Open Access
Mapping Dependencies Trees: An Application to Question Answering
TLDR
An approach for answer selection in a free form question answering task is described, representing both questions and candidate passages using dependency trees, and incorporating semantic information such as named entities in this representation.Abstract:
We describe an approach for answer selection in a free form question answering task. In order to go beyond the key-word based matching in selecting answers to questions, one would like to incorporate both syntactic and semantic information in the question answering process. We achieve this goal by representing both questions and candidate passages using dependency trees, and incorporating semantic information such as named entities in this representation. The sentence that best answers a question is determined to be the one that minimizes the generalized edit distance between it and the question tree, computed via an approximate tree matching algorithm. We evaluate the approach on question-answer pairs taken from previous TREC Q/A competitions. Preliminary experiments show its potential by significantly outperforming common bag-of-word scoring methods.read more
Citations
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ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
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What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA
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Proceedings ArticleDOI
A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering
Di Wang,Eric Nyberg +1 more
TL;DR: The proposed method uses a stacked bidirectional Long-Short Term Memory network to sequentially read words from question and answer sentences, and then outputs their relevance scores, which outperforms previous work which requires syntactic features and external knowledge resources.
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Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions
Michael Heilman,Noah A. Smith +1 more
TL;DR: A logistic regression model that uses 33 syntactic features of edit sequences to classify the sentence pairs and leads to competitive performance in recognizing textual entailment, paraphrase identification, and answer selection for question answering.
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Question Answering Using Enhanced Lexical Semantic Models
TL;DR: This work focuses on improving the performance using models of lexical semantic resources and shows that these systems can be consistently and significantly improved with rich lexical semantics information, regardless of the choice of learning algorithms.
References
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