Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Semantic segment extraction and matching for Internet.

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Presentation transcript:

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Semantic segment extraction and matching for Internet FAQ retrieval Graduate : Chen, Shao-Pei Authors : Chung-Hsien Wu, Senior Member, IEEE, Jui-Feng Yeh, and Yu-Sheng Lai TKDE, 2006

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Objective Methodology Experimental Results Conclusion

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation The conventional keyword information retrieval schemes cannot retrieve the semantically related questions.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objective  The study presents a method that can exactly pinpoint a user’s question category segment by structurally analyzing a sentence to find questions with the same meaning.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Methodology “ 我要如何治療感冒 ? ” QS parse tree-matching Answer similarity Question category segment similarity Keyword similarity

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Answer matcher Multistage ranking Strategy Answer similarity

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Experimental Results Determination of the Depth Effect Factor

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Experimental Results

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Conclusion  The system performance indicates significant improvements by comparing question categories embedded in questions, it has two problems that must be solved in the future: ─ Many out-of-grammar questions cannot be parsed successfully. ─ This study adopted the conventional VSM-based approach for answer matching.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 KS = keywords - interrogative question words QS = interrogative words + structural + semantic information Construction of the Question Semantic Grammar Keyword Segment Extraction G=(Σ,N, P, Q,P QS ) ΣA set of terminal words. NA set of nonterminal symbols, such as Q, NP, and VP. P A set of syntactic productions. (N ∪ Σ)* QA query sentence or a question. P QS A set of question category segment productions, each with the form β → QS: ζ

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 11 Question matcher – keyword segment matching and question category matching Question category segment similarity Keyword similarity Phrase to phrase Word to phrase Word to word Phrase to word

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 13 Feature-base similarity In the node-based aspect, each node denotes a unique concept and contains a certain quantity of information. In the edge-based aspect, the similarity between two concepts can be measured according to the summation of the traversed edge costs.