JP2849341B2 - Dialogue processing device - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a dialogue processing apparatus, and more particularly to a dialogue processing apparatus capable of detecting the presence or absence of a topic change in a dialogue between dialogue parties.

[0002]

2. Description of the Related Art Most of the conventional topic recognition techniques are related to the topic understanding of "object-oriented dialogue". Here, the purpose-oriented dialogue includes, for example, a dialogue at a reception of a company and a dialogue in an application for participation in a conference. For such a goal-oriented dialogue, the topic is understood by describing the structure of the dialogue using, for example, a plan / goal model.

However, when the plan-goal model is used, scripts must be prepared for the number of situations. Therefore, the flexibility of the device capable of understanding the topic of the goal-oriented dialogue is lacking. Further, such a device is not applied to a "free-development type dialog" having no particular purpose, and does not understand the topic of the free-development type dialog.

[0004] On the other hand, there is a device for slightly understanding a topic for a free-development type dialogue. FIG. 9 is a diagram for explaining a topic understanding method using such a topic understanding device for a slightly free-development type dialogue.

Referring to FIG. 9, first, it is assumed that a dialogue is performed between the talkers. Thus, it is assumed that utterances are obtained in this order, such as utterance 311, utterance 313, utterance 315, utterance 317, and utterance 319. Next, the utterance 311
Is changed to text 301 which can be processed by a computer, utterance 313 is changed to text 303, utterance 315 is changed to text 305, utterance 317 is changed to text 307, and utterance 319 is changed to text 309. The text 301 includes topic candidate words 321a and 321b. The text 303 includes topic candidate words 323a, 32
3b is included. The text 305 includes topic candidate words 32
5 is included. The text 307 includes topic candidate words 327
a, 327b. The text 309 includes a topic candidate word 329.

The five utterances 311 and 3 obtained in this way are
Text 30 corresponding to 13,315,317,319
1, 303, 305, 307, 309 are stacked on the topic stack 331. Stacked text 30
1, 303, 305, 307, 309 include topic candidate words 321a, 321b, 323a, 323b, 325, 3
27a, 327b, and 329 are included. Therefore, these topic candidate words 321a, 321b, 323a, 32
If the same topic candidate word exists among 3b, 325, 327a, 327b, and 329, the topic is identified. Further, after the topic identification, a topic change is detected. In this way, the topic is understood for the freely deployable type.

[0007] However, although there is a method of understanding topics for such a free-development type dialogue, it is actually necessary to prepare a field dictionary corresponding to the content of the dialogue. Therefore, the topic understanding of the content outside the prepared field is not performed, and the topic understanding of the free-development dialogue in the true sense is not performed.

Further, since the topic stack is used for topic identification, a delay of several utterances occurs in recognition of topic change and topic identification. In the example described above, a delay of five utterances occurs. Therefore, recognition of topic change and topic identification in a state close to real time are not performed.

The above is summarized. Conventional topic understanding does not apply to topics in fields other than domain knowledge prepared for each field, whether goal-oriented conversations or freely expanding conversations. Furthermore, in the conventional free-development-type dialogue, topic identification is performed first, and then topic change is recognized. Therefore, topic change is not detected in real time.

Therefore, it is an object of the present invention to provide a dialog processing method capable of detecting a topic change in almost real time, and applicable to a topic in any field in a goal-oriented dialog and a free-evolving dialog. It is to provide a device.

[0011]

In order to solve the problems] by that dialogue processing apparatus to the invention of this is, the input means, and the morphological analysis means, topic conversion factor
Extracting means, topic candidate word extracting means, and arithmetic processing means
Prepare. The input means inputs the utterance of the topic,
Convert utterances into sentences. The morphological analysis means is an input means
Morphologically analyzes the sentence given by. Topic change factor extraction
The output means is selected from words given by the morphological analysis means.
Extract topic conversion factor words that can be the cause of topic conversion
You. Topic candidate word extraction means is provided by the morphological analysis means.
Topics that can be topic words that represent topics from among the words
Extract complements. The arithmetic processing means is provided by a morphological analysis means.
Number of occurrences and occurrences of topic candidate words in given words
Topics whose topic candidate words contribute to topic switching based on intervals
Calculate the conversion contribution index, and convert the topic conversion contribution index and the topic conversion
Detect topic change based on factors.

Preferably, the input means includes a text input device for expressing a sentence in a text format.

[0013] Preferably, said input means including a speaker monitoring device that monitors one talk <br/>'s is speaking. Up
The notation processing means uses a topic conversion contribution index and a topic conversion factor word.
In addition to the above, based on the monitoring result of the speaker monitoring device ,
Detect conversion .

[0014] Preferably, said input means including a time monitoring device for monitoring the time from the utterance <br/> start time of the speaker to the speech ending time. The arithmetic processing means is a topic conversion contribution index.
And the topic conversion factor words, and the monitoring results of the time monitoring device
Detect topic shift based on

[0015] Preferably, the morphological analysis means has a predetermined word dictionary, to form <br/> state element analyzes the sentence using the word dictionary.

[0016] Preferably, the morphological analysis means has a predetermined synonym dictionary, and uses a synonym dictionary to form a synonym.
Extract synonyms from words obtained by elementary analysis
To create a synonym list.

Preferably, the morphological analysis means has a predetermined contrast word dictionary, and uses a contrast word dictionary to form
Extract a comparison word from the words obtained by elementary analysis
To create a specific word list.

[0018] Preferably, in the dialog processing system Hasa, et al.,
A topic word identification unit is provided. Topic word identification means performs arithmetic processing
When means detects the conversion topic, we identify <br/> topic words from the topic candidate word.

Preferably, the morphological analysis means has a predetermined synonym dictionary, and forms a synonym using the synonym dictionary.
Extract synonyms from words obtained by elementary analysis
Create a synonym list. The above-mentioned topic word identification means is a synonym.
Identify topic words based on the list .

More preferably, the morphological analysis means has a predetermined contrast word dictionary , and extracts a contrast word from words obtained by morphological analysis using the contrast word dictionary.
To create a contrasting word list. The topic word identification means identifies a topic word based on a comparison word list .

[0021]

[Action] by that dialogue processing apparatus to the invention of this is, because it does not have a configuration that is dependent on the contents of the dialogue between the interlocutors, can be applied to the topic of any of the fields, further, the number speech to the topic identification Since there is no need to accumulate, it is possible to detect the presence / absence of a topic change almost in real time.

Further, by using the text input device as an input device, it can be converted into a computer processable text format.

Further, because it includes a speaker monitoring device as input means, able to detect the occurrence of the topic transformed by the speaker specified, it is possible to improve the topic transformed detection accuracy.

Further, because it includes a time monitoring device as input means, able to detect the occurrence of the topic transformed by a blank period between utterances, it is possible to improve the topic transformed detection accuracy.

[0025] In addition, since the morphological analysis means for analyzing the speech content by the word of the part of speech, it can be extracted the part of speech another topic candidate of the word.

[0026] In addition, since the analysis by the synonym of the speech content, it can be extracted the topic conversion factors obtained from synonyms.

Further, since analyzes the utterance by comparing words, by statistical processing of the analysis result obtained from the comparison word, it is possible to obtain a statistical processing information about the comparison word.

[0028] In addition, because to identify topic words based on the topic candidate word according to the evaluation result for the detection of whether or not the topic conversion exists, almost perform the topic identified close to real time.

Also , the utterance contents are analyzed with synonyms, and synonyms are analyzed.
Because to identify the topic word by, it is possible to improve the topic word same Teisei degree.

Also , the utterance contents are analyzed by contrast words,
Since identifying topic words based on the comparison word, it is possible to improve the topic language same Teisei <br/> degree.

[0031]

FIG. 1 is a schematic block diagram of a dialog processing apparatus according to an embodiment of the present invention.

Referring to FIG. 1, a dialogue processing apparatus 1 for processing a dialogue between people includes a dialogue input unit 3, a morphological analysis unit 5, a topic conversion factor extraction unit 7, and a topic candidate word extraction. A section 9, an analysis information statistical processing section 11, a topic topic conversion contribution index calculation section 13, and a topic conversion likelihood evaluation section 15.

The dialogue input unit 3 inputs utterances of people who are in a dialogue. The dialogue input unit 3 includes a text input device 19. Text input device 19
Input the utterance contents of the people into the apparatus body in a text format. For example, when the text input device 19 is constituted by a speech recognition device, speech recognition is performed for speech uttered by a speaker among people, and the speech content is converted into a text format to convert the speech content into a text format. Output to In addition, a keyboard or the like may be used for the text input device 19, and the uttered content of the speaker may be converted into a text format by the used keyboard and provided to the morphological analysis unit 5. Further, the text input device 19 may input the utterance content of the dialogue between the people in sign language or Braille.

The morphological analysis section 5 has a word dictionary 21. Then, using the word dictionary 21, the morphological analysis unit 5
Decomposes the utterance sentence converted into text by the dialog input unit 3 into words. After that, the morphological analysis unit 5 determines the part of speech of the decomposed word. Then, the morphological analysis unit 5 gives the word part-of-speech list to the topic conversion factor extraction unit 7, the topic candidate word extraction unit 9, and the analysis information statistical processing unit 11.

The topic conversion factor extraction unit 7 has a predetermined topic conversion factor rule 22 as knowledge. Then, the conversion factor rule 22 is applied to the part of speech of the word determined by the morphological analysis unit 5 to detect a word that easily causes topic conversion. For example, such words may include crew words (instead of, by the way, etc.), question sentences, or words or phrases that identify the other party. Then, the topic conversion factor extraction unit 7 gives, for example, the presence or absence of a crew word to the topic conversion likelihood evaluation unit 15.

The topic candidate word extracting section 9 has the topicing rule 23 as knowledge. Then, the topicalization rule 23 is applied to the part of speech of the word determined by the morphological analysis unit 5, and a syntax applicable to the rule is detected from the utterance. Then, by performing the detection, a topic candidate indicating the syntax is obtained, and a topic candidate word list is output to the analysis information statistical processing unit 11.

The analysis information statistical processing section 11 includes a morphological analysis section 5, a topic candidate word extraction section 9, a topic conversion contribution number calculation section 13 for a word to be described later, and a topic conversion likelihood evaluation section 15 to be described later. Statistical processing is performed based on the obtained information. The conversion contribution index of each topic candidate word obtained after the statistical processing and the number / presence of the topic candidate words are determined by the topic conversion likelihood evaluation unit 1.
Give 5

The topic conversion contribution index calculation unit 13 for the word, based on the number of appearances of the input noun and the elapsed time since the word was used most recently, obtained by the analysis information statistical processing unit 11, Then, the topic conversion contribution index of the word is calculated and output to the analysis information statistical processing unit 11.

The topic conversion likelihood evaluation unit 15 includes a number / presence / absence of topic candidate words obtained by performing statistical processing by the analysis information statistical processing unit 11, a conversion contribution index of each topic candidate word, and a topic conversion factor extraction unit. Based on, for example, the presence or absence of a crew word obtained in step 7, it is evaluated whether a topic change has occurred in the current utterance. Then, the presence or absence 25 of topic change is obtained from the evaluation result. Further, whether or not the topic has been changed is also given to the analysis information statistical processing unit 11.

Next, the operation will be briefly described. First, the utterance input to the dialog input unit 3 is converted by the text input device 19 into a format (text) that can be processed by a computer. Then, the converted text is provided to the morphological analyzer 5.

Next, the morphological analysis unit 5 analyzes the input utterance using the word dictionary 21 and decomposes the utterance into words, and determines the part of speech of each word. The word part-of-speech list, which is the analysis result, is provided to the topic conversion factor extraction unit 7, the topic candidate word extraction unit 9, and the analysis information statistical processing unit 11.

Next, the topic conversion factor extraction unit 7 checks according to the topic conversion factor rule 22 whether or not a word (phrase) that prompts a topic conversion appears in the analysis result of the morphological analysis unit 5. When such a word (phrase) is detected, the topic conversion factor extraction unit 7 notifies the analysis information statistical processing unit 11.

The topic candidate word extraction unit 9 applies the topicization rule 23 to the analysis result of the morphological analysis unit 5, and if a syntax that matches the rule is found, extracts the topic candidate word indicated by the syntax. To the analysis information statistical processing unit 11.

The analysis information statistical processing section 11 is based on information input from the dialogue input section 3, morphological analysis section 5, topic conversion factor extraction section 7, topic candidate extraction section 9, and converts each quantity into a word / sentence / utterance unit. Take statistics. In addition, the noun and the topic candidate word are input from each of the morphological analysis unit 5 and the topic candidate word extraction unit 9, and the number of appearances thereafter, and if not the first appearance of the word, the elapsed time since the last appearance is indicated by the topic of the word. The conversion contribution index calculation unit 13 is provided. Then, the calculation result of the topic conversion contribution index calculation unit 13 of the word is received, and the value is also statistically processed.

The word topic conversion contribution index calculation unit 13 uses the number of appearances of a word input from the analysis information statistical processing unit 11 and the elapsed time since the last appearance to convert the word into a topic conversion occurrence. Is calculated, and the result is given to the analysis information statistical processing unit 11.

The topic conversion likelihood evaluation unit 15 receives various statistical information such as the number / presence of topic conversion candidate words and the conversion contribution index of each topic candidate word from the analysis information statistical processing unit 11, and a topic conversion factor extraction unit. Based on a topic conversion factor, such as the presence or absence of a crew word, given from 7, whether or not a topic change has occurred in the current utterance is evaluated by a predetermined evaluation method. The evaluation result is sent to the analysis information statistical processing unit 1
Given to one. Further, when it is determined that the topic has changed, the topic conversion likelihood evaluating unit 15 notifies the user, for example, of the fact.

With such an apparatus, there is provided an interaction processing apparatus for detecting the presence / absence of a topic change applicable to not only goal-oriented dialogue but also free-development-type dialogue having no particular purpose. Also, since there is no part in the processing that depends on the content of the dialogue such as a dictionary corresponding to the model or topic field, there is provided an apparatus that does not need to be changed for topics in any field. This is because the structure of the dialogue, the field of the content, and the like are not assumed at all, and the detection of the change of the topic is performed based on the heuristic rules obtained from the actual person. Further, topic switching and topic detection are performed without time delay. That is, as in the conventional example, the topic stack is not used for the recognition and topic identification of topic conversion, and the change is grasped by detecting the difference between the current utterance and the utterance up to that point. Near real time detection of topic change is performed.

FIG. 2 is a schematic block diagram showing a dialog processing apparatus according to another embodiment of the present invention.

Referring to FIG. 2, a dialogue processing apparatus 101 of this embodiment includes a dialogue input unit 103 and a morphological analyzer 105.
And topic conversion factor extraction unit 107 and topic candidate word extraction unit 1
09, an analysis information statistical processing unit 111, a word topic conversion contribution index calculation unit 113, and a topic conversion likelihood evaluation unit 115
And a topic word identification unit 117.

The parts different from the embodiment shown in FIG. 1 will be particularly described. The dialogue input unit 103 includes a speaker monitoring device 119 and an utterance transition time monitoring device 121 in addition to the text input device 19. The utterances of people are input to the speaker monitor 119, and the speaker monitor 119 monitors who the current speaker is, and indicates the speaker I indicating the monitoring result.
D is given to the analysis information statistical processing unit 111. The utterance transition time monitoring device 121 receives input of people's utterances, and the utterance transition time monitoring device 121 acquires time information related to the utterance act. Such time information includes the start time of the current utterance and the end time of the current utterance. These pieces of information are provided to the analysis information statistical processing unit 111.

Next, the morphological analysis unit 105 sends the word dictionary 2
1 and a comparison word dictionary 123 and a synonym dictionary 125. Therefore, the morphological analysis unit 105 detects a contrast word (antonym) using the contrast word dictionary 123. Similarly, the morphological analysis unit 105 uses the synonym dictionary 125 to detect synonyms. Then, the comparison word list obtained by the comparison word dictionary 123 of the morphological analysis unit 105 is provided to the analysis information statistical processing unit 111 and a topic identification unit 117 described later. Further, the synonym dictionary 1 of the morphological analysis unit 105
The synonym list obtained in 25 is the topic conversion factor extraction unit 1
07 and the topic word identification unit 117.

The topic conversion factor extraction unit 107 extracts the following topic conversion factors using the topic conversion factor rule 22 and provides the same to the topic conversion likelihood evaluation unit 115. That is, not only the presence or absence of the crew word shown in the embodiment of FIG. 1, but also the question sentence, the presence / absence of the partner specific word, the number / presence of synonyms, the number / presence of descriptive words, the number / presence of connectives This is given to the topic conversion likelihood evaluation unit 115. The presence / absence of a crew word, the presence or absence of a question, the presence / absence of a partner specific word, the number / presence of descriptive words, and the number / presence of connectives are obtained from the word part-of-speech list provided by the word dictionary 21. The number / presence of synonyms is obtained from a synonym list obtained from the synonym dictionary 125.

The topic candidate word extraction unit 109 is almost the same as the topic candidate word extraction unit 9 in FIG. 1, and the obtained topic candidate word list is sent to the topic word identification unit 117 in addition to the analysis information statistical processing unit 111. Also give.

Next, the analysis information statistical processing unit 111
The statistical information processing unit 11 obtains more statistical processing results than the analysis information statistical processing unit 11 and supplies it to the topic conversion likelihood evaluation unit 115. First, in accordance with the given word part-of-speech list, the analysis information statistical processing unit 111 determines not only the number / presence of topic candidate words and the conversion contribution index of each topic candidate word, but also the number / presence of first appearance words, first appearance word and topic candidate. The number / presence / non-existence of words and the conversion contribution index of each first word are given to the topic conversion likelihood evaluation unit 115. Also, with the addition of the speaker monitoring device 119 and the utterance transition time monitoring device 121 to the dialogue input unit 103 and the addition of the contrast word dictionary 123 to the morphological analysis unit 105, the analysis information statistical processing unit 111 Such statistical information is obtained and given to the topic change likelihood evaluator 115.

First, the morphological analysis unit 105 sends the comparison word dictionary 1
With the addition of 23, information on the number / presence or absence of pre- sentence comparison words is obtained, and is provided to the topic conversion likelihood evaluation unit 115. With the addition of the speaker monitoring device 119 and the utterance transition time monitoring device 121 to the dialog input unit 103, the analysis information statistical processing unit 111 determines whether or not the speaker has been changed, the elapsed time since the previous topic change, the current utterance. Time since the previous utterance of the person,
Information about the utterance time of the current utterance and the time from the end of the previous utterance to the start of the current utterance is obtained, and the obtained information is provided to the topic conversion likelihood evaluation unit 115.

Next, the word topic conversion contribution index calculation unit 11
3 is that the analysis information statistical processing unit 111 gives the topic conversion likelihood evaluation unit 115 not only the conversion contribution index of each topic candidate but also the conversion contribution index of each first word. It is calculated and given to the analysis information statistical processing unit 111.

Next, the topic conversion likelihood evaluation unit 115 evaluates the topic conversion factor provided from the topic conversion factor extraction unit 107 and the information of the statistical processing result provided from the analysis information statistical processing unit 111. Determine if the topic has changed.

Next, the topic word identification unit 117 is obtained by the comparison word dictionary 123 and the synonym dictionary 125 of the morphological analysis unit 105 in response to the topic conversion likelihood evaluation unit 115 evaluating that there is topic conversion. A topic word is identified according to the compared word list and the synonym list and the topic candidate word list obtained by the topic candidate word extraction unit 109, and the topic 1 is identified.
27 is provided to the user.

In the dialogue processing apparatus 101 having such a new configuration, it is possible to detect a topic change almost in real time, as in the embodiment shown in FIG. Further, the following effects can be obtained by the new configuration.

First, the speaker monitoring device 119 makes it possible to apply the rule of occurrence of topic change by speaker designation,
Conversion detection accuracy is improved. Next, the utterance transition time monitoring device 121 makes it possible to apply a rule of occurrence of a topic change based on a vacant manner between utterances, thereby improving the accuracy of change detection. Next, the synonym dictionary 125 and the comparison word dictionary 1
The use of 23 makes it possible to apply the rule of topic conversion using synonyms and contrast words, and improves conversion detection accuracy.
Next, with the addition of the topic word identification unit 117, it is possible to extract a word representing a topic that has been changed when a topic change is detected.

Note that the result identified by the topic word identification unit 117 may be given to the analysis information statistical processing unit 111. If the number of topicalizations indicating how many times the given topic word contributed to topicalization is statistically processed by the analysis information statistical processing unit 111, the topic conversion detection accuracy is further improved.

Next, the operation of the present apparatus that produces such effects will be described in detail. First, the dialogue input unit 103
The text input device 19 converts the input utterance into a format (text) that can be processed by a computer, and supplies the converted utterance to the contrast word dictionary 123, the word dictionary 21, and the synonym dictionary 125 of the morphological analysis unit 105. Further, the current speaker is monitored by the speaker monitoring device 119. Then, the utterance transition time monitoring device 121 detects transition time information relating to the utterance act. Then, the detection result (speaker id) output from the speaker monitoring device 119 and the detection result of the speaker transition time monitoring device are provided to the analysis information statistical processing unit 111.

The morphological analysis device 105 operates in the synonym dictionary 12
5 and the comparison word dictionary 123 are used to detect synonyms and comparison words. The synonym list that is the result of the detection is provided to the topic conversion factor extraction unit 107 and the topic word identification unit 117, and the comparison word list is provided to the analysis information statistical processing unit 111 and the topic word identification unit 117.

The topic conversion factor extraction unit 107 detects the following information as a topic conversion factor according to the word part of speech list given from the word dictionary 21 of the morphological analysis unit 105 as shown in the embodiment of FIG. The following information is the first
Is the presence or absence of a crew word, the second is whether or not it is a question sentence, the third is the presence or absence of a partner specific word, the fourth is the number / presence of synonyms (synonyms), and the fifth is The sixth is the number / presence of connectives.

The first crew word is generally a word (phrase) that explicitly prompts a change in topic, and examples thereof include, but not limited to, the next one, the second, and the like. The presence or absence of the crew word is obtained by finding a word corresponding to the crew word described in the topic conversion factor rule 22 from the word part of speech list provided to the topic conversion factor extraction unit 107.

The second question sentence is information obtained by an experimental heuristic rule that, when a question sentence appears, it is easily recognized as a topic change. Whether the sentence is a question sentence is determined by the topic conversion factor extraction unit 10.
7 is determined by determining from the word part-of-speech list given to 7 whether or not it corresponds to the question sentence described in the topic conversion factor rule 22.

The presence / absence of the third partner specific word is information obtained by a heuristic rule based on an experiment, which is easily recognized as a topic change when talking to a specific person participating in the dialogue. For example, a participant's name and personal pronoun are mentioned. The presence / absence of this partner specific word is obtained by extracting a word corresponding to the partner specific word described in the topic conversion factor rule 22 from the word part-of-speech list provided to the topic conversion factor extraction unit 107.

The number / presence of the fourth synonym is that if synonyms (synonyms) appear in one utterance or one sentence, those words are recognized as being emphasized (that is, words indicating a topic). This is information obtained by a heuristic rule based on experimentation. The number / presence of the synonyms is obtained by extracting the synonyms from the synonym list provided to the topic conversion factor extraction unit 107.

The number / presence of the fifth directive is information obtained by a heuristic rule based on an experiment that it is easy to recognize that the utterance in which the directive relating to the previous utterance appears is a continuing topic. The number / presence / non-presence of the descriptive words is obtained by extracting the descriptive words from the word part-of-speech list provided to the topic conversion factor extraction unit 107.

The number / presence / absence of the sixth connective is information obtained by a heuristic rule based on an experiment that an utterance in which a connective to the previous utterance appears is likely to be recognized as a continuing topic. Number of this conjunction /
The presence / absence is obtained by extracting a conjunction from the word part-of-speech list provided to the topic conversion factor extraction unit 107.

Next, the topic candidate word extraction unit 109 extracts topic candidate words according to the topicization rule 23. The topic rules 23 include the following.

First, the noun of the emphasized sentence is set as a topic candidate word. Examples of the emphasized sentence include a noun + auxiliary verb "da" or a noun + auxiliary verb "de". Secondly, nouns that are absent are considered topic candidate words. For example, a noun in the case of a noun + phrase "." Syntax is a topic candidate word.

Third, a noun in the case of the syntax of noun + case particle + proposal particle is regarded as a topic candidate word. Fourth, a noun in the case of a noun + proposal particle structure is a topic candidate word. Fifth
Is a topic candidate word in the case of the syntax of noun + accessory particle + proposed particle. Sixth, a noun in the case of a noun + collection particle construction is a topic candidate word. The seventh is noun + case particle +
A noun in the case of the collection particle is regarded as a topic candidate word.

Eighth, nouns in the case of existence sentences are topic candidate words. Examples of existence sentences include noun + case particle "ga" + a (main verb stem), noun + case particle "ga" + i (main verb stem), or noun + case particle "ga" + a (main verb) Stem) syntax.

Ninthly, nouns in the case of a noun + adjunctive + collective particle syntax are set as topic candidate words. Tenth, a noun in the case of a noun + adjunct particle “toka” syntax is set as a topic candidate word.

The analysis information statistical processing unit 111 to which such topic candidate words are also input creates a management table as described below to manage information.

FIG. 3 is a first diagram illustrating such management, FIG. 4 is a second diagram illustrating such management, and FIG. 5 is a diagram illustrating such management. It is the 3rd figure for explaining management.

Referring to FIGS. 3 to 5, the analysis information statistical processing unit 111 manages the information in the dialog management table 201 shown in FIG. The dialog management table 201 includes current meta information 203, speaker information 205, language information 207, and context information 209. The current meta 203 includes an utterance number, a sentence number, an in-utterance sentence number, and an utterance ID.

The speaker information 205 is shown in an array with the speaker ID as a subscript. For example, information with a speaker ID of 0 includes a recently uttered sentence number 211, a recently uttered time 213,
The total number of utterances 215 and the name 217 are included as information. Such speaker information will be specifically described later.

The language information 207 includes a noun element list 219
Is included. As shown in FIG. 4, the noun element list 219 includes a headword 221, an appearance frequency 223, an ENTRY (comparison word) 225, a topic frequency 227, and a topic recognition frequency 2
29, latest sentence number 231 and Left (left) 23
3 and Right (right) 235 are included. The headword 221 is normalized by thesaurus synonym information. ENTRY (comparison word) 225 has been incorporated to refer to the opposite word. And the noun element list 21
9, a noun element list 237 is further recorded in Left (left) 233 of No. 9, and Right of the noun element list 219 is recorded.
(Right) 235 records a noun element list 239. Thus, a tree structure is created by recording the left and right noun element lists in each noun element list.
This facilitates the search. Such a noun element list will be specifically described later.

Next, returning to the dialog management table 201 of FIG.
The context information 209 of the dialog management table 201 includes current information 241 and a short-term utterance memory (FIFO) 243. The current information 241 includes a recent conversion sentence number 245.
And the current topic 247.

The current information other than the current information 241 is included in the short-term speech memory (FIFO) 243, and the short-term speech memory (FIFO) 243 is formed by five pieces of speech information. As shown in FIG. 5, the one utterance information 249 includes an utterance number 251, a speaker number 253, a sentence information list 255, and a previous utterance information pointer 257. The sentence information 255 includes a sentence number 259, an in-speech sentence number 261, various indexes (bit representation) 263, a title sentence flag 265, a chart (morphological information and the like) 267, and NEXT sentence information 269. Including. Further, the various indexes (bit representation) 263 include information 271 classified according to position, division, and content. Such utterance information 249 and sentence information 255 will be described later.

Next, a specific description will be given with reference to Tables 1 to 4.

[0084]

[Table 1]

[0085]

[Table 2]

[0086]

[Table 3]

[0087]

[Table 4]

It is assumed that a dialog management table as shown in Table 1 has been obtained. That is, it is assumed that the utterance number is 13, the sentence number is 23, the in-speech sentence number is 1, and the information of the speaker ID is 0. This information indicates the following state. That is, the speaker ID. The state where a person with 0 (name is black iron) is speaking is 13 from a certain standard.
This indicates that the utterance is the 23rd sentence counted from the same criterion, and is the first sentence in the 13th utterance.

Further, according to the speaker information shown in Table 2, the recent sentence that the person who uttered before the person whose speaker ID is currently uttering 0 is the person whose speaker ID is 2 is indicated. It can be seen from the value indicated by the number. In addition, if the total number of utterances of all the speaker IDs 0, 1, and 2 is added, the utterance number in the dialog management table in Table 1 is 13 because it is 12. The information for each speaker ID in the speaker information in Table 2 corresponds to the speaker information 205 in FIG.

Next, the linguistic information (noun element list) in the dialog management table in Table 1 is information as specifically shown in Table 3. Information corresponding to each headword in Table 3 corresponds to each of the noun element lists 219, 237, and 239 in FIG. In particular, the number of topics is determined by the topic candidate word extraction unit 10.
9 and the topic recognition frequency is calculated by the topic word identification unit 11
7 obtained.

By the way, although the word evaluation value (%) is described in Table 3, there is no such description in the noun element list of FIG. This is the word topic conversion contribution index calculation unit 1
This is to indicate that the topic conversion contribution index obtained in 13 may or may not be fed back to the analysis information statistical processing unit 111. That is, in the case of the noun element list obtained in Table 3, feedback of the topic conversion contribution index from the word topic conversion contribution index calculation unit 113 is performed, and in the case of the noun element list shown in FIG. The topic conversion contribution index from the word topic conversion contribution index calculation unit 113 is not fed back, and the topic conversion likelihood evaluation unit 1
15 directly. Therefore, when the output of the word topic conversion contribution index calculation unit 113 is not fed back to the analysis information statistical processing unit 111, the conversion contribution index of each topic candidate word and the conversion contribution index of each first appearance word are the topic conversion conversion index of the word. It is directly input from the operation unit 113 to the topic conversion likelihood evaluation unit 115. When the feedback of the topic conversion contribution index is required, the function for calculating the topic conversion contribution index is expressed by a recurrence formula, and the value of the previous topic conversion contribution index is used to calculate the current topic conversion contribution index. Such cases may be necessary.

Next, the sentence information of the dialog management table is information as specifically shown in Table 4. The context information shown in Table 4 is
It is roughly divided into utterance information and sentence information.

The utterance information includes an utterance number represented by U, a start time and an end time of each utterance, a sentence number in which the topic represented by the word is changed, and a wadai.
The utterance number U = 9 has a start time of 14:30:05 and an end time of 14:30:10. Utterance number U
The start time of = 10 is 14:30:11, and the end time is 14:30:20. The start time of the utterance number U = 11 is 14:30:22, and the end time is 14:30:43. The start time of the utterance number U = 12 is 1
4:30:44 and end time is 14:30:46
Seconds. And the start time of the utterance number U = 13 is 14
It is 30:30 and 51 seconds. The latest input sentence of the utterance information has an utterance number of 13, a topic change sentence number of 23, and two topics, and corresponds to the dialog management table in Table 1.

The sentence information includes meta information, various indexes (bit representation), and other information. Meta information includes
In the table, a sentence number represented by S and a sentence number in an utterance represented by SU are shown. The sentence number which is the latest input sentence is 23, and the sentence number in the utterance is 1, which corresponds to the dialog management table in Table 1.

The following indices (bit expressions) include the following factors:
The factors in the previous section, conversion factors, and judgment are included. Subsequent factors include:
Information indicating whether or not the question is represented by Q in the table is shown. The factors in the previous section include contact, finger,
As shown by a pair, information indicating a conjunction, a descriptive word, a synonym, and a contrast word is shown. The conversion factors include the presence of the first word, presence of the crew word, presence of the word that is the first word and a topic candidate word, and the partner of the dialogue, which are indicated by the first, kuru, first proposal, phase, and title, respectively. Information indicating presence / absence of a word and presence / absence of a topic candidate word is shown.

In the judgment, information indicating whether or not there is a change in the topic indicated in the table is shown. If the utterance transition time monitoring device 121 is present, the start time of the utterance where the topic change occurred is recorded here.

As described above, the context information shown in Table 4, for example, information in which the sentence number of the meta information is 16 to 21 corresponds to the utterance information 249 in FIG. 5, and each line of the context information corresponds to the context in FIG. This corresponds to the information 255.

The analysis information statistical processing unit using FIGS. 3 to 5 and Tables 1 to 4 also manages information that does not need to be subjected to statistical processing, but as shown in FIG. Only the information output from the unit 111 may be managed.

As described above, the analysis information statistical processing unit 111
Performs statistical processing on various information. Next, in accordance with the appearance frequency of the input word obtained as a result of such statistical processing and the elapsed time since the latest appearance, the word topic conversion contribution index calculation unit 113 obtains a topic conversion contribution index. If the appearance frequency of the input word is represented by f and the elapsed time since the most recent appearance is represented by t, the contribution index h is obtained by a function as shown in equation (1). Further, g (t) on the right side of Expression (1) is given by Expression (2).

H =: e− ^{g (t) f} (1) g (t) = (1 + kt) ⁻¹ (2) where k is a count for adjusting the weight of time. Such a function is a function as shown in FIG.

In the graph of FIG. 6, one axis of the plane coordinates is t (elapsed time), the other axis is f (frequency of appearance), and the vertical axis is h (evaluation value). Have been. As is clear from FIG. 6, the longer the elapsed time from the previous appearance to the current appearance, the larger the value of the evaluation value. On the other hand, as the number of appearances increases, the value of the evaluation value decreases.

In the expression (1), the value of f in the case of the first word is 0. That is, the first word is set so that the evaluation value (h) is always 1.

As described above, the contribution rate is high when a certain word first appears or after a long time,
Conversely, by using a function that reduces the contribution ratio when it frequently appears or when it appears just before, it is possible to evaluate the topic change.

FIG. 7 is a graph showing the result of an experiment on what value the function as shown in the equation (1) actually shows. In FIG. 7, the horizontal axis indicates the appearance time (t), and the vertical axis indicates the appearance word topic conversion index (h). However, the number of characters is used as the appearance time (t) instead of the actual elapsed time, and k is set to 0.01. further,
The graph shown in FIG. 7 shows the contribution index of the word "male" in a certain talk article.

Referring to FIG. 7, since the word "male" appears frequently up to t = 2000, the contribution index decreases with time. However, t = 2000 to 50
During the period between 00 and 00, t = 500
The contribution index when appearing near zero is again highly evaluated. That is, at around t = 1500, topic switching by the word “male” is not so much promoted, but at t = 500
The graph shown in FIG. 7 indicates that the topic is likely to be changed by "male" when reappearing near zero.

Next, the topic conversion likelihood evaluation section 115 will be described. The topic conversion likelihood evaluation unit 115 is configured by a three-layer neural network. The data given to the input layer and the possible values of each data are as follows.

The first is the presence or absence of a crew word. This crew word takes a value of 0 when there is no crew word, and takes a value of 1 when there is a crew word. The presence or absence of such a crew word is given to the topic conversion likelihood evaluation unit 115 by the topic conversion factor extraction unit 107 as described above.

The second is whether it is a question sentence or not. If the question is a question, take 1; otherwise, take 0. Whether or not such a question sentence is given by the topic conversion factor extraction unit 107.

The third is the presence / absence of a partner specific word. If there is no word identifying the conversation partner, 0 is taken, and in some cases, 1 is taken. The presence / absence of a word specifying such a conversation partner is given by the topic conversion factor extraction unit 107.

The fourth is the number / presence of synonyms. 0 if a synonym (synonym) is not included in one utterance or sentence
And if it is included, take 1 or its value. The number of such synonyms (synonym) included in one utterance or one sentence is obtained by counting the number of synonyms from the synonym list obtained in the synonym dictionary 125.

Fifth is the number / presence / non-presence of the descriptive words. If there is no descriptive word, it takes 0, and if it does, it takes 1 or its value. The number of such descriptive words is determined by the word dictionary 21
Are obtained by counting descriptive words from the word part-of-speech list obtained from.

The sixth is the number / presence of connectives. If there is no conjunction, it takes 0, and if there is, it takes 1 or its number. The number of such conjunctions can be found in the word dictionary 21
Is obtained by extracting the conjunction of the word part-of-speech list obtained from.

The seventh is the number / presence of first words. If there is no first word, it takes 0, and if there is, it takes 1 or its value. The presence or absence and the number of such first words are obtained by statistically processing the word part-of-speech list provided from the word dictionary 21 by the analysis information statistical processing unit 11.

Eighth is the number / presence of topic candidate words. If there is no topic candidate word, 0 is taken, and if there is, 1 or the value of the number is taken. The number and presence / absence of topic candidate words are obtained by extracting topic candidate words from the topic candidate word list obtained by the topic candidate word extraction unit 109.

The ninth is the presence / absence / number of words that are new words and topic candidate words. When there is no word that is the first word and topic candidate word, 0 is taken, and in some cases, 1 or a value of the number is taken. The presence / absence and the number of such words that are first words and topic candidates are obtained from the number / presence of first words and the number / presence of topic candidate words.

The tenth is the number / presence / absence of words compared with words included in the preceding sentence or the previous utterance. If there is no contrast word of the word included in the previous sentence or previous utterance, 0 is taken, and if there is, there is 1
Or take the value of that number. The number and presence / absence of such words in the previous sentence or previous utterance are determined by comparing the information obtained by statistical processing from the word part-of-speech list with the comparative word list output from the current utterance. The word included in the contrast word list is obtained by checking whether or not the word appears in the previous utterance.

Eleventh is the conversion contribution index of each first word. This topic conversion contribution index takes a real number between 0 and 1, and takes 0 when there is no first word.

The twelfth is a conversion contribution index of each topic candidate word. This topic conversion contribution index takes a real number from 0 to 1 and takes 0 when there is no candidate word.

The thirteenth is whether or not the speaker has been changed. It is obtained from the speaker ID obtained by the speaker monitoring device 119 and the statistical information statistically processed by the analysis information statistical processing unit 11. If the speaker has changed, take 1; if not, take 0. Note that whether or not this speaker has been changed is normally always 1 if the processing is performed in units of speech.

Fourteenth is the elapsed time since the previous topic change. The elapsed time from the topic change also takes a real number greater than zero. This is because when the topic change likelihood evaluation unit 115 detects a change, the utterance time of the previous topic change detection utterance can be obtained from the utterance start and end times from the utterance transition time monitoring device 121 from the statistical information. The utterance start time and end time of the utterance are calculated, and are obtained based on the elapsed time therebetween.

Fifteenth is the time from the end of the previous utterance of the current speaker. This value takes a real number of 0 or more. The start time of the previous utterance by the speaker having the same speaker ID as that of the current utterance can be obtained from the statistical information, and the difference from the start time of the current utterance is statistically processed by the analysis information statistical processing unit 11. Gives the elapsed time.

The sixteenth is the utterance time of the current utterance. The utterance time of the current utterance is obtained from the difference between the start time of the current utterance and the end time of the current utterance provided from the utterance transition time monitoring device 121.

The seventeenth is the time from the end of the previous utterance to the start of the current utterance. This time corresponds to the utterance transition time monitoring device 12.
1 is obtained from the difference between the start time of the current utterance given from 1 and the end time of the previous utterance recorded as statistical information.

When the above values are given to the input layer,
The output layer outputs the possibility of topic change as a real number.
Finally, the output value is truncated to determine whether or not there is a change. As described above, the information used to determine the topic change is basically obtained from the current utterance. Various elapsed time information,
The past utterance information is used for the word appearance frequency and contrast word detection, but is different from the topic identification method using a conventional topic stack. In other words, such past data is not used to retroactively recognize topic identification / conversion, but is used only for determining whether or not a conversion has occurred in the current utterance. This does not impair the real-time performance of the device.

The evaluation of the degree of topic change does not necessarily require the use of a neural network as in this embodiment. For example, if an evaluation function based on any appropriate polynomial is defined, it may be used. However, the number of elements used for determination is extremely large as described above, and it is difficult to weight each element. Therefore, it is generally extremely difficult to obtain some evaluation addend from the result of the subjective evaluation experiment. Seems difficult. Therefore, if the result obtained from the subjective evaluation experiment is given as teacher data using a neural network and learning is performed, a function corresponding to the evaluation function can be obtained relatively easily. For this purpose, a neural network is used in this embodiment.

The learning is performed as follows. First,
Ask a subject to roughly read a dialogue sentence. And
Ask them to check where they think there was a change in topic. From the results obtained in this way, the utterance or sentence determined by two or more subjects to be changed is determined to be the utterance or sentence having the topic change.

Next, the above data obtained from each utterance or sentence is input to the input layer of the neural network, and 1 is output if there is a topic change in the utterance or sentence, and 0 if not. Given to. Then, learning is performed by back propagation. Such learning is sequentially repeated from the beginning to the end of the dialog, and if necessary, learning is performed a plurality of times for the same conversation. As a result, the neural network is gradually learned so as to be appropriately evaluated. Alternatively, teacher data is generated using a function such as sin (xπ / 2m) (where x is the number of subjects who have detected a topic change and m is the total number of subjects). There is also a possible method. If such a function is used, the conversion likelihood of the teacher data is lower when the total number of subjects is small, and conversely, when there are more than a certain number of detectors, the conversion likelihood does not need to be detected by all members. The degree can be set to almost 1, and there is a possibility of more realistic learning.

In the embodiment, learning is performed on the part where two or more examinees have recognized the conversion as the conversion.
However, besides, for example, a method of learning using the ratio of how many of the five persons recognized that there is a change can be considered.
For example, for a part where three persons have changed in a subjective evaluation experiment by five persons, the teacher data given to the output layer is set to 0.
6 may be used.

According to such a method, it is possible to evaluate whether or not there has been a topic change without time delay.

Next, the topic word identification unit 117 will be described. The topic word identification unit 117 includes a topic conversion likelihood evaluation unit 115
When it is notified that there is a change in topic from, words that are regarded as topics are extracted from topic candidate words included in the current utterance provided from the topic candidate word extraction unit 109 and presented to the user. As a method of extracting a word regarded as a topic from the topic candidate words, the following method can be used.

FIG. 8 is a diagram for explaining various methods for extracting words regarded as topics from such topic candidate words. In particular, FIGS. 8A to 8E show FIGS.
As shown in FIG. 5, the following five methods are shown.

The first method is, as shown in FIG. 8A, a method in which all topic candidate words are regarded as topic candidate words and presented to the user.

Second, as shown in FIG. 8B, if there is a first candidate word among topic candidate words, it is presented as a topic word. If it is not the first word, a word that has not appeared for the longest time since the previous appearance may be presented as a topic word. Alternatively, if there is no first word, a predetermined number of words may be presented as topic words in the order in which they appeared last or did not appear for a long time. Furthermore, if there is no first word, the word with the lowest appearance frequency may be presented as a topic word. Furthermore, if there is no first word, a predetermined number of words may be presented as topic words in ascending order of appearance frequency.

The third method is as shown in FIG.
This is a method of presenting, as topic words, remaining words obtained by deleting words previously identified as topic words from topic candidate words. In the case of the third method, a topic word as a topic word identification result is fed back to the analysis information statistical processing unit 111, so that information on a word identified as a topic word in the past is analyzed. Provided to the unit 11.

Fourth, as shown in FIG. 8D, words that are used more than once in the utterance in which conversion is detected, or words in which synonyms appear, are regarded as being emphasized. Is presented as a topic word.

Fifth, as shown in FIG. 8 (e), if the topic conversion factor extracting unit 107 notifies that the contrast word of the word included in the immediately preceding utterance is included in the main utterance. ,
This is a method of presenting the contrast word as a topic word.

Although there are various methods as described above, these methods may be used not only alone but also in combination. However, in this device, topic identification does not occupy a very important position. That is, the most important purpose of the present apparatus is to detect a topic change in real time, and it is not necessary to identify a topic in advance for the detection. This is clearly different from the prior art. In the related art, a topic is first identified, and after the topic has been identified, if a certain part of the topic has changed, it is recognized that a change has occurred. In other words, it is a method that assumes that there is a topic first. On the other hand, the present device is a method that presupposes a topic change at the beginning, and the topic is identified if necessary after the change is detected.

Incidentally, it is actually difficult to identify a topic. Although it is possible to extract a topic word by some kind of weighting as described above, it is determined whether the word really represents a semantically correct topic.
Have difficulty. For the accurate judgment, a mechanism of "understanding the meaning of the utterance" is necessary, and it is considered impossible at present to construct a device capable of understanding the contents of the utterance in the freely developing interactive type. For this reason, in the conventional method, a field dictionary needs to be actually prepared while targeting a free-development-type dialogue, and utterances outside the field cannot be processed. In other words, in order to detect topic change after topic identification, it is generally essential to understand the meaning of the utterance, and as a result, the device needs to prepare some domain knowledge for each corresponding field. is there. Therefore, the applicable field of the device is naturally limited.

On the other hand, in the apparatus according to the present embodiment, conversion recognition is performed first without depending on the field. Then, the utterance in which the conversion is detected always includes the next topic. Extremely speaking, the present apparatus does not need to perform the topic word identification as described above. In that case, the utterance in which the conversion is detected may be presented to the user as it is. In other words, the difficult task of understanding the utterance content may be left to the user,
This is because the meaning is more likely to be conveyed than the device does not understand halfway.

[0140]

As described above, according to the present invention, the morpheme of the input utterance content of the interlocutor is analyzed, and a topic conversion factor rule determined in advance is applied to the analysis result of the morpheme to convert the topic. A factor is extracted, a topic rule determined in advance is applied to the analysis result of the morpheme to extract a topic candidate word, and then the analysis result of the morpheme and the extraction result of the topic candidate word are statistically processed. The conversion contribution index is obtained, the topic conversion factor, the statistical processing result, and the conversion contribution index of the topic candidate word are evaluated, and it can be detected in real time as to whether topic conversion exists in the utterance content.

Further, topic identification when a topic change is detected can be performed irrespective of the type of conversation such as the object-oriented type or the free-development type.

[Brief description of the drawings]

FIG. 1 is a schematic block diagram of a dialog processing device according to an embodiment of the present invention.

FIG. 2 is a schematic block diagram showing an interactive processing device according to another embodiment of the present invention.

FIG. 3 is a first diagram illustrating an operation of an analysis information statistical processing unit in FIG. 2;

FIG. 4 is a second diagram illustrating the operation of the analysis information statistical processing unit in FIG. 2;

FIG. 5 is a third diagram for explaining the operation of the analysis information statistical processing unit in FIG. 2;

FIG. 6 is a graph showing functions used in the operation of the word topic conversion contribution index calculating unit of FIG. 2;

FIG. 7 is a graph showing a result of an experiment performed on the function shown in FIG. 6;

FIG. 8 is a diagram for explaining the operation of the topic word identification unit in FIG. 2;

FIG. 9 is a diagram for explaining a conventional topic understanding method.

[Explanation of symbols]

 1,101 Dialogue processing device 3,103 Dialogue input unit 5,105 Morphological analysis unit 7,107 Topic conversion factor extraction unit 9,109 Topic candidate word extraction unit 11,111 Analysis information statistical processing unit 13,113 Word topic conversion contribution Index operation unit 15, 115 Topic conversion likelihood evaluation unit 117 Topic word identification unit 19 Text input device 119 Speaker monitoring device 121 Utterance transition time monitoring device 123 Comparative word dictionary 21 Word dictionary 125 Synonym dictionary

────────────────────────────────────────────────── ─── Continuation of the front page (56) References JP-A-4-332084 (JP, A) JP-A-6-139276 (JP, A) Atsushi Takeshita, "Topic extraction by surface processing", Information Processing Society of Japan Research Report, Vol. 37 (NL83-4), p. 23-p. 30 (1991) (58) Field surveyed (Int. Cl. ⁶ , DB name) G06F 17/20-17/30 G06F 9/44 JICST file (JOIS)

Claims (10)

(57) [Claims] [Claim 1] Enter the utterance of the speaker, which is the input originating
And input means that converts talk to the sentence, and the morphological analysis means to form state Motokai analyze the sentence given from the entering force means, the topic from the word given from the morphological analysis means
Topic conversion factor extraction means for extracting a topic conversion factor word that can be a conversion factor ; and a topic from words given by the morphological analysis means.
A topic candidate word extracting means for extracting the topic candidate words that can be the topic language representative, the topic within a word given from the morphological analysis unit
Based on the number of appearances and the appearance interval of the candidate word,
Calculate topic conversion contribution index that complements contribute to topic conversion
Based on the topic conversion contribution index and the topic conversion factor word.
Computing means for detecting a change in topic based on the
Interaction processor. Wherein the entering force means includes a text input device to represent the sentence in text format, interaction processing apparatus according to claim 1. Wherein the entering force means comprises a speaker monitoring device for monitoring one speaker is speaking, the processing means, the topic and the topic transformed contribution index
In addition to the conversion factor word, the monitoring result of the speaker monitoring device
The interactive processing device according to claim 1, wherein a conversation change is detected based on the topic . Wherein the entering force means comprises a time monitoring device for monitoring the time from the utterance start time of the speaker to the speech end time, the processing means, the topic and the topic transformed contribution index
In addition to the conversion factor word, the monitoring result of the time monitoring device
The interactive processing device according to claim 1, wherein a conversation change is detected based on the topic . Wherein said morphological analysis means has a predetermined word dictionary, the shape condition of the sentence using the word dictionary
To Motokai analysis, interaction processing apparatus according to claim 1. 6. The morphological analysis means has a predetermined synonym dictionary, and uses the synonym dictionary to generate the morphological
Synonymous out extract the synonym from within a word obtained by the analysis
To create a word list, interaction processing apparatus according to claim 1. 7. The morphological analysis means has a predetermined contrast word dictionary, and uses the contrast word dictionary to generate the morpheme
Contrast by extracting contrast words from words obtained by analysis
To create a word list, interaction processing apparatus according to claim 1. 8. The method according to claim 1, wherein the arithmetic processing means detects a topic change.
When in further Ru comprising a topic word identification means for identifying the topic terms from among the topic candidate word, dialogue processing apparatus according to claim 1. 9. The morphological analysis means has a predetermined synonym dictionary, and uses the synonym dictionary to generate the morphological
Synonymous by extracting synonyms from words obtained by analysis
A word list is created, and the topic word identification unit determines a topic word based on the synonym list.
Identifying serial topic words, interaction processing apparatus according to claim 8. Wherein said morphological analysis means has a predetermined compared Dictionary, the forms using the contrast Dictionary
Extract a comparison word from the words obtained by elementary analysis
A word list is created, and the topic word identification unit determines a topic word based on the word list.
Identifying serial topic words, interaction processing apparatus according to claim 8 or claim 9.