Semantic Representation and Ontology Construction in the Question Answering System

2007 ◽  
Author(s):  
Ying-Hong Wang ◽  
Wen-Nan Wang ◽  
Chu-Chi Huang ◽  
Ting-Wei Chang ◽  
Yi-Hsiang Yen
Keyword(s):  
Question Answering ◽  
Semantic Representation ◽  
Ontology Construction ◽  
Question Answering System

Toward Automatic Answers in User-Interactive Question Answering Systems

2013 ◽  
pp. 88-101
Author(s):  
Tianyong Hao ◽  
Feifei Xu ◽  
Jingsheng Lei ◽  
Liu Wenyin ◽  
Qing Li
Keyword(s):  
Pattern Matching ◽  
Similar Pattern ◽  
Question Answering ◽  
Semantic Representation ◽  
Pattern Generation ◽  
Interactive Systems ◽  
Question Answering System ◽  
Semantic Annotations ◽  
Question Answering Systems ◽  
Interactive Question

A strategy of automatic answer retrieval for repeated or similar questions in user-interactive systems by employing semantic question patterns is proposed in this paper. The used semantic question pattern is a generalized representation of a group of questions with both similar structure and relevant semantics. Specifically, it consists of semantic annotations (or constraints) for the variable components in the pattern and hence enhances the semantic representation and greatly reduces the ambiguity of a question instance when asked by a user using such pattern. The proposed method consists of four major steps: structure processing, similar pattern matching and filtering, automatic pattern generation, question similarity evaluation and answer retrieval. Preliminary experiments in a real question answering system show a precision of more than 90% of the method.

Toward Automatic Answers in User-Interactive Question Answering Systems

2011 ◽  
Vol 3 (4) ◽  
pp. 52-66 ◽  
Author(s):  
Tianyong Hao ◽  
Feifei Xu ◽  
Jingsheng Lei ◽  
Liu Wenyin ◽  
Qing Li
Keyword(s):  
Pattern Matching ◽  
Similar Pattern ◽  
Question Answering ◽  
Semantic Representation ◽  
Pattern Generation ◽  
Interactive Systems ◽  
Question Answering System ◽  
Semantic Annotations ◽  
Question Answering Systems ◽  
Interactive Question

A strategy of automatic answer retrieval for repeated or similar questions in user-interactive systems by employing semantic question patterns is proposed in this paper. The used semantic question pattern is a generalized representation of a group of questions with both similar structure and relevant semantics. Specifically, it consists of semantic annotations (or constraints) for the variable components in the pattern and hence enhances the semantic representation and greatly reduces the ambiguity of a question instance when asked by a user using such pattern. The proposed method consists of four major steps: structure processing, similar pattern matching and filtering, automatic pattern generation, question similarity evaluation and answer retrieval. Preliminary experiments in a real question answering system show a precision of more than 90% of the method.

scholarly journals Bi-directional Long Short-Term Memory Model with Semantic Positional Attention for the Question Answering System

2021 ◽  
Vol 20 (5) ◽  
pp. 1-13
Author(s):  
Mingwen Bi ◽  
Qingchuan Zhang ◽  
Min Zuo ◽  
Zelong Xu ◽  
Qingyu Jin
Keyword(s):  
Question Answering ◽  
Short Term Memory ◽  
Semantic Representation ◽  
Semantic Mapping ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Position Information ◽  
Question Answering System ◽  
Questions And Answers ◽  
Mapping Mechanism

The intelligent question answering system aims to provide quick and concise feedback on the questions of users. Although the performance of phrase-level and numerous attention models have been improved, the sentence components and position information are not emphasized enough. This article combines Ci-Lin and word2vec to divide all of the words in the question-answer pairs into groups according to the semantics and select one kernel word in each group. The remaining words are common words and realize the semantic mapping mechanism between kernel words and common words. With this Chinese semantic mapping mechanism, the common words in all questions and answers are replaced by the semantic kernel words to realize the normalization of the semantic representation. Meanwhile, based on the bi-directional LSTM model, this article introduces a method of the combination of semantic role labeling and positional context, dividing the sentence into multiple semantic segments according to semantic logic. The weight is given to the neighboring words in the same semantic segment and propose semantic role labeling position attention based on the bi-directional LSTM model (BLSTM-SRLP). The good performance of the BLSTM-SRLP model has been demonstrated in comparative experiments on the food safety field dataset (FS-QA).

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