RECOMMENDING QUESTIONS TO USERS OF COMMUNITY QIESTION ANSWERING

• US 20090253112A1
• Filed: 04/07/2008
• Published: 10/08/2009
• Est. Priority Date: 04/07/2008
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Cao, Yunbo, Lin, Chin-Yew
• Application Number:
  US12/098,457
• Publication Number:
  US 20090253112A1
• Time in Patent Office: Days
• US Class Current: 434/322
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

The present system graphs topic terms in stored cQA questions and also converts a submitted question into a graph of topic terms. Topic terms that correspond to a question topic are delineated from topic terms that correspond to question focus. New questions are recommended to the user based on a comparison between the topics of the new questions and the topic of the submitted question as well as the focus of the new questions and the focus of the submitted question.

Claims

1. A method of recommending additional questions based on an input question to a question answering system, comprising:

dividing the input question into a question topic and a question focus;

accessing an index of questions to identify stored questions having a similar question topic to the input question, but different question focus from the input question;

generating recommended questions by substituting the question focus for the identified stored questions for the question focus of the input question; and

outputting the recommended questions as the additional questions.

2. The method of claim 1 wherein dividing the input question comprises:

identifying topic terms in the input question; and

generating a topic chain by linking the topic terms to one another based on a specificity of each of the topic terms.

3. The method of claim 2 wherein, in the index of questions, the stored questions are indexed by topic chains generated for each of the stored questions and wherein accessing the index comprises:

identifying topic chains in the index that have topic terms with highest specificity that are the same as topic terms in the topic chain for the input question that has a highest specificity.

4. The method of claim 3 wherein dividing the input question comprises:

constructing a question tree from the topic chains identified in the index and the topic chain for the input question; and

performing a tree cut on the question tree to divide the topic terms in the topic chains used to construct the question tree into topic terms that represent question topic and question focus for the input question and stored questions represented by the topic chains used to construct the question tree.

5. The method of claim 4 wherein generating recommended questions comprises:

forming the recommended questions using the topic terms representing the question topic of the input question but using topic terms representing the question focus of the stored questions.

6. The method of claim 5 wherein generating recommended questions comprises:

generating a recommendation score for each recommended question and wherein outputting the recommended questions comprises outputting only recommended questions having a sufficient recommendation score.

7. The method of claim 6 wherein performing a tree cut divides the topic chains used to construct the question tree into head portions and tail portions and wherein generating a recommendation score comprises:

calculating a similarity between the head portion of each topic chain corresponding to a stored recommended question with the head portion of the topic chain generated for the input question; and

calculating a similarity between the tail portion of each topic chain corresponding to a stored recommended question with the tail portion of the topic chain generated for the input question.

8. The method of claim 7 wherein outputting only recommended questions having a sufficient recommendation score comprises:

outputting a recommended question only if it has a recommendation score indicating the head portion of its corresponding topic chain is sufficiently similar

to the head portion of the topic chain for the input question and indicating that the tail portion of its corresponding topic chain is sufficiently dissimilar to the tail portion of the topic chain for the input question.

9. The method of claim 3 and further comprising:

generating the index by, for each stored question to be indexed, extracting topic terms from the question;

calculating a specificity for each topic term extracted;

linking the topic terms to one another in order of the calculated specificity to obtain a topic chain for the question; and

indexing the question based on the topic chain.

10. The method of claim 9 wherein extracting the topic terms comprises:

identifying as topic terms base noun phrases and wh-n-grams in the question.

11. The method of claim 9 wherein extracting topic terms comprises:

extracting a set of topic terms for all of the questions to be indexed; and

reducing the set of topic terms to a subset of topic terms more general that the set of topic terms.

12. The method of claim 4 wherein constructing a question tree comprises:

constructing a prefix tree using the topic terms in the topic chains identified in the index and the topic chain for the input question.

13. A system for recommending questions to a user of a community based question answering system, comprising:

an indexing system configured to generate an index of previously asked questions comprising;

a topic chain generator configured to generate a topic chain for each previously asked question to be indexed, each topic chain being a linked set of topic terms, linked in an order based on a specificity of the topic terms occurring in the previously asked question being indexed;

an indexing component configured to index the previously asked questions to be indexed based on the topic chains;

a question answering system configured to recommend questions based on an input question, comprising;

a question collection component configured to identify a set of topic chains in the index based on a topic chain generated for the input question;

a topic and focus identifier component configured to identify topic terms corresponding to question topic and question focus in the topic chains identified in the index and the topic chain for the input question; and

a recommendation component configured to generate and output recommended questions by substituting the topic terms corresponding to question focus in the topic chains identified in the index, for the topic terms corresponding to question focus in the topic chain for the input question.

14. The system of claim 13 wherein the topic chain generator is configured to generate the topic chain for the input sentence.

15. The system of claim 13 wherein the topic chain generator comprises:

a topic term acquisition component configured to extract topic terms from a question; and

a topic term linking component configured to calculate a specificity measure for each topic term and to link the topic terms extracted from a question to one another in an order based on a value of the specificity measure.

16. The system of claim 15 wherein the question answering system comprises:

a question tree construction component configured to construct a question tree from the set of topic chains identified; and

wherein the topic and focus identifier component comprises a tree cut component configured to cut the question tree to divide the topic chains used to construct the question tree into topic and focus portions.

17. The system of claim 16 wherein the recommendation component is configured to generate a recommendation score for each topic chain identified based on how similar the topic and focus portions are to the topic and focus portions of the topic chain for the input question.

18. The system of claim 17 wherein the recommendation score for an identified topic chain increases as a similarity of the topic portions of the identified topic chain and the topic chain for the input question increases and as a similarity of the focus portions of the identified topic chain and the topic chain for the input question decreases.

19. A computer readable storage medium having computer executable instructions encoded thereon which, when executed by a computer, cause the computer to recommend additional questions to a user of a community-based question answering system by performing steps of:

generating topic chains of linked topic terms for each of a plurality of stored questions;

generating a topic chain for the input question;

identifying a set of topic chains for the stored questions based on the topic chain for the input question;

building a question tree using the identified set of topic chains and the topic chain for the input question;

dividing the question tree to identify topics and foci in the topic chains used to construct the question tree; and

generating recommended questions by substituting the foci of the topic chains in the identified set of topic chains for the focus of the topic chain for the input question; and

outputting the recommended questions if the substituted foci are sufficiently dissimilar from the focus of the topic chain for the input question.

20. The computer readable medium of claim 19 wherein generating topic chains comprises:

extracting topic terms from questions previously asked in the community-based question answering system;

calculating a specificity for each topic term; and

linking the topic terms for each question based on the specificity.