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Erschienen in:
HIMM: An HMM-Based Interactive Map-Matching System Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Semantic Definition Ranking

verfasst von : Zehui Hao, Zhongyuan Wang, Xiaofeng Meng, Jun Yan, Qiuyue Wang

Erschienen in: Database Systems for Advanced Applications

Verlag: Springer International Publishing

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Abstract

Question answering has been a focus of much attention from academia and industry. Search engines have already tried to provide direct answers for question-like queries. Among these queries, “What” is one of the biggest segments. Since results excerpted from Wikipedia often have a coverage problem, some models begin to rank definitions that are extracted from web documents, including Ranking SVM and Maximum Entropy Context Model. But they only adopt syntactic features and cannot understand definitions semantically. In this paper, we propose a language model incorporating knowledge bases to learn the regularities behind good definitions. It combines recurrent neural network based language model with a process of mapping words to context-appropriate concepts. Using the knowledge learnt from neural networks, we define two semantic features to evaluate definitions, one of which is confirmed to be effective by experiments. Results show that our model improves precision a lot. Our approach has been applied in production.

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Vorheriges Kapitel Towards a Query-Less News Search Framework on Twitter
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Fußnoten
1
The angle brackets mean a word and its concept.
 
2
If there is a contradiction among annotators, they will be asked to re-annotate the definition. If different opinions still exist, another two annotators will take part, and we will adopt the label given by most annotators.
 
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Metadaten
Titel
Semantic Definition Ranking
verfasst von
Zehui Hao
Zhongyuan Wang
Xiaofeng Meng
Jun Yan
Qiuyue Wang
Copyright-Jahr
2017
Buch
Database Systems for Advanced Applications

Print ISBN: 978-3-319-55698-7

Electronic ISBN: 978-3-319-55699-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-55699-4_10