Lr：以“'''desin:''' 1. a more detailed design of question classification 2. a more detailed design of keyword compensation 3. a more detailed design of word normalizati...”为内容创建页面

2014-10-03T07:20:49Z

以“'''desin:''' 1. a more detailed design of question classification 2. a more detailed design of keyword compensation 3. a more detailed design of word normalizati...”为内容创建页面

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'''desin:'''

1. a more detailed design of question classification

2. a more detailed design of keyword compensation

3. a more detailed design of word normalization, word expansion

'''PPT''' [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/3/3c/%E9%98%85%E8%AF%BB.pdf]

'''learn'''
* the word weight that computed using preme
* the translate model for similarity of word
* Queston answering with subgraph embeddings to learn the relation and entity matrix

'''paper:'''

1 Zhang, Guangzhi, et al. "The Architecture of ProMe Instant Question Answering System." Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2013 International Conference on. IEEE, 2013.

2 Park, Jungyeul, Jong Gun Lee, and Beatrice Daille. "UNPMC: Naive approach to extract keyphrases from scientific articles." Proceedings of the 5th international workshop on semantic evaluation. Association for Computational Linguistics, 2010.

3.Guangyou Zhou, Li Cai, Jun Zhao, and Kang 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 - Volume 1 (HLT '11), Vol. 1. Association for Computational Linguistics, Stroudsburg, PA, USA, 653-662.

4. Lei Zou, Ruizhe Huang, Haixun Wang, Jeffrey Xu Yu, Wenqiang He, and Dongyan Zhao. 2014. Natural language question answering over RDF: a graph data driven approach. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data (SIGMOD '14). ACM, New York, NY, USA, 313-324. DOI=10.1145/2588555.2610525 http://doi.acm.org/10.1145/2588555.2610525

4. Shekarpour, Saeedeh, et al. "SINA: Semantic interpretation of user queries for question answering on interlinked data." Web Semantics: Science, Services and Agents on the World Wide Web 2014).

5. Bordes, Antoine, Sumit Chopra, and Jason Weston. "Question Answering with Subgraph Embeddings." arXiv preprint arXiv:1406.3676 (2014).

6. Choi, Erik, Vanessa Kitzie, and Chirag Shah. "A machine learning-based approach to predicting success of questions on social question-answering." (2013).

7. iphaine Dalmas, Bonnie Webber, Answer comparison in automated question answering, Journal of Applied Logic, Volume 5, Issue 1, March 2007, Pages 104-120, ISSN 1570-8683, http://dx.doi.org/10.1016/j.jal.2005.12.002.

8. Zhou, Guangyou, et al. "Statistical Machine Translation Improves Question Retrieval in Community Question Answering via Matrix Factorization." ACL (1). 2013.

9. Sherzod Hakimov, Hakan Tunc, Marlen Akimaliev, and Erdogan Dogdu. 2013. Semantic question answering system over linked data using relational patterns. In Proceedings of the Joint EDBT/ICDT 2013 Workshops (EDBT '13). ACM, New York, NY, USA, 83-88. DOI=10.1145/2457317.2457331 http://doi.acm.org/10.1145/2457317.2457331

10. Wu, Youzheng, et al. "Leveraging Social Q&A Collections for Improving Complex Question Answering." Computer Speech & Language (2014).

11. Giannone, Cristina, Valentina Bellomaria, and Roberto Basili. "A HMM-based approach to question answering against linked data." Proceedings of the Question Answering over Linked Data lab (QALD-3) at CLEF (2013).

2014-08-22-qalr - 版本历史

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