Real, Live, and Concise: Answering Open-Domain Questions with Word Embedding and Summarization
暂无分享,去创建一个
[1] J. Shane Culpepper,et al. RMIT at the TREC 2015 LiveQA Track , 2015, TREC.
[2] Hiroya Takamura,et al. Text Summarization Model Based on Maximum Coverage Problem and its Variant , 2009, EACL.
[3] Tamer Elsayed,et al. QU-IR at SemEval 2016 Task 3: Learning to Rank on Arabic Community Question Answering Forums with Word Embedding , 2016, *SEMEVAL.
[4] Douglas W. Oard,et al. CLIP at TREC 2015: Microblog and LiveQA , 2015, TREC.
[5] Tamer Elsayed,et al. QU at TREC-2015: Building Real-Time Systems for Tweet Filtering and Question Answering , 2015, TREC.
[6] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[7] John D. Lafferty,et al. A study of smoothing methods for language models applied to Ad Hoc information retrieval , 2001, SIGIR '01.
[8] W. Bruce Croft,et al. Search Engines - Information Retrieval in Practice , 2009 .
[9] Mark Levene,et al. Search Engines: Information Retrieval in Practice , 2011, Comput. J..
[10] Tapas Kanungo,et al. Machine Learned Sentence Selection Strategies for Query-Biased Summarization , 2008 .
[11] Idan Szpektor,et al. Learning from the past: answering new questions with past answers , 2012, WWW.
[12] W. Bruce Croft,et al. Harnessing Semantics for Answer Sentence Retrieval , 2015, ESAIR@CIKM.
[13] Mihai Surdeanu,et al. Learning to Rank Answers on Large Online QA Collections , 2008, ACL.