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Erschienen in:
Parallel Collaborative Filtering Recommendation Model Based on Two-Phase Similarity Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Leveraging Semantic Labeling for Question Matching to Facilitate Question-Answer Archive Reuse

verfasst von : Tianyong Hao, Xinying Qiu, Shengyi Jiang

Erschienen in: Intelligent Computing Theories and Methodologies

Verlag: Springer International Publishing

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Abstract

A new question representation method is proposed for automated question matching over accumulated question-answer data archive. The representation defines four kinds of question words as question-type words, user-centered words, shareable-pattern words, and irrelevant words for question analysis. These question words are further annotated by a semantic labeling ontology to enhance the semantic representation for the purpose of word ambiguity reduction. We tested the matching precision on 5,000 questions with respect to various generators and the result demonstrated the stability of the method. We further compared the method with Cosine similarity and WordNet-based semantic similarity as baselines on a standard TREC dataset containing 5,536 questions. The results presented that our method improved MRR by 8.6 % and accuracy by 9.6 % on average, indicating its effectiveness.

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Vorheriges Kapitel The Chaotic Measurement Matrix for Compressed Sensing
Nächstes Kapitel How to Detect Communities in Large Networks
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Metadaten
Titel
Leveraging Semantic Labeling for Question Matching to Facilitate Question-Answer Archive Reuse
verfasst von
Tianyong Hao
Xinying Qiu
Shengyi Jiang
Copyright-Jahr
2015
Buch
Intelligent Computing Theories and Methodologies

Print ISBN: 978-3-319-22179-3

Electronic ISBN: 978-3-319-22180-9

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-22180-9_7