keys, hiragana keys, katakana keys, and,
It has various function keys such as a conversion key, a next candidate key, and a selection key for instructing various functions to the character processing apparatus.

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

CR is a cursor register. The CPU can read and write the contents of the cursor register. A 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 for storing patterns of data to be displayed. When displaying text contents, the display pattern is DBUF according to the contents of the text buffer TBUF.
Will be displayed.

The CRTC plays a role of displaying the contents stored in the cursor register CR and the buffer DBUF on the display CRT.

The CRT is a display device using a cathode ray tube or the like, and a display pattern of a dot configuration and a display of a cursor on the display device CRT are controlled by a CRT controller.

Further, CG is a character generator for storing patterns of characters and symbols to be displayed on the display device CRT.

In the character processing device according to the present invention comprising such components, the apparatus operates in response to various inputs from the keyboard KB. When an input from the keyboard KB is supplied, first, an interrupt signal is generated by a microcontroller. Processor
Sent to the CPU, the microprocessor CPU reads out various control signals stored in the ROM, and performs various controls according to the control signals.

FIG. 2 is a diagram showing an example of a conversion selection operation by a conventional character processing device. 2-1 shows the initial screen first. The upper part of the screen is a screen on which texts and readings during input are displayed, and the lower part is a screen on which a candidate list is displayed. The cursor is indicated by an underline. 2-2 shows a screen when the reading column "Impossible" is input. The input reading is displayed with an underline, and the cursor is displayed next to the input reading. Here, when the conversion key is depressed, a screen of 2-3 is displayed, and the reading sequence "Impossible" is converted to "Poor use". There are candidates such as "use" and "specifications" for "shall", but "use" is generally converted to the first candidate because it has a higher frequency.
If the operator desires a conversion of "bad specifications", he or she taps the next candidate key, and the screen of 2- appears. Here, a conversion candidate of "Let's" is displayed. The first candidate is “use”, and the second candidate is “specification”. The current candidate is the “spec” of the second candidate, and 2 is highlighted. Here, when the selection key is pressed, a screen 2-5 is displayed. Second
The "specification" of the candidate is determined and stored in the text data, and at the same time, the learning flag of "specification" is turned ON, and the priority is improved. Next, if you input "yes", the screen of 2-6 appears, and if you press the conversion key, the screen of 2-7 appears. Since “specifications” have been learned, “operation specifications” are displayed as first candidates. Here, when the next candidate key is touched, the screen of 2-8 is displayed, and the auxiliary word "shou" is displayed as the second candidate of "shou". Here, when the selection key is depressed, the screen of 2-9 is displayed, and "Let's" is determined and stored in the text data. At the same time, supposed word
Is turned on, and the learning flag of the independent word "specification" is turned off. Next, if you input "Sho ga Dai" again, the screen of 2-10 appears, and if you press the conversion key, the conversion result is displayed as in the screen of 2-11. Since the learning flag of "Specifications" is OFF, this conversion result is 2
-3 is exactly the same as the screen. In order to input "bad specifications", it is necessary to further press the next candidate key to display conversion candidates as in the screen 2-12. As described above, as long as "bad specifications" and "let's operate" are alternately input, it is necessary to always select the target candidate from the candidate list.

FIG. 3 is a diagram showing another example of the conversion selection operation by the conventional character processing device. 3-1 is a processing screen. 3-
Reference numeral 2 denotes a screen when the reading column “Fun fun” is input. Here, when the conversion key is typed, the screen of 3-3 is displayed, and the reading string "Fun is fun" is converted to "Five is fun". Although “go” includes candidates such as “go” and “go”, “go” is generally converted to the first candidate because it has a higher frequency. If the operator desires the conversion of "go is fun", the next candidate key is pressed, and the screen of 3-4 is displayed. Here, the conversion candidate of “go” is displayed. The first candidate is “five” and the second candidate is “go”. Here, when the selection key is pressed, a screen 3-5 is displayed. The second candidate “Go” is determined and stored in the text data, and at the same time, the “Go” learning flag is turned on, and the priority is improved.
Next, when "Spanish" is input, the screen of 3-6 is displayed, and when the conversion key is further pressed, a screen of 3-7 is displayed.
Since "go" has been learned, "(in Spanish) Go" is displayed as the first candidate. If you hit the next candidate key here, the screen of 3-8 appears, and the second
The suffix "word" is displayed as a candidate. Here, when the select key is pressed, the screen of 3-9 appears, and "in words" is confirmed and stored in the text data. At the same time, the learning flag for the suffix “in the word” is turned on, and the learning flag for the noun “go” is turned off. Next, if you input "goha" again, the screen of 3-10 will appear, and if you press the conversion key further, 3-
The conversion result is displayed as in the screen of 11. Since the learning flag of "go" is OFF, this conversion result is exactly the same as the screen of 3-3. In order to input "Go is fun", it is necessary to press the next candidate key and display the conversion candidates as shown on the screen 3-12. As described above, as long as "Go is fun" and "In Spanish" are alternately input, it is necessary to always select the target candidate from the candidate list.

FIG. 4 shows a third example of the conversion selecting operation by the conventional character processing device.
It is a figure showing the example of the. 4-1 is an initial screen.
4-2 shows a screen at the time of inputting the reading string "measure". Here, when the conversion key is pressed, the screen of 4-3 is displayed, and the reading sequence "measure" is converted to "measure".
“Measure” includes candidates such as “measure”, “measure”, and “conspire”, but since “measure” is generally more frequent, it is converted to the first candidate. Assuming that the operator desires the conversion of “conspire”, if the next candidate key is pressed twice, a screen 4-4 is displayed. Here, a conversion candidate of “measure” is displayed. First
The candidate is "measure", the second candidate is "measure", and the third candidate is "rebel". Here, when the selection key is pressed, a screen 4-5 is displayed. The third candidate “conspiracy” is determined and stored in the text data, and at the same time, the learning flag of “conspiracy” is turned on, and the priority is improved. Next, "Measure Shincho"
Is displayed, the screen of 4-6 is displayed, and when the conversion key is further pressed, the screen of 4-7 is displayed. Examples of "measuring height" and "measuring height" exist in the example dictionary, so
Although "has been learned", "measure height" is displayed as the first candidate. The reason why “measure” is converted prior to “measure” is that the frequency of “measure” is generally higher. Here, when the next candidate key is pressed, a screen 4-8 is displayed, and "measure", which also has an example, is displayed as a second candidate of "measure". Here, when the selection key is depressed, the screen of 4-9 is displayed, and "measure" is determined and stored in the text data. At the same time, the learning flag of "measure" is turned on, and the learning flag of "believe" is turned off. Next, when "Haru" is input again, the screen 4-10 is displayed, and when the conversion key is pressed, the conversion result is displayed as in the screen 4-11. Since the learning flag of "pluck" is OFF, "pluck" is not converted as the first candidate, but "measure" learned previously is converted as the first candidate. In order to input "Trust", it is necessary to press the next candidate key twice to display the conversion candidates as shown in the screen 4-12. If you select "Attract" here, the learning flag of "Measure" will be turned off at the same time, and "Measure Shincho" will be converted to "Measure height". As described above, it is necessary to always select the desired candidate from the candidate list as long as the "pluck" and "measure the height" are alternately input.

FIG. 5 is a diagram showing an example of a conversion selection operation by the character processing device according to the present invention. 5-1 is a processing screen. 5
Reference numeral -2 denotes a screen when the reading column "Impossible" is input. Here, when the conversion key is pressed, the screen of 5-3 is displayed, and the reading sequence "Impossible" has been converted to "Poor use". There are candidates such as "use" and "specifications" for "shall", but "use" is generally converted to the first candidate because it has a higher frequency. When the operator presses the next candidate key, assuming that the conversion of “specification is bad” is desired, 5-
The screen shown in Fig. 4 is displayed. Here, a conversion candidate of "Let's" is displayed. The first candidate is “use” and the second candidate is “specification”
It is. The current candidate is the "spec" of the second candidate. Here, when the selection key is pressed, a screen 5-5 is displayed. The “specification” of the second candidate is determined and stored in the text data. At the same time, the learning flag of “specification” is turned on, and the priority is improved. Next, when "Let's do it" is input, the screen of 5-6 is displayed, and when the conversion key is further pressed, the screen of 5-7 is displayed. Since “specifications” have been learned, “operation specifications” are displayed as first candidates. Here, when the next candidate key is touched, a screen 5-8 is displayed, and the auxiliary word "shou" is displayed as a second candidate of "shou". Here, when the selection key is depressed, the screen of 5-9 is displayed, "Let's" is determined and stored in the text data. At the same time, supposed word
Is turned on, but unlike FIG. 2, the learning flag of the independent word "specification" is not turned off but remains on. Next, when you enter "I'm sorry" again, 5-
The screen of 10 is displayed, and when the conversion key is pressed again, the conversion result is displayed as in the screen of 5-11. Since the learning flag of “specification” remains ON, the conversion result is as the previous selection operation by the operator. Further, if you input the reading column "Sou Sasou", the screen of 5-12 appears, and when you press the conversion key, "Let's operate" according to the previous selection like the screen of 5-13.
Is converted as the first candidate. After this, even if the user repeatedly inputs "bad specifications" and "let's operate", it is no longer necessary to perform the selection operation.

FIG. 6 is a diagram showing another example of the conversion selection device by the character processing device according to the present invention. 6-1 is an initial screen. 6-2 shows a screen at the time of inputting the reading sequence "Fun fun". If you press the conversion key here, the screen changes to 6-3.
Has been converted to. Although “go” includes candidates such as “go” and “go”, “go” is generally converted to the first candidate because it has a higher frequency. Assuming that the operator desires the conversion “Go is fun”, the next candidate key is pressed, and a screen 6-4 is displayed. Here, the conversion candidate of “go” is displayed. The first candidate is “five” and the second candidate is “go”. Here, when the selection key is pressed, a screen 6-6 is displayed. The second candidate “Go” is determined and stored in the text data, and at the same time, the “Go” learning flag is turned on, and the priority is improved.
Next, when "Spanish" is input, the screen of 6-6 appears, and when the conversion key is further pressed, the screen of 6-7 appears.
Since "go" has been learned, "(in Spanish) Go" is displayed as the first candidate. If you hit the next candidate key here, the screen 6-8 appears and the second
The suffix "word" is displayed as a candidate. Here, when the selection key is depressed, the screen of 6-9 is displayed, and "in words" is determined and stored in the text data. At the same time, the learning flag of the suffix “word” is turned on, but unlike FIG. 3, the learning flag of the noun “go” remains on. Next, if you input "goha" again, the screen of 6-10 is displayed, and if you press the conversion key, the conversion result is displayed as in the screen of 6-11. Since the “go” learning flag remains ON, the result of this conversion is as selected by the previous operator. If you enter "In Spanish" in the reading column, the screen becomes 6-12, and if you press the conversion key, the screen becomes 6-13. As in the previous selection, "In Spanish" is the first candidate. To be converted. After this, there is no longer any need to perform the selection operation even if "Go is fun" and "In Spanish" are repeatedly input.

FIG. 7 is a diagram showing a third example of the conversion selection operation by the character processing device according to the present invention. 7-1 is an initial screen. 7-2 shows a screen at the time of inputting the reading string "Measure". When the user presses the conversion key, the screen changes to screen 7-3, and the reading string "Measure" is converted to "Measure". There are candidates for "measure" such as "measure", "measure", and "conspire", but in general, "measure" is more frequent, so
Converted to a candidate. Assuming that the operator desires the conversion of "conspiracy", if the next candidate key is pressed twice, a screen 7-4 is displayed. Here, a conversion candidate of “measure” is displayed.
The first candidate is “measure”, the second candidate is “measure”, and the third candidate is “mare”. If you hit the select key here, 7-5
Screen. The third candidate “conspiracy” is determined and stored in the text data.
Becomes ON, and the priority improves. Next, the screen of FIG. 7-6 is displayed when "Measure Shincho" is input, and the screen of 7-7 is displayed by further inputting the conversion key. Examples of "measure height" and "measure height" exist in the example dictionary, so "measure height" despite learning "conspiracy"
Is displayed as the first candidate. Here, when the next candidate key is pressed, a screen 7-8 is displayed, and "Measure," which also has an example, is displayed as the second candidate of "Haru". Here, when the selection key is depressed, a screen 7-9 is displayed, and "measure" is determined and stored in the text data. At the same time, the learning flag of "measure" is turned on, but unlike FIG.
The learning flag of "Take" remains ON. At the same time, the example "Measure height" is suppressed, and after that "Measure height"
Will no longer be used. Next, if "Haru" is input again, the screen 7-10 is displayed, and if the conversion key is further pressed, the conversion result is displayed as in the screen 7-11.
Since the learning flag of "pluck" remains ON, this conversion result is as the previous selection operation by the operator. Further, if you enter the reading column “Shincho wa”, the screen of 7-12 will appear, and if you press the conversion key, the screen of 7-13 will be displayed. As shown in the screen of 7-13, “Measure height” will be the first candidate. To be done.
After that, it is no longer necessary to perform the selection operation even if the user repeatedly inputs “attempt” and “measure height”.

FIG. 8 is a diagram showing a configuration of the input buffer IBUF and the output buffer OBUF.

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

FIG. 9 is a diagram showing a configuration of the dictionary DIC. "reading"
"Notation""Part of speech""Standardfrequency""Part of speech classification""Learning"
Field.

"Yomi" is a word reading, "Notation" is a word notation,
The “part of speech” stores the part of speech of a word.

In the “standard frequency”, information indicating the likelihood of the word itself such as frequency information is stored as a value of 1 to 5. A likelihood value of 5 means most likely and is interpreted as suspicious as the value decreases. Since the likelihood value 0 means that it cannot be considered at all, it does not exist as a word likelihood value.

In the “part of speech classification”, information obtained by further subdividing the part of speech of the word is stored. For example, prefixes are further subdivided into several types. When a prefix is entered in the part of speech field, the subclassification information of the prefix is described in the part of speech subclassification. For the prefix "reverse", a sub-classification code of "prefix 23" is assigned. In the case of a noun, "abstract concept""article"
The sub-classification information such as "action" is stored. In particular, in the case of a part of speech in which there is no detailed classification information (for example, 5 lines in a row), no value is entered.

"Learning" is a learning flag, and indicates by 1 bit whether or not the word is learned. Words for which the learning flag is ON are preferentially converted. This is a short term learning scheme, since there are only two states of a word, learned or unlearned.

FIG. 10 is a diagram showing a configuration of the example dictionary YDIC. It is composed of fields of "first word", "second word", "particle" and "example suppression".

A pair of co-occurring words is described in “first word” and “second word”.

"Particles" describe particles that connect word pairs.

Information indicating whether or not the example can be used is stored in 1-bit in the "example suppression". 0 means not suppressed and 1 means suppressed. The initial state is 0, which means that the example may be used.

The examples shown in the figure are "Height / Measure", "Water depth / Measure", "Height / Measure", "Meet / Consult" / "Promote / Aim", and the reading string that matches this pattern is input. In this case, the notation shown in the example dictionary is preferentially converted.

FIG. 11 is a diagram showing the concept of the phrase candidate table BCTBL. The phrase candidate table is a representation of possible phrase candidates as a result of the analysis of the input reading in a binary tree. In the figure, a horizontal line indicates a child pointer, and a vertical line indicates a younger pointer. The younger brother pointer links other phrase candidates (usually shorter candidates) starting from a certain reading position, and the child pointer links a phrase candidate following the phrase.

At the beginning of the input reading string “Impossible”, there are interpretations such as “Use”, “Specification”, “Use”, “Specification”, and “Death”, and these are linked in order by a younger brother pointer (vertical line) .

The clause following "use is" is considered "bad" and is linked by a child pointer.

When such a phrase candidate table is completed, it is easy to create a phrase candidate sequence such as “bad use”, “poor specification”, “consideration type”, “use type”, and the like.

FIG. 12 is a diagram showing a specific configuration of the phrase candidate table BCTBL.

The “independent word” stores a pointer to a head position on the dictionary where the independent word of the phrase candidate exists.

The “adjunct word string” is composed of 2 bytes, and is an area for specifying an adjunct word string following the independent word of the bunsetsu candidate. The first byte indexes the first character of the adjunct string in the input buffer, and the next byte indexes the last character of the adjunct string in the input buffer. For example, in the case of the input buffer shown in FIG.
Set 6 in the second byte. When there is no attached word string, it is indicated by “φ” in the figure.

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

The “child link” links a phrase candidate that follows the phrase candidate.

Note that the link is terminated when the value is 0.

For example, the phrase candidate 0 (use) has a younger brother link of 1, and the phrase candidate 1 (specification) is linked. Phrase candidate 0
The child link of (to the newspaper shop) is 100, and links the phrase candidate 100 (bad). The child link of the phrase candidate 100 is 0, which indicates that the input reading is terminated.

"Independent part type" describes the type of the independent part of the phrase. This is specified when determining the connectability between adjacent clauses. For example, 0: a noun indicating an abstract concept, 1: a noun indicating a human, 2: a noun, 3: a place name stem, 4: an adjective, and the like.

“Clause type” describes the type of the clause. This is used to determine the possibility of connection between adjacent clauses. For example, 0: a clause ending with a noun indicating an abstract concept, 1:
Phrase ending with a noun that indicates a human, 2: Phrase ending with a phenomenon stem, 3: Phrase ending with a place name stem, 4: Phrase ending with an adjective ending form, 5: Phrase ending with the case particle "ni", 6 : Described as a clause ending with the case particle "ga".

FIG. 13 is a diagram showing the configuration of a phrase connection matrix for determining the possibility of connection between phrases.

(1) in FIG. 13 is a CTBL1 for determining the possibility of connection between a phrase and an attached word that follows it. 1 means connectable,
0 means that connection is not possible. For example, it can be seen that the phrase of the auxiliary word 125 can follow the phrase of the phrase type = 2, but the phrase of the auxiliary word 125 cannot follow the phrase of the phrase type = 3.

(2) is CTBL2 which determines the connection possibility of a phrase and a suffix following it. 1 means that connection is possible, and 0 means that connection is impossible. For example, it can be seen that a clause of suffix 15 can follow a clause of clause type = 3, but a clause of suffix 15 cannot follow a clause of clause type = 2.

(3) is CTBL3 for determining the possibility of connection between the prefix and the following clause. 1 means that connection is possible, and 0 means that connection is impossible. For example, it can be seen that a clause of prefix 23 can be followed by a clause of freestanding type = 2, but a clause of a clause of prefix 23 cannot be followed by a clause of freestanding type = 3.

FIG. 14 shows the structure of the text buffer TBUF.

The text buffer is composed of a sequence 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. Remaining
The 15 bits represent a character code for normal characters and a homophone number for homophones. As the character code, for example, JIS X 0208 code is used. The homophone number is a number indicating which homophone in the homophone buffer DOBUF shown in FIG.

FIG. 15 is a diagram showing the structure of the homophone buffer DOBUF. Each homonym has a homonym number and is identified by it.

Each homonym is composed of “reading”, “total number of candidates”, “current candidate number”, and “i-th candidate information”.

 "Reading" stores the reading of the same phonetic word.

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 same phonetic word. Since the first candidate is displayed as the initial value immediately after the conversion, “1” is stored.

In the “i-th candidate information”, “notation”, “word address”, and “applied meaning classification” of each candidate are stored.

The “notation” stores the candidate notation. ”The“ word address ”stores the address where the word candidate exists on the dictionary DIC.

The “applicable example number” stores an example number of an example that can be applied to the candidate. Here, the number of the suppressed example is also described. In general, since there are a plurality of examples that can be applied to the candidate, a plurality of “applicable example numbers” may exist for each candidate. In some cases, it does not exist at all.

FIG. 16 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 means that the larger the value, the more likely it is.

The sentence likelihood is calculated by adding all of the sum of phrase likelihoods, the sum of inter-segment likelihoods, and the sum of 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, a standard frequency described in a dictionary DIC of an independent word of the phrase is used. If the learning flag is ON, 26 is added.

The inter-phrase likelihood expresses the likelihood of connection between adjacent phrase candidates. In this embodiment, the following phrase is 0 when the subsequent phrase is an adjunct, −10 when the preceding phrase is a prefix, and suffix is In this case, it is -10, and in other cases, it is -20. With this definition, the attached word is converted before the independent word, and the prefix and the suffix are converted before the noun. For example, if the sentence is composed of n clauses, and all of them are in the other cases described above, there is (n-1) between the clauses, and the sum of the likelihoods between the clauses is -20 (n-1 )
Becomes

The example likelihood is added to the sentence likelihood for each example used between each phrase. The example likelihood is obtained by subtracting 10 times the number of clauses skipped when applying the example from 30.

FIG. 17 is a diagram illustrating a calculation example of the sentence likelihood of FIG. 16 for the reading sequence “Let's do it” for each situation.

(1) in FIG. 17 is a calculation example of “operation / let's go” when learning is not particularly performed, and the sentence likelihood is 8.

(2) is a calculation example of “operation / specification” when learning is not particularly performed, and the sentence likelihood is −12.

(3) is a calculation example of “operation / letter” when only the attached word “letter” is learned, and the sentence likelihood is 34.

(4) is a calculation example of “operation / specification” when only the independent word “specification” is learned, and the sentence likelihood is 14.

In other words, when there is no learning, it is converted to "Let's operate", when only "Specifications" is learned, it is converted to "Operation specifications", and when only "Let's learn" is learned, "Let's operate" It can be seen that when both "Let's" and "Specification" are learned, it is converted to "Let's operate".

FIG. 18 is a diagram for explaining a calculation example of the sentence likelihood of FIG. 16 for the reading sequence “Spain” according to the situation.

(1) in FIG. 18 is a calculation example of “Spain / language” when learning is not particularly performed, and the sentence likelihood is −2.

(2) is "Spain /
This is a calculation example of “go”, and the sentence likelihood is −14.

(3) is a calculation example of "Spain / word" when only the suffix "word" is learned, and the sentence likelihood is 24.

(4) is a calculation example of "Spain / Go" when only the independent word "Go" is learned, and the sentence likelihood is 15.

In other words, if there is no learning, it is converted to "Spanish", if only "Go" is learned, it is converted to "Spanish Go", and if only "word" is learned, it is converted to "Spanish" It can be seen that when both "go" and "word" are learned, they are converted to "Spanish".

FIG. 19 is a diagram for explaining a calculation example of the sentence likelihood in FIG. 16 for a reading sequence “measurement” for each situation. It is assumed that “measuring height” is registered as an example.

(1) in FIG. 19 is a calculation example of “measuring / height” when learning is not particularly performed, and the sentence likelihood is 18.

(2) is a calculation example of “height / involvement” when no particular learning has been performed, and the sentence likelihood is −15.

(3) is a calculation example of "height / measure" when only "measure" is learned, and the sentence likelihood is 44.

(4) is a calculation example of "height / attempt" when only "attack" is learned, and the sentence likelihood is 11.

In other words, if there is no learning, "measure / height"
In the state where only "meaning" is learned, it is converted to "height / measure", and in the state where only "measurement" is learned, it is converted to "height / measure", and "meaning"
It can be seen that in the state where both "measurement" and "measurement" are learned, "height / measurement" is converted.

The operation of the above embodiment will be described with reference to the flowcharts shown in FIGS.

FIG. 20 is a flow chart of a part for taking in a key input and performing processing.

Step 20-1 is a process for fetching data from the keyboard. The type of the key fetched in step 20-2 is determined, and the process branches to the processing routine for each key.

If it is the conversion key, the process branches to step 20-3, and in step 20-3, the conversion process of kana-kanji conversion is performed as described in detail in FIG. If it is the next candidate key, the next candidate process described in detail in FIG. 26 is performed in step 20-4. If it is the select key, the selecting process detailed in FIG. 27 is performed in step 20-5. If the key is any other key, the process branches to step 20-6 to perform other processes such as insertion and deletion which are carried out in a normal character processing device. Then it loops to step 20-1.

FIG. 21 is a detailed flowchart of the “conversion process” in step 20-3.

In step 21-1, the phrase candidate table BCTBL is created by performing the phrase candidate creation process described in detail in FIG.

In step 21-2, the first candidate determination process described in detail in FIG. 23 is performed.

In step 21-3, a homophone word buffer and a conversion result are created and output based on the determined first candidate.

FIG. 22 is a detailed flowchart of the “segment candidate creating process” in step 21-1.

In step 22-1, the input buffer index i and the phrase candidate table index j are initialized to 0.

In step 22-2, a dictionary is searched for a word candidate based on the reading in the input buffer indicated by i.

In step 22-3, a morphological analysis process is performed to analyze the attached word string connected to the found word candidate. As a result, a phrase candidate is obtained.

In step 22-4, the independent section type and the bunsetsu type of the bunsetsu are determined based on the result of the morphological analysis.

In step 22-5, the connectability of adjacent clauses is checked by referring to the clause connection matrix. Only the phrase that can be connected to the adjacent phrase is left. Further, a child link and a younger brother link on the phrase candidate table are determined.

The phrase candidates obtained in step 22-6 are stored in the phrase candidate table. When storing, it is stored in the (j + 1) th entry. Also, set necessary information. For example, a child link and a younger brother link are set for a phrase candidate that has this phrase candidate as a child or a younger brother. After storage, the value of j is counted up.

In step 22-7, a bunsetsu candidate that has not been terminated, that is, a bunsetsu candidate whose child link has not been decided yet is found from the bunsetsu candidate table, and the next reading position is set to i.
Substitute for

In step 22-8, it is determined whether or not the child links of all the bunsetsu candidates have been decided, and if there are some that have not been decided, the process branches to step 22-2. Otherwise return.

FIG. 23 is a detailed flowchart of the “first candidate determination process” in step 21-2.

In step 23-1, the maximum likelihood sentence likelihood is initialized to the minimum value permitted in processing, for example, -32767.

In step 23-2, one phrase candidate string is extracted from the phrase candidate table.

In step 23-3, an example applicable to the extracted phrase candidate sequence is searched as detailed in FIG. 24,
“Example likelihood sum” which is a sum of example likelihoods is calculated.

In step 23-4, the sentence likelihood of the phrase candidate sequence is set to 16th.
It is calculated as shown in the figure.

In step 23-5, it is determined whether the calculated sentence likelihood is more likely than the maximum likelihood sentence likelihood, specifically, larger,
If it is larger, the maximum likelihood sentence likelihood is updated to the calculated sentence likelihood in step 23-6. In step 23-7, the current phrase candidate sequence is stored as the phrase candidate sequence corresponding to the maximum likelihood sentence likelihood.

In step 23-8, 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 23-2. If not, return.

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

FIG. 24 is a detailed flowchart of the “calculation of the example likelihood sum” in step 23-3.

In step 24-1, the example likelihood sum is first initialized to 0.

At step 24-2, one phrase is extracted as a reference phrase from the phrase candidate sequence. In the following processing, the likelihood of the example related to this reference clause is obtained.

In step 24-3, it is judged whether or not the reference phrase can be extracted, and if not extracted, the process returns.

In step 24-4, the maximum likelihood likelihood is initialized to 0.

In step 24-5, a phrase paired with the reference phrase is extracted from the phrase candidate string.

In step 24-6, when the pair phrase of the standard phrase cannot be extracted, the process of the further standard phrase is abandoned and the process branches to step 24-9.

In step 24-7, as detailed in FIG. 25,
It is checked whether the example between the standard clause and the pair clause is applied, and the example likelihood is set according to the application situation.

In step 24-8, it is checked whether or not the obtained example likelihood is more likely (that is, larger) than the maximum likelihood example likelihood, and if there is likelihood (that is, larger), the value of the example likelihood is set to the maximum likelihood example likelihood. To. Then step 24-
Branch to 5 and find the example likelihood for another pair.

In step 24-9, since the processing of the standard clause is completed, the processing moves to the next standard clause, but it is determined whether or not there is any unprocessed clause. If not, the process returns as it is, but if it remains, the process branches to step 24-10 to add the maximum likelihood example likelihood obtained last time to the example likelihood sum. Then, it loops to step 24-2 and takes out the next reference clause.

FIG. 25 is a detailed flowchart of the “setting of example likelihood value” in step 24-7.

In step 25-1, it is judged whether or not there is an example applied between two clauses of the standard clause and the pair clause. At this time, processing is performed so that the suppressed example is not applied.

If there is no application example, the process branches to step 25-2, 0 is assigned to the example likelihood, and the process returns.

If there is an application example, the process branches to step 25-3 and 30 is substituted as the example likelihood.

In step 25-4, the distance between two clauses, that is,
Find the number of bunsetsu to jump, multiply by 10 and subtract from the example likelihood. If there are two adjacent clauses, the number of skipped clauses is 0, and thus 0 is subtracted from the example likelihood.

In step 25-5, it is determined whether or not the calculated example likelihood value is negative, and if negative, the value is corrected to 0 in step 25-6, and the process returns. If it is not negative, return without changing the value.

FIG. 26 is a detailed flowchart of the "next candidate process" of step 20-4.

At step 26-1, the homophone word number of the homophone to see the next candidate is obtained from the text buffer TBUF.

In step 26-2, the position of the homophone word buffer is obtained from the homophone word number, and the current candidate number is counted up.

 In step 26-3, the candidate list is displayed.

FIG. 27 is a detailed flowchart of the "selection process" of step 20-5.

In step 27-1, the homophone number of the homophone to be selected is obtained from the text buffer TBUF.

In step 27-2, 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 27-3, the word address is obtained from the candidate information indicated by the current candidate number, and the learning flag of the indicated word is turned ON.

Similarly, in step 27-4, the applicable example number is obtained from the candidate information indicated by the current candidate number, and the example suppression flag of the indicated example is turned off.

In step 27-5, candidates that conflict with the selected candidate, that is, candidates that are displayed higher than the selected candidate in the candidate list are taken out one by one.

In step 27-6, the processing is completed for all the conflict candidates, and if no more candidates can be extracted, the process returns.

In step 27-7, the category to which the selection candidate and the confrontation candidate belong, that is, the part-of-speech is examined. Loop to step 27-5. For example, when the selection candidate is the prefix and the conflict candidate is the noun, the process loops to step 27-5. If the selection candidate is an adjunct word and the conflict candidate is an independent word such as a noun or a verb, the process loops to step 27-5.

In step 27-8, it is determined whether or not the example is applied to the selection candidate, and if the example is not applied, the process branches to step 27-10.

When the example is applied to the selection candidates, step 27-
In 9, it is determined whether or not the example is applied to the conflict candidate, and if the example is applied, step 27-10.
Branch to When the example is applied to the selection candidate but the example is not applied to the conflict candidate, the learning flag of the conflict candidate is not operated and the process directly loops to step 27-5.

When the example is not applied to the selection candidate, or when the example is applied to both the selection candidate and the conflict candidate, in step 27-10, first, turn on the example suppression flag of the example applied to the conflict candidate, Make sure that the example is not specified in the future.

Next, at step 27-11, the learning flag of the conflict candidate is turned off. Then, it loops to step 27-5.

[Other Embodiments] In the above description, when an adjunct and an independent word conflict, when a noun and a suffix or prefix conflict, a candidate to which an example is applied and a candidate to which no example is applied conflict. In the three cases described above, the short-term learning OFF processing of the conflict candidate has been described.

The essence of the present invention is to minimize the range of information (learning information) that is corrected when learning a selected word. Therefore, the present invention is not limited to short-term learning, but can be easily applied to other learning, and significant effects can be expected.

For example, the present invention is also applicable when the learning method is a frequency learning method. At this time, the frequency of the selected word is counted up, and when the frequency value reaches the maximum value, the frequency of the conflicting word is counted down,
It is sufficient to determine whether the selected word can be converted as the first candidate without counting down (specifically, compare the frequency values of both), and if the conversion can be performed, the frequency countdown processing of the contradictory word may not be performed. .

For example, the present invention is also applicable when the learning method is a method of registering learned words in a dictionary format (learning dictionary method). In this case, the selected word is registered in the learning dictionary, and at the same time, the opposing words are expelled from the learning dictionary. Good.

Further, a method of storing addresses of learned words on a dictionary (learning word address storage method) is also conceivable. In this case, the address of the selected word is stored as learning data, and the address of the conflicting word is deleted.
After determining whether or not there is a need to delete, it is sufficient to perform a process of deleting the conflicting word address.

[Effects of the Invention] As described above, according to the present invention, the learning data of the selected candidate is improved, and if the example is applied to the selected word, the candidate higher than the selected word is selected. Among the words, the priority of the words that are determined to be applied to the example is lowered, the learning data of the words that are determined to be not applied to the example are made invariant, and learning of candidates other than the selected candidate is performed. It is possible to prevent the data from being unnecessarily changed, and to prevent the learning effect of the learning data set for a certain word from being unnecessarily erased.

As a result, a comfortable character processing device with a high conversion rate can be realized.

[Brief description of the drawings]

FIG. 1 is a block diagram of the overall configuration of a character processing device according to the present invention, FIGS. 2 to 4 are diagrams showing an operation example of conversion / learning in a conventional device, and FIGS. 5 to 7 are present inventions. 8 shows an example of conversion / learning operation in FIG. 8, FIG. 8 shows a configuration of an input buffer IBUF and an output buffer OBUF in the present invention, and FIG. 9 shows a configuration of a kana-kanji conversion dictionary DIC in the present invention. FIG. 10, FIG. 10 is a diagram showing the configuration of the example dictionary YDIC in the present invention, FIG. 11 is a diagram conceptually showing the storage contents of the phrase candidate table BCTBL in the present invention, and FIG. 12 is a phrase in the present invention. FIG. 13 is a diagram showing the structure of the candidate table BCTBL, and FIG. 13 is a clause connection matrix CTBL1, C in the present invention.
FIG. 14 is a diagram showing a configuration of a text buffer TBUF in the present invention, FIG. 15 is a diagram showing a configuration of a homophone buffer DOBUF in the present invention, and FIG. FIGS. 17 to 19 show an example of the likelihood calculation in the present invention, and FIGS. 20 to 27 show the operation of the character processing device of the present invention. flowchart. DISK …… External storage CPU …… Microprocessor ROM …… Read only memory RAM …… Random access memory IBUF …… Input buffer OBUF …… Output buffer DIC …… Kana-to-Kanji conversion dictionary YDIC …… Example dictionary BCTBL …… clause Candidate table CBUF1 …… clause connection matrix 1 (for clause × annex) CBUF2 …… text buffer 2 (for clause × suffix) CBUF3 …… text buffer 3 (for prefix × clause) TBUF …… text buffer DOBUF …… Homophone buffer

Front page continuation (72) Inventor Kazuyo Ikeda 3-30-2 Shimomaruko, Ota-ku, Tokyo Canon Inc. (56) Reference JP-A-1-229369 (JP, A)

Claims (3)

(57) [Claims] 1. An input unit for inputting a reading, a storage for storing a reading of a word, a notation, and learning data in association with each other, and an example of storing a set of a plurality of words having a co-occurrence relationship as an example. With respect to the reading input by the storage unit and the input unit, the storage unit and the example storage unit are referred to, the word to which the example is applied and the word with high priority represented by the learning data are given priority, and First candidate display means for determining one candidate word and displaying the notation of the first candidate word, next candidate display means for displaying notation of candidates lower than the first candidate, and next candidate display means Alternatively, the learning data of the word is updated so that the priority of the word selected by the selecting means for selecting one candidate from the candidates displayed by the first candidate displaying means and the word selected by the selecting means is improved. Learning means and Determination means for determining whether or not an example is applied to each of the selected word and each candidate word higher than the selected word; and the selection based on the result of the determination by the determination means. If the example has been applied to the word that has been applied, among the words of the upper candidates, lower the priority of the word that is determined that the example is applied, the word that is determined that the example is not applied And a suppressing means for controlling the learning data of 1. to be unchanged. 2. The suppressing means lowers the priority of each word of the upper candidates when it is judged by the judgment means that the example is not applied to the selected word. The control is performed as follows.
The character processing device described in. 3. The learning data is a 1-bit learning flag in which each word can be in a state independent of other words, and the first candidate display means gives priority to a word whose learning flag is ON, The character according to claim 1, wherein the learning unit turns on a learning flag of the selected word, and the suppressing unit lowers the priority of the word by turning off the learning flag of the word. Processing equipment.