Abstract
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).
- A. Abdi, N. Idris, and Z. Ahmad. 2018. QAPD: An ontology-based question answering system in the physics domain. Soft Computing 22, 1 (2018), 213. Google Scholar
Digital Library
- A. Figueroa and G. Neumann. 2016. Context-aware semantic classification of search queries for browsing community question-answering archives. Knowledge-Based Systems 96 (2016), 1–13. Google Scholar
Digital Library
- G. Zhou, Y. Zhou, T. He, and W. Wu. 2016. Learning semantic representation with neural networks for community question answering retrieval. Knowledge-Based Systems 93 (2016), 75–83. Google Scholar
Digital Library
- T. Hao, W. Xie, Q. Wu, H. Weng, and Y. Qu. 2017. Leveraging question target word features through semantic relation expansion for answer type classification. Knowledge-Based Systems 133 (2017), 43–52. Google Scholar
Digital Library
- C.-H. Wu, C.-H. Liu, and P.-H. Su. 2015. Sentence extraction with topic modeling for question-answer pair generation. Soft Computing 19, 1 (2015), 39–46. Google Scholar
Digital Library
- D. Bahdanau, K. Cho, and Y. Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473.Google Scholar
- K. Cho, B. V. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078.Google Scholar
- I. Sutskever, O. Vinyals, and Q. V. Le. 2014. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, Vol. 3104. Curran Associates, Red Hook, NY, 1–9. Google Scholar
Digital Library
- J. Zhao, J. X. Huang, and B. He. 2011. CRTER: Using cross terms to enhance probabilistic information retrieval. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, 155–164. Google Scholar
Digital Library
- M. Wang, N. A. Smith, and T. Mitamura. 2007. What is the Jeopardy model? A quasi-synchronous grammar for QA. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL’07). 1–11.Google Scholar
- M. Heilman and N. A. Smith. 2010. Tree edit models for recognizing textual entailments, paraphrases, and answers to questions. In Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. 1011–1019. Google Scholar
Digital Library
- J. Jeon, W. B. Croft, and J. H. Lee. 2005. Finding similar questions in large question and answer archives. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management. ACM, New York, NY, 84–90. Google Scholar
Digital Library
- G. Zhou, L. Cai, J. Zhao, and K. Liu. 2011. Phrase-based translation model for question retrieval in community question answer archives. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 653–662. Google Scholar
Digital Library
- C. D. Santos, M. Tan, B. Xiang, and B. Zhou. Attentive pooling networks. arXiv:1602.03609.Google Scholar
- M. Tan, C. Santos, B. Xiang, and B. Zhou. 2015. LSTM-based deep learning models for non-factoid answer selection. arXiv:1511.04108.Google Scholar
- B. Wang, K. Liu, and J. Zhao. 2016. Inner attention based recurrent neural networks for answer selection. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1288–1297. Google Scholar
Digital Library
- Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy. 2016. Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the Association for Computational Linguistics: Human Language Technologies. 1480–1489.Google Scholar
- H. Li, M. Min, Y. Ge, and A. Kadav. 2017. A context-aware attention network for interactive question answering. arXiv:1612.07411. Google Scholar
Digital Library
- B. Liu, X. An, and J. X. Huang. 2015. Using term location information to enhance probabilistic information retrieval. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 883–886. Google Scholar
Digital Library
- Y. Zhang, Y. Wang, G. Zhou, J. Jin, B. Wang, and X. Wang. 2018. Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. Expert Systems with Applications 96 (2018), 302–310 Google Scholar
Digital Library
- D. Wang and E. Nyberg. 2015. A long short-term memory model for answer sentence selection in question answering. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 707–712.Google Scholar
- M. D. Zeiler. 2012. ADADELTA: An adaptive learning rate method. arXiv:1212.5701.Google Scholar
Index Terms
Bi-directional Long Short-Term Memory Model with Semantic Positional Attention for the Question Answering System
Recommendations
Bi-directional LSTM Model with Symptoms-Frequency Position Attention for Question Answering System in Medical Domain
AbstractOnline medical intelligent question answering system plays an increasingly important role as a supplement of the traditional medical service systems. The purpose is to provide quick and concise feedback on users’ questions through natural ...
Question answering system based on ontology and semantic web
RSKT'08: Proceedings of the 3rd international conference on Rough sets and knowledge technologySemantic web and ontology are the key technologies of Question Answering system. Ontology is becoming the pivotal methodology to represent domain-specific conceptual knowledge in order to promote the semantic capability of a QA system. In this paper we ...
Semantic question answering system over linked data using relational patterns
EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 WorkshopsQuestion answering is the task of answering questions in natural language. Linked Data project and Semantic Web community made it possible for us to query structured knowledge bases like DBpedia and YAGO. Only expert users, however, with the knowledge ...






Comments