@inproceedings{ffe8f48cb54041af8273d7d77a4f487c,
title = "Combine non-text features with deep learning structures based on attention-LSTM for answer selection",
abstract = "Because of the lexical gap between questions and answer candidates, methods with only word features cannot solve Answer Selection (AS) problem well. In this paper, we apply a LSTMs with Attention model to extract the latent semantic information of sentences and propose a method to learning non-text features. Besides, we propose an index to evaluate the sorting ability of models with the same accuracy value. Our model achieved the best accuracy and F1 performance than other known models, and the ranking index results, including MAP, AvgRec and MRR index{\textquoteright}s result, are after only KeLP system and Beihang MSRA system in SemEval-2017 Task 3 Subtask A.",
keywords = "Answer selection, Attention, LSTMs, Non-text features",
author = "Chang{\textquoteright}e Jia and Chengjie Sun and Bingquan Liu and Lei Lin",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 23rd China conference on Information Retrieval, CCIR 2017 ; Conference date: 13-07-2017 Through 14-07-2017",
year = "2017",
doi = "10.1007/978-3-319-68699-8\_21",
language = "英语",
isbn = "9783319686981",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "261--271",
editor = "Jianyun Nie and Tong Ruan and Tieyun Qian and Jirong Wen and Yiqun Liu",
booktitle = "Information Retrieval - 23rd China conference, CCIR 2017, Proceedings",
address = "德国",
}