JP4831737B2 - Keyword emphasis device and program - Google Patents

Description

  The present invention corresponds to a keyword indicated by, for example, “interrogative words” + “nouns that can be combined with numbers (hours, months, years, years, photos, etc.)” in the words included in the area specified by the user. Then, a keyword emphasizing device and a program that make it easy to find the answer part corresponding to the question of the question word by highlighting the part represented by “numerical value” + “noun that can be combined with a number” in the text About.

A conventional search result highlighting system for keyword input highlights a word appearing in a title in the text (see Patent Document 1).
JP 2004-280176 A

  The conventional highlighting system cannot highlight a portion corresponding to the question question word in the answer sentence in the case where the title is the question sentence and the text is the answer sentence.

  SUMMARY OF THE INVENTION It is an object of the present invention to solve the above-mentioned problems and to easily find a display portion corresponding to an interrogative word that the user really wants to know in a displayed answer document.

  FIG. 1 is an explanatory diagram of a keyword emphasizing apparatus according to the present invention. In FIG. 1, 1 is a display device (display means), 2 is an input device (input means), 3 is an extraction means (extraction device), 4 is an interrogative postfix postfix extraction device (interrogative postfix postfix extraction means), Reference numeral 5 denotes a main word extraction device (main word extraction means).

  The present invention has the following means in order to solve the conventional problems.

  (1): an input means 2 for inputting a set of questions and their answer articles, and a question word postfix extraction means 4 for extracting a noun that follows the question word or a suffix that follows the question word from the question sentence; And display means 1 for highlighting the noun or suffix that was suffixed to the question word extracted in the answer article. For this reason, it is possible to easily find the display portion corresponding to the question word that the user really wants to know in the displayed answer document.

  (2): a predetermined noun that can be combined with a number that follows the question from the question sentence and a predetermined suffix that can be combined with the number that follows the question. And a display unit 1 that highlights at least one of a number and the extracted predetermined noun or predetermined suffix in the article of the answer. For this reason, it is possible to easily find the display portion of the answer (number) corresponding to the question word that the user really wants to know in the displayed answer document.

  (3): Input means 2 for inputting a set of questions and their answer articles, extraction means 3 for confirming that there is a question word indicating a predetermined quantity expression from the question sentence, and numbers in the answer articles And display means 1 for highlighting. For this reason, it is possible to easily find the display portion of the answer (number) corresponding to the question word that the user really wants to know in the displayed answer document.

  (4): an input means 2 for inputting a set of questions and their answer articles, an extraction means 3 for specifying a pre-designated question word type from the question sentence, and a question word type in the answer article And display means 1 for extracting and highlighting the specific expression corresponding to. For this reason, the display part of the specific expression corresponding to the question word which wants to know really can be easily found in the displayed answer document.

  (5): Input means 2 for inputting a set of questions and their answer articles, extraction means 3 for specifying a question word indicating the reason specified in advance from the sentence of the question, and the reason in the answer article Display means 1 for highlighting a predetermined word. For this reason, the display part which shows the reason corresponding to the question word which wants to know really in the displayed answer document can be found easily.

  (6): In the keyword emphasizing device according to (1) to (5), the keyword emphasizing device includes a main word extracting unit 5 for extracting a main word from the sentence of the question, and the display unit 1 includes the main word extracted in the article of the answer. Highlight a word. For this reason, it is possible to easily find the display portion (answer) corresponding to the question word that the user really wants to know in the answer documents around the highlighted main word.

  (7): In the keyword emphasizing device of (6), different emphasis display is performed for the main word highlighted by the display means 1 and other emphasis display. For this reason, it is possible to more easily find a display portion corresponding to a question word that is really desired to be known in the displayed answer document.

  (8): an input means 2 for inputting a set of questions and their answer articles, and a questionable postfix postfix extraction means 4 for extracting a noun or postscript suffixed from the question sentence from the question sentence. A program for causing a computer to function as the display means 1 that highlights the noun or suffix that is suffixed to the interrogative word extracted in the answer article. For this reason, by installing this program in a computer, it is possible to easily provide a keyword emphasis device that can easily find a display portion corresponding to a question word that the user really wants to know in the displayed answer document.

The present invention has the following effects.
(1): In order to highlight the noun or suffix that was followed by the extracted question word in the answer article by the display means, a display portion corresponding to the question word that is really desired to be known in the displayed answer document is displayed. Can be easily found.

  (2): Because the display means highlights at least one of the number and the specified noun or the specified suffix in the answer article, it corresponds to the question word you really want to know in the displayed answer document The display part of the answer (number) to be performed can be easily found.

  (3): Since the number is highlighted in the answer article by the display means, it is possible to easily find the display portion of the answer (number) corresponding to the question word that the user really wants to know in the displayed answer document.

  (4): Since the specific means corresponding to the type of question word is extracted by the display means and highlighted in the answer article, the proper expression corresponding to the question word that is really desired to be known in the displayed answer document is displayed. The display part can be easily found.

  (5): In order to highlight a predetermined word indicating the reason in the answer article by the display means, it is possible to easily find a display portion indicating the reason corresponding to the question word that the user really wants to know in the displayed answer document. Can do.

  (6): Since the extracted main word is highlighted in the answer article by the display means, it is possible to easily find the display portion corresponding to the question word that the user really wants to know in the answer document.

  (7): Since the main word highlighted by the display means and other highlights are displayed differently, it is easier to find the display part corresponding to the question word that you really want to know in the displayed answer document. Can do.

  The keyword emphasizing device of the present invention is indicated by, for example, “interrogative words” + “nouns that can be combined with numbers (hour, month, year, year, year, sheet, etc.)” among the words included in the region specified by the user. By highlighting the part of the text that is represented by “numerical value” + “noun that can be combined with a number”, the answer part corresponding to the questioning question is easily understood. It is.

  Questions and answers on Web sites and their answers and FAQs (frequently asked questions and their answers) are frequently created and stored manually and presented to users. Yes. At that time, if the highlighting as in the present invention is used, the answer to the question can be easily understood.

(1): Explanation of Keyword Enhancement Device FIG. 1 is an explanatory diagram of a keyword enhancement device. In FIG. 1, a keyword emphasis device (system) is provided with a display device 1, an input device 2, and an extraction device 3. The extraction device 3 is provided with an interrogative postfix word extraction device 4 and a main word extraction device 5.

  The display device 1 is a display means having a display screen such as a CRT or liquid crystal for displaying information. The input device 2 is input means for inputting information. The extraction means 3 is an extraction device (processing means) that performs word extraction processing and the like. The interrogative postfix extraction device 4 is interrogative postfix postfix extraction means for extracting a noun or suffix that comes after the interrogative. The main word extraction device 5 is a main word extraction unit that extracts nouns, verbs, and the like excluding words that are not very meaningful (previously designated words such as “things” and “things”).

(2): Explanation that highlights the word after the question (1)
FIG. 2 is a flowchart for highlighting a word after an interrogative word. Hereinafter, a description will be given according to the processes S1 to S4 of FIG.

S1: A set of a question and an answer article is given by the input device 2, and the process proceeds to step S2.
S2: The interrogative postfix extraction device 4 extracts the interrogative + “noun or suffix” from the question sentence, and proceeds to processing S3.

S3: The main word extraction device 5 extracts the main word from the question sentence, and proceeds to processing S4.
The main words here are nouns and verbs. However, predetermined words designated in advance are excluded (for example, words that do not make much sense such as “things” and “things”).

  S4: The display device 1 highlights the “noun or suffix” that is followed by the main word and question word extracted in the answer article (eg, always displays yellow on the back).

  Example: In the case of a question of how many universities ..., highlight the university in yellow in the answer text. Thereby, the answer to the question can be easily found by looking at the highlighted portion.

  Note that highlighting here is to change the character color, change the background color of the character or shade, change the character font (bold, italic, etc.), underline or parentheses. This can be done by enclosing or providing a symbol or the like on the character.

(Explanation by specific example of FAQ)
(Question) How many universities have high deviation values in Tokyo?
(Answer) Although the value of the deviation varies depending on the faculty that takes the exam, in general, the deviation value of the University of Tokyo seems to be high in each faculty.
In the keyword emphasis device, highlighting is performed as follows (in this case, highlighting is performed with “<” and “>”).
(Question) What is the high degree of deviation in Tokyo?
(Answer) The value of the deviation varies depending on the faculty to take the exam, but in general, the deviation value of Tokyo <University> seems to be high in each faculty.

(3): Specific explanation of word segmentation part of speech Extraction of interrogative words, nouns, suffixes, and verb words can be performed using morphological analysis.

(Description of morphological analysis system)
Here, ChaSen (Japanese) is explained. The morphological analysis system tea bowl developed at Nara Institute of Technology http://chasen.aist-nara.ac.jp/index.html.ja
This splits the Japanese sentence and also estimates the part of speech of each word.

For example, entering “go to school” gives the following results:
School Gakkou School Noun-General To He To Particle-Case Particle-General Go Iku Go Verb-Independence
EOS
In this way, each line is divided so that one word is included, and reading and part-of-speech information are given to each word.

Explanation of English part-of-speech tagging The following part-of-speech tagging systems in English are famous.
Eric Brill,
Transformation-Based Error-Driven Learning and
Natural Language Processing: A Case Study in Part-of-Speech Tagging,
Computational Linguistics, Vol. 21, No. 4, p.543-565, 1995.
This estimates the part of speech of each word in an English sentence.

(4): Explanation highlighted using the word after the question (2)
FIG. 3 is a flowchart for highlighting a word after the question word. Hereinafter, a description will be given according to the processes S11 to S14 of FIG.

  S11: A set of a question and an answer article is given by the input device 2, and the process proceeds to S12.

  S12: The interrogative postfix extraction unit 4 extracts the interrogative + “predetermined noun or suffix that can be combined with a number” from the question sentence, and proceeds to processing S13.

S13: The main word extraction device 5 extracts the main word from the question sentence, and proceeds to processing S14.
The main words here are nouns and verbs. However, predetermined words specified in advance (for example, words that do not make much sense such as “things” and “things”) are excluded.

S14: The display device 1 highlights the main word extracted in the answer article, the number + “the extracted noun or suffix”. The number + “taken out noun or suffix” is highlighted (for example, a color different from the main word (e.g., yellow is always displayed in the background)).
For example: ························································· “3” is highlighted in yellow Thereby, the answer to the question can be easily found by looking at the highlighted portion.

(Explanation by specific example of FAQ)
(Question) How many hours should I sleep?
(Answer) There are various theories, but the theory that 7 to 8 hours is good is common. But I think it is important to go to bed. Even if you sleep for a long time in the daytime, it is less effective than sleeping at night.
In the keyword emphasis device, highlighting is performed as follows (in this case, highlighting is performed with “<” and “>”).

(Question) What is the best time to sleep?
(Answer) There are various theories, but the theory that <7 hours> to <8 hours> is good. But I think it is important to go to bed. Even if you sleep for a long time in the daytime, it is less effective than sleeping at night.
It can also be highlighted as follows (in this case, it is highlighted with “<” and “>”).
(Question) What is the best time to sleep?
(Answer) There are various theories, but the general theory is that 7 <time> to 8 <time> are good. But I think it is important to go to bed. Sleeping in the daytime <time> is less effective than sleeping at night.
In this way, it is immediately convenient to express 7 hours and 8 hours.

(5): Explanation of highlighting using a question word indicating a quantity expression FIG. 4 is a flowchart for highlighting using a question word indicating a quantity expression. Hereinafter, a description will be given according to the processes S21 to S24 in FIG.

  S21: A set of questions and their answers is given by the input device 2, and the process proceeds to S22.

  S22: The extraction means 3 confirms that there is an interrogative that indicates a predetermined quantity expression from the question sentence, and proceeds to processing S23.

S23: The main word extraction device 5 extracts the main word from the question sentence, and proceeds to processing S24.
The main words here are nouns and verbs. However, predetermined words specified in advance (for example, words that do not make much sense such as “things” and “things”) are excluded.

S24: The display device 1 highlights the main words and numbers extracted in the answer article. Here, the numbers are highlighted (for example, a color different from the main word (for example, red) (e.g., yellow is always displayed in the background)).
Example: .... For some questions, three "3" s are highlighted in yellow in the answer text. Thereby, the answer to the question can be easily found by looking at the highlighted portion.

  In this case, the question word whose answer is a number is stored in advance in storage means (not shown) of the keyword emphasis device. There are "how much", "how much", etc. as the question words whose answer is a number.

(Explanation by specific example of FAQ)
(Question) What is the best sleeping time?
(Answer) There are various theories, but the theory that 7 to 8 hours is good is common. But I think it is important to go to bed. Even if you sleep for a long time in the daytime, it is less effective than sleeping at night.
In the keyword emphasis device, highlighting is performed as follows (in this case, highlighting is performed with “<” and “>”).
(Question) What is the best sleeping time?
(Answer) There are various theories, but the theory that <7> hours to <8> hours are good. But I think it is important to go to bed. Even if you sleep for a long time in the daytime, it is less effective than sleeping at night.

(6): Explanation highlighted using the meaning of interrogative words (1)
FIG. 5 is a flowchart for highlighting using the meaning of an interrogative word. Hereinafter, a description will be given according to processing S31 to S34 in FIG.

  S31: A set of questions and their answers is given by the input device 2, and the process proceeds to S32.

  S32: The extraction device 3 specifies the type of question word from the question sentence, and proceeds to processing S33. Do you name people, place names, time? A certain rule, such as which questionable type is what kind, is prepared in advance.

S33: The main word extraction device 5 extracts the main word from the question sentence, and proceeds to processing S34.
The main words here are nouns and verbs. However, predetermined words specified in advance (for example, words that do not make much sense such as “things” and “things”) are excluded.

S34: The display device 1 reads the main word extracted in the answer article,
The name of a question name (eg, “who”) refers to the name of the person. The name of the question name (eg, “where”) refers to the time of the place name. (Including seasons such as spring, summer, etc.).

  Here, in order to determine whether each word indicates a person name, place name, or time, a technique of extracting a specific expression is used.

(Explanation by specific example of FAQ)
(Question) Who is the most disturbing person this year?
(Answer) There were various events this year, but Mr. Horie is the most prominent person in the general elections and acquisition plays. I'm looking forward to seeing who will come out next year.
In the keyword emphasis device, highlighting is performed as follows (in this case, highlighting is performed with “<” and “>”).
(Question) Who is the most disturbing person this year?
(Answer) There were various events this year, but Mr. Horie is the person who is conspicuous in various fields, such as general elections and acquisition plays. I'm looking forward to seeing who will come out next year.

(7): Explanation highlighted using the meaning of interrogative words (2)
FIG. 6 is a flowchart for highlighting using the meaning of a question word. Hereinafter, a description will be given according to processing S41 to S44 of FIG.

  S41: A set of a question and an answer article is given by the input device 2, and the process proceeds to step S42.

  S42: The extraction means 3 identifies the type of question word from the question sentence, and proceeds to processing S43. Here, it is assumed that the question word indicates the reason (for example, “why” “why”). A certain rule, such as which questionable type is what kind, is prepared in advance.

S43: The main word extraction device 5 extracts the main word from the question sentence, and proceeds to processing S44.
The main words here are nouns and verbs. However, predetermined words specified in advance (for example, words that do not make much sense such as “things” and “things”) are excluded.

  S44: The display device 1 uses the main words taken out in the reply article, the predetermined words “reason”, “for”, “from”, “so”, “reason”, “cause”, “for this”, etc. Emphasize the display (always put yellow on the back).

(Explanation by specific example of FAQ)
(Question) Why are computers useful?
(Answer) Computers, also called computers, are useful machines that perform various calculations on behalf of humans. A computer generally consists of a computing device and a storage device. When a computer gives a program, it executes it on a computing device and a storage device and performs various calculations. By changing the program to be given, the computer can execute different processes accordingly. For this reason, the computer can perform various processes and is convenient.

In the keyword emphasis device, highlighting is performed as follows (in this case, highlighting is performed with “<” and “>”).
(Question) Why are computers useful?
(Answer) Computers, also called computers, are useful machines that perform various calculations on behalf of humans. A computer generally consists of a computing device and a storage device. When a computer gives a program, it executes it on a computing device and a storage device and performs various calculations. By changing the program to be given, the computer can execute different processes accordingly. <For this reason> Computers are useful because they can perform various processes.
In this way, it is easy to immediately see that the reason is written in front of the highlighted “for this purpose”.

  It should be noted that the user who uses the keyword emphasizing apparatus can be set not to perform the emphasis display.

  In addition, in order to determine whether each word indicates a person name, place name, or time, a technique for extracting a specific expression is used.

(8): Explanation of Specific Expression Extraction The specific expression is a linguistic expression that means a specific thing / quantity such as a proper noun such as a person name, a place name, or an organization name, or a numerical expression such as a monetary amount. Specific entity extraction is a technology that automatically extracts such specific expressions from a sentence using a computer. For example, if a specific expression is extracted for a sentence “The Japanese Prime Minister is Junichiro Koizumi”, the specific expressions “Japan” and “Junichiro Koizumi” are extracted as place names and personal names.

a, Explanation in the case of using morpheme analysis To extract a specific expression, the morpheme analysis system ChaSen described above can be used. For example, if “Tokyo is the capital of Japan” is input to ChaSen, the following output is obtained.

Output Japan Japan Japan Noun-proprietary noun-region-country noun-association capital capital capital noun-general is ha particle-subject tokyo tokyo noun-proprietary noun-region-general Basic form
EOS
In this case, the part-of-speech of noun-proprietary noun-region is output, and this system can be used to extract the proper expression of the place name.

  For example, if “Mr. Murayama said” is input to the system, the following is obtained as an output.

Output Murayama Murayama Murayama Noun-Proper noun-Personal name-Surname Prime Minister Shosho Prime Noun-General ga is a particle-case particle-general Say Veri
EOS
If this is the case, the part of speech of noun-proper noun-personal name will be output. Using this system, it is possible to retrieve a specific name of a person.

b. Explanation of manual rule creation In addition to the case of using morphological analysis, there is also a method of manually creating a rule to extract a specific expression.

For example, by creating a rule manually, the extraction means (device) can extract a specific expression (person name, place name, etc.) according to the following rule.
Noun + “san” is the name of the person Noun + “prime” is the name of the person Noun + “town” is the name of the place Noun + “city” is the name of the place

c, Explanation when using machine learning (Explanation of user-dependent specific expression extraction and display system)
This is a technique in which a user tags specific expressions with some corpus (language resources, for example, electronic data of newspapers), and automatically extracts the specific expressions with other data.

  In the extraction of the specific expression, a learning destination is used to estimate a classification destination that is likely to be the case for the data of a predetermined unit of the input data.

  For example, when the support vector machine method is used for extracting a proper expression, the machine learning means specifies a classification destination that can be a solution from the teacher data, divides the classification destination into a positive example and a negative example, A hyperplane that divides the positive and negative examples into hyperplanes by maximizing the interval between the positive and negative examples on a space whose dimension is a set of features according to a function that executes a support vector machine method using a kernel function The hyperplane is obtained as a learning result, and the hyperplane is stored in the learning result storage means. Then, using the learning result hyperplane stored in the learning result storage means, whether the set of features of the input data is on the positive example side or the negative example side in the space divided by this hyperplane And a classification destination determined based on the identified result is estimated as a classification destination that is likely to be a set of input data features.

  The unique expression extraction process is a process of extracting unique expressions such as place names, person names, organization names, and numerical expressions from text data. The classification destination that is the analysis result in the specific expression extraction processing is, for example, a place name, a person name, an organization name, a date expression, a time expression, a monetary expression, and a ratio expression. The teacher data is assigned a classification label corresponding to each of these classification destinations.

  The tag registration means for creating teacher data, when the user designates the classification destination of the following specific expression extraction process and the corresponding classification tag via the input device, the classification destination specified by the user and the classification Tags (start tag and end tag) are input and stored in the tag storage means.

<PERSON></PERSON>: Classification destination = person name,
<LOCATION></LOCATION>: Classification destination = place name,
<ORGANIZATION></ORGANIZATION>: Classification destination = organization name
<ARTIFACT></ARTIFACT>: Destination = unique name,
<DATE></DATE>: Classification destination = date expression,
<TIME></TIME>: Classification destination = time expression
<MONEY></MONEY>: Classification destination = amount expression,
<PERCENT></PERCENT>: Classification destination = ratio expression.

  In this example, teacher data is created by assigning classification labels to be assigned to each character. For example, the <PERSON> </ PERSON> classification tag corresponding to the classification label “person name” has “B-” indicating the first character or “I-” indicating a character other than the first character. “PERSON” and “I-PERSON”. Also, “OTHER” is registered as a label to be added to characters that do not fall under the classification destination.

  Further, when a character type is used as a classification destination in the unique expression extraction process, the following classification destination and classification tag are stored in the tag storage unit.

<KANJI></KANJI>: Classification destination = Kanji,
<KATAKANA></KATAKANA>: Classification destination = Katakana
<ALPHABETIC></ALPHABETIC>: Classification destination = English characters
<NUMERIC></NUMERIC>: Classification destination = number.

  Then, when the corpus input means inputs a corpus composed of text data to which the classification destination for the specific expression extraction processing is not assigned, the tag assignment means displays the corpus text data and prompts the user for a tagging operation. The grant screen is displayed on the display device.

  When the user wants to assign the classification destination and the classification destination to be assigned, the tag assignment unit inserts the selected classification tags before and after the character string corresponding to the place designated on the tag assignment screen.

  For example, it is assumed that the input corpus includes text data “… The prime minister of Japan is Mr. Koizumi. Mr. Koizumi always does what he thought.” The user designates the word “Japan” to which the classification destination is to be given by a mouse drag operation or the like on the text data displayed in the designated item on the tag assignment screen. Further, from the selection items displayed by clicking the right button of the mouse, the classification destination “place name” is selected by clicking the left button of the mouse. Similarly, the word “Koizumi” is designated in the designated item, and the classification destination “person name” is selected from the selection item.

The tag assigning means inserts the selected classification tag before and after the character string in the text data corresponding to the location specified on the tag assignment screen. The text data to which the classification tag is assigned is as follows.
“… <LOCATION> Japan </ LOCATION>'s prime minister is <PERSON> Koizumi </ PERSON>. Mr. Koizumi always does what he thought ....
Further, when the user assigns the classification destination by the designated item and the range to be used as the teacher data is designated, the tag assigning means reads the character string of the text data corresponding to the range designated on the tag assignment screen. Add start tag and end tag of range specification tag before and after. For example, assume that the user designates the sentence “The Japanese Prime Minister is Mr. Koizumi” as a range by dragging the mouse. The tag assigning means inserts a range designation tag before and after the character string of the text data corresponding to the designated range. The text data to which the range specification tag is attached is as follows.
“... <UC><LOCATION> Japan </ LOCATION>'s prime minister is <PERSON> Koizumi </ PERSON>. </ UC>
On the other hand, when the user does not specify the range to be used as the teacher data after assigning the classification destination, the tag assignment means allows the user to select a predetermined location including the location where the classification destination is assigned in the designated item. It is regarded as the selected range, and a range specification tag is added before and after the range. For example, the tag assigning means regards a range such as a predetermined number of characters and the number of words consecutively before and after a word in a character string to which a classification tag in text data is assigned as a range selected by the user, and before and after the considered range. Add a range specification tag.

  Then, the tag assigning means stores the text data (tagged corpus) obtained by adding the classification tag and the range designation tag to the text data in the corpus storage means.

  Thereafter, the user range extraction unit extracts text data (user range data) in a range surrounded by the start tag <UC> and end tag </ UC> of the range designation tag from the tagged corpus of the corpus storage unit. . Note that the user explained here that the UC tag is attached, but the system creator can also attach this tag, and it is also possible to use all data as teacher data without attaching the UC tag. is there.

  Then, the teacher data conversion means divides the extracted text data into predetermined units (here, character units), detects a character string surrounded by the classification tag from the extracted text data, and each unit ( Character) is assigned a classification label corresponding to the classification tag, and a character that is not assigned a classification tag is assigned a classification label indicating that there is no classification destination. To do.

  For example, the text data “<UC> <LOCATION> Japan </ LOCATION> 's prime minister is <PERSON> Koizumi </ PERSON>. </ UC>” is extracted as teacher data. To do. The teacher data conversion means, for example, classifies the first character “small” in the character string “small, fountain” surrounded by the text data classification tags <PERSON> and </ PERSON>, indicating the beginning of the classification destination “person name”. Similarly, the label “B-PERSON” is given the classification label “I-PERSON” indicating the other than the head of the classification destination “person name” to the next character “Izumi”. In addition, a portion of the text data that is not surrounded by the classification tag “No, Neck, Phase, Sasan, N, D, S, etc.” indicates that each character does not correspond to the classification destination designated by the user. A classification label “0” is assigned.

  Then, the feature extraction means performs morpheme analysis processing on the teacher data, extracts features for each predetermined unit (for example, character), and generates a set of feature sets and classification labels.

  Features include, for example, parts of speech information (classification of nouns, proper nouns, personal names, surnames, etc.), character position information in morphemes (classification of the first, other, etc.), character type information (kanji, katakana, English letters, numbers, etc.) Classification), classification destination, and the like are extracted.

  In the language analysis process, the machine learning means uses a set of feature sets and classification labels to learn for each unit (character) what kind of classification destination is likely to be in the case of that feature set, The learning result is stored in the learning result storage means.

  The machine learning means, for example, uses a set of features to learn about the character “small” in the combination of the feature of each character and the classification label.

  Here, as the machine learning method, an extended support vector machine method capable of dealing with multiple classifications is used.

  The support vector machine method is a method of classifying data composed of two classifications by dividing a space by a hyperplane. At this time, if the two classifications consist of a positive example and a negative example, the larger the interval (margin) between the positive example and the negative example in the learning data, the lower the possibility of incorrect classification with open data. The hyperplane that maximizes the margin is obtained, and classification is performed using the obtained hyperplane.

  The maximum margin of the support vector machine method is to determine a separation hyperplane that maximizes the distance (margin) between the separation hyperplane obtained in a certain space and the hyperplane parallel to and equidistant from the classification hyperplane.

  In the support vector machine method, in general, in the training data, an extension of the method that a small amount of cases may be included in the inner area of the margin, or an extension that makes the linear part of the hyperplane nonlinear (introduction of kernel function ) Is used. Such an extended method is equivalent to classification using a discriminant function, and two classes can be discriminated depending on whether the output value of the discriminant function is positive or negative.

  In addition, since support vector machines are binary classification of positive examples and negative examples, binary classification is performed using techniques such as the One vs Rest method and the Pair Wise method. Extend to multi-value classification.

  In the One vs Rest method, for example, when there are three classification destinations a, b, and c, there are three sets of “a and other”, “b and other”, and “c and other”. Prepare a binary classifier (whether it is a certain classification destination or another classification destination), and learn each with a support vector machine. And when estimating the classification destination which is a solution, the learning result of three support vector machines is utilized. See how the input data to be estimated is estimated in these three support vector machines, and are classified into the non-other side (positive example) of the three support vector machines, and the separation of the support vector machines The solution to be obtained is the classification destination of the one farthest from the plane.

The pair-wise (Pair Wise) method, and binary classifier _k C ₂ pieces prepared for any two grouping destination of k grouping destination performs Round Robin of grouping destinations among the most among the A solution for obtaining a classification destination having a large number of times selected as a classification destination.

  After the completion of the machine learning, the data input unit inputs text data to be analyzed. The feature extraction means performs morpheme analysis on the input text data (input data) and extracts features for each predetermined unit (for example, character) as in the teacher data creation process.

  Then, the solution estimation means estimates a classification label that is likely to be the case for a predetermined unit (character) of the input data, using the learning result stored in the learning result storage means.

  And a tag provision means inserts the classification tag corresponding to the classification label estimated to be the solution before and after the corresponding character or character string of the input data.

  The analysis result display processing means displays the input data to which the classification tag is added on the display device in a display mode according to a predetermined display rule. Here, a character string surrounded by the classification tags <PERSON> </ PERSON> and a character string surrounded by <LOCATION> </ LOCATION> can be extracted as specific specific expressions.

(9): Description of program installation Display device (display means) 1, input device (input means) 2, extraction means (extraction device) 3, interrogative postfix excerpt extraction device (interrogative postfix postfix extraction means) 4, The main word extraction device (main word extraction means) 5 and the like can be configured by a program, executed by a main control unit (CPU), and stored in the main memory. This program is generally processed by a computer. This computer is composed of hardware such as an input device as input means such as a main control unit, main memory, file device, display device, and keyboard.

  The program of the present invention is installed on this computer. In this installation, these programs are stored in a portable recording (storage) medium such as a floppy disk or a magneto-optical disk, and a drive device for accessing the recording medium provided in the computer is used. Alternatively, it is installed in a file device provided in the computer via a network such as a LAN. Then, the program steps necessary for processing are read from the file device into the main memory and executed by the main control unit.

It is explanatory drawing of the keyword emphasis apparatus of this invention. It is a flowchart which highlights and displays the word attached after the interrogative word of this invention. It is a flowchart which highlights and displays the word attached after the interrogative word of this invention. It is a flowchart which highlights using the question word which points to the quantity expression of this invention. It is a flowchart which highlights using the meaning of the question word of this invention. It is a flowchart which highlights using the meaning of the question word of this invention.

Explanation of symbols

1. Display device (display means)
2 Input device (input means)
3 Extraction means (extraction device)
4 Interrogative postfix extraction device (interpreter postfix extraction means)
5. Main word extraction device (main word extraction means)

Claims (5)

An input means for entering a set of questions and their answer articles;
An interrogative postfix extraction means for extracting a noun that follows the interrogative from the sentence of the question or a suffix that follows the interrogative;
A keyword emphasizing apparatus comprising: a display unit that highlights a noun or suffix that is suffixed to the question word extracted in the answer article. An input means for entering a set of questions and their answer articles;
A predetermined noun that can be combined with a number after the question sentence from the sentence of the question, or an interrogative postfix extraction means that extracts a predetermined suffix that can be combined with the number after the question word;
A keyword emphasizing apparatus comprising: a display means for emphasizing at least one of a number and the extracted predetermined noun or predetermined suffix in the answer article. An input means for entering a set of questions and their answer articles;
Extraction means for confirming that there is a question word indicating a predetermined quantity expression from the sentence of the question;
The question sentence confirmed by the extraction means includes a questioning unit that points to the predetermined quantity expression, and the answer to the question is a number, thereby including display means for highlighting the number in the article of the answer. Keyword emphasis device characterized by. An input means for entering a set of questions and their answer articles;
From the question sentence, extraction means for specifying the type of question word specified in advance, which indicates a person name, a place name, or a time;
A keyword emphasizing apparatus comprising: display means for extracting and highlighting a specific expression of a person name, place name, and time corresponding to the type of question word in the answer article. An input means for entering a set of questions and their answer articles;
An interrogative postfix extraction means for extracting a noun that follows the interrogative from the sentence of the question or a suffix that follows the interrogative;
As a display means for highlighting the noun or suffix that followed the question word extracted in the answer article,
A program that allows a computer to function.

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