Document retrieval system and question answering system

• US 20040049499A1
• Filed: 08/11/2003
• Published: 03/11/2004
• Est. Priority Date: 08/19/2002
• Status: Abandoned Application

• Current Assignee: Matsushita Electric Industrial Company Limited (Panasonic Holdings Corporation)
• Original Assignee: Matsushita Electric Industrial Company Limited (Panasonic Holdings Corporation)
• Inventors: Nomoto, Masako, Suzuki, Hiroyuki, Sato, Mitsuhiro
• Application Number:
  US10/637,498
• Publication Number:
  US 20040049499A1
• Time in Patent Office: Days
• US Class Current: 707/3
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems

Abstract

A document retrieval system capable of obtaining information requested by the user with a high degree of accuracy. In this system, the query input section 102 receives query input by the user. The keyword extraction section 104 analyzes the input query and extracts keywords. The keyword type assignment section 106 decides the type of each extracted keyword and assigns a keyword type. The question type decision section 108 decides the question type. The keyword classification section 110 classifies the keywords to which the keyword types are assigned into a major type and minor type with reference to the keyword classification rules stored in the keyword classification rule storage section 112. The document retrieval section 114 searches a document collection stored in the document storage section 116 using the classified keyword groups and obtains the document of the retrieved result.

Claims

1. A document retrieval system that compares a degree of similarity between a query and a document collection and outputs a retrieved result ranked in order of similarity, comprising:

an extraction section that extracts a keyword from the query;

a classification section that classifies the keyword extracted by said extraction section into a major type related to a central subject indicated by the query and a minor type related to supplementary information, based on attributes of said keyword; and

a retrieval section that carries out document search processing to obtain the retrieved result ranked in order of similarity based on the classification result of said classification section.

2. The document retrieval system according to claim 1, wherein said attributes are semantic attributes.

3. The document retrieval system according to claim 1, wherein said attributes are syntactic attributes.

4. The document retrieval system according to claim 1, wherein said attributes are statistical attributes.

5. The document retrieval system according to claim 1, wherein said attributes are a combination of at least two types of attributes of semantic attributes, syntactic attributes and statistical attributes.

6. The document retrieval system according to claim 2, wherein meaning classification whereby factual expressions and interrogative expressions are classified according to meanings of said respective expressions is used for said semantic attributes.

7. The document retrieval system according to claim 6, wherein said meaning classification has hierarchic levels of detailedness.

8. The document retrieval system according to claim 3, using criteria as to whether said syntactic attributes are elements to be syntactical core elements or not.

9. The document retrieval system according to claim 1, further comprising a storage section that stores rules for classifying keywords used by said classification section into a major type and minor type, wherein said rules take the type of the query into consideration.

10. The document retrieval system according to claim 1, further comprising a storage section that stores rules for classifying keywords used by said classification section into a major type and minor type, wherein said rules do not take the type of the query into consideration.

11. The document retrieval system according to claim 1, wherein said retrieval section carries out document search processing using keywords that belong to the major type as keywords essential to limit a set of documents to be retrieved, and keywords that belong to the major type and keywords that belong to the minor type as ranking keywords for comparing the degree of similarity between the query and document collection and sorting the retrieved documents of the retrieved result based on the degree of similarity.

12. The document retrieval system according to claim 1, wherein when comparing the degree of similarity between the query and document collection, said retrieval section classifies the documents of the retrieved result into layers based on the number of types of keywords belonging to the major type that have appeared and compares the degree of similarity in said respective layers obtained.

13. The document retrieval system according to claim 1, wherein when comparing the degree of similarity between the query and individual documents in the collection document collection, said retrieval section classifies the documents of the retrieved result into layers based on the number of major-type keywords in individual documents, then further classifies the documents in said respective layers obtained into layers based on the number of minor-type keywords in individual documents, and compares the degree of similarity in said respective layers obtained.

14. The document retrieval system according to claim 12, wherein when classifying the documents of the retrieved result into layers based on the number of types of keywords belonging to the major type that have appeared, said retrieval section classifies the documents into layers based on not only the number of types of said keywords that have appeared but also document restrictiveness of said keywords.

15. The document retrieval system according to claim 13, wherein in at least one of the case where the documents of the retrieved result are classified into layers based on the number of types of keywords belonging to the major type that have appeared and the case where the documents in said respective layers obtained are further classified into layers based on the number of types of keywords belonging to the minor type that have appeared, said retrieval section classifies the documents into layers based on not only the number of types of said keywords that have appeared but also document restrictiveness of said keywords.

16. The document retrieval system according to claim 1, wherein of the keywords extracted by said extraction section, keywords having specific semantic attributes are used as search conditions for bibliographic information of documents.

17. The document retrieval system according to claim 1, wherein when semantic attribute shaving hierarchic levels of detailedness are associated with their corresponding keywords, said retrieval section estimates, when comparing the degree of similarity between the query and document collection, the level of detailedness of the semantic attributes required of the keywords in the documents of the retrieved result based on the level of detailedness of the semantic attributes of the keywords in the query, evaluates the level of detailedness of the semantic attributes of the keywords in the documents of the retrieved result and thereby performs filtering of the documents of the retrieved result.

18. The document retrieval system according to claim 1, wherein when semantic attribute shaving hierarchic levels of detailedness are associated with their corresponding keywords, said retrieval section estimates, when comparing the degree of similarity between the query and document collection, the level of detailedness of the semantic attributes required of the keywords in the documents of the retrieved result based on the level of detailedness of the semantic attributes of the keywords in the query, evaluates the level of detailedness of the semantic attributes of the keywords in the documents of the retrieved result and thereby determines ranking of the documents of the retrieved result.

19. The document retrieval system according to claim 1, further comprising an assignment section that assigns semantic attributes to the document collections, wherein said assignment section assigns tags indicating semantic attributes to the document collections beforehand.

20. The document retrieval system according to claim 17 or 18, wherein expressions of keywords in the query and keywords in the document of the retrieved result are normalized beforehand.

21. The document retrieval system according to claim 19, wherein expressions of keywords in the query and keywords in the document of the retrieved result are normalized beforehand.

22. The document retrieval system according to claim 1, wherein said retrieval section carries out document search processing using portions of a document as a search unit.

34. The question answering system according to claim 25, wherein said document retrieval section comprises the document retrieval system according to claim 1.

23. A document searching method for comparing the degree of similarity between query and an individual documents in the collection and outputting a retrieved result ranked in order of similarity, comprising:

an extraction step of extracting keywords from the query;

a classification step of classifying the keywords extracted in said extraction step into a major type related to a central subject indicated by the query and a minor type related to supplementary information based on attributes of said keywords; and

a searching step of carrying out document search processing to obtain retrieved results ranked in order of similarity based on the classification result in said classification step.

24. A document search program for comparing the degree of similarity between query and an individual documents in the collection and outputting a retrieved result ranked in order of similarity, causing a computer to execute:

an extraction step of extracting keywords from the query;

a classification step of classifying the keywords extracted in said extraction step into a major type related to a central subject indicated by the query and a minor type related to supplementary information based on attributes of said keywords; and

a searching step of carrying out document search processing to obtain retrieved results ranked in order of similarity based on the classification result in said classification step.

25. A question answering system comprising:

a question input section that inputs query;

a question analysis section that analyzes the input query;

a document retrieval section that searches for a document collection based on the analysis result of the query;

an answer generation section that generates an answer to the query based on the document of the retrieved result; and

an answer output section that outputs the answer generated, wherein said question analysis section comprising;

a keyword extraction section that extracts keywords from the input query;

a keyword type assignment section that assigns semantic attributes having hierarchic levels of detailedness to the extracted keywords as the keyword types; and

a question type decision section that decides the type of the query based on the semantic attributes with a level of detailedness assigned to the extracted keywords,

said answer generation section comprising;

a semantic attribute assignment section that assigns semantic attributes with a level of detailedness to the keywords in the document of the retrieved result;

an answer candidate selection section that selects answer candidates from expressions of retrieved documents, keywords of which are assigned semantic attributes with a level of detailedness, based on the decision result of said question type decision section and the level of detailedness of said decision result; and

an answer ranking section that ranks the selected answer candidates, and

said answer output section outputs the answers based on the ranking result of said answer ranking section.

26. The question answering system according to claim 25, using meaning classification whereby factual expressions and interrogative expressions are classified according to meanings of said expressions as said semantic attributes.

27. The question answering system according to claim 25, wherein when semantic attributes or level of detailedness of keywords in a retrieved document cannot be uniquely decided, said semantic attribute assignment section assigns semantic attributes with a level of detailedness while leaving a plurality of possibilities.

28. The question answering system according to claim 25, wherein when the level of detailedness requested by the query is not clear, said answer generation section further comprises an answer detailedness level decision section that decides an appropriate level of detailedness as an answer.

29. The question answering system according to claim 25, wherein when there are variations in the level of detailedness of keywords in the retrieved documents, said answer generation section further comprises an answer detailedness level decision section that decides an appropriate level of detailedness as an answer.

30. The question answering system according to claim 28 or 29, wherein said answer detailedness level decision section presents the decision result as a recommended level together with other levels of detailedness to the user and decides the level of detailedness of the answer according to the selection by the user.

31. The question answering system according to claim 25, wherein when expressions of keywords in the query and keywords in the documents in the collection are normalized, said answer candidate selection section approves keywords of expressions different from expressions of the keywords in the query as different expressions indicating the same object.

32. The question answering system according to claim 31, wherein when there are different expressions in the answer candidates, said answer output section outputs normalized expressions as an answer.

33. The question answering system according to claim 31, wherein when an answer candidate character string has a different expression, said answer output section selects an appropriate answer candidate character string from expressions approved as the different expressions based on the level of detailedness of the different expressions indicating the same object or normalized expressions.

35. A question answering method comprising:

a question input section that inputs query;

a question analysis section that analyzes the input query;

a document retrieval section that searches a document collection based on the analysis result of the query;

an answer generation section that generates answers to the query based on the retrieved documents; and

an answer output section that outputs the generated answers, wherein said question input section comprising a question inputting step of inputting query,

said question analysis section comprises a keyword extracting step of extracting keywords from the query input in said question inputting step,

said question analysis section comprises a keyword type assigning step of assigning semantic attributes having hierarchic levels of detailedness as keyword types to the keywords extracted in said keyword extracting step,

said question analysis section comprises a question type deciding step based on semantic attributes having level of detailedness assigned to the keywords extracted in said keyword extracting step,

said document retrieval section comprises a document searching step of searching a document collection based on the query analysis results in said keyword type assigning step and said question type deciding step,

said answer generation section comprises a semantic attribute assigning step of assigning semantic attributes with a level of detailedness to keywords in the document of the retrieved result in said document searching step,

said answer generation section comprises an answer candidate selecting step of selecting answer candidates from expressions of retrieved documents, keywords of which are assigned semantic attributes with a level of detailedness, in said semantic attribute assigning step based on the decision result in said question type deciding step and the level of detailedness of said decision result,

said answer generation section comprises an answer ranking step of ranking the answer candidates selected in said answer candidate selecting step, and

said answer output section comprises an answer outputting step of outputting answers based on the ranking result in said answer ranking step.

36. A question answering program in a question answering system comprising:

a question input section that inputs query;

a question analysis section that analyzes the input query;

a document retrieval section that searches a document collection based on the analysis result of the query;

an answer generation section that generates answers to the query based on the document of the retrieved result; and

an answer output section that outputs the generated answers, said question answering program causing a computer to execute;

a question inputting step of inputting query;

a keyword extracting step of extracting keywords from the query input in said question inputting step;

a keyword type assigning step of assigning semantic attributes having hierarchic levels of detailedness as keyword types to the keywords extracted in said keyword extracting step;

a question type deciding step of deciding the type of the query based on semantic attributes having a level of detailedness assigned to the keywords extracted in said keyword extracting step;

a document searching step of searching a document collection based on the query analysis results in said keyword type assigning step and said question type deciding step;

a semantic attribute assigning step of assigning semantic attributes with a level of detailedness to keywords in the document of the retrieved result in said document searching step;

an answer candidate selecting step of selecting answer candidates from expressions of retrieved documents, keywords of which are assigned semantic attributes with a level of detailedness, in said semantic attribute assigning step based on the decision result in said question type deciding step and the level of detailedness of said decision result;

an answer ranking step of ranking the answer candidates selected in said answer candidate selecting step; and

an answer outputting step of outputting answers based on the ranking result in said answer ranking step.