{"title":"基于注意力的Bi-LSTM网络的端到端答案选择","authors":"Yuqi Ren, Tongxuan Zhang, Xikai Liu, Hongfei Lin","doi":"10.1109/HOTICN.2018.8606015","DOIUrl":null,"url":null,"abstract":"Many people ask medical questions online, finding the most suitable answer from candidate answers is an important research area in health care. The IEEE HotICN Knowledge Graph Academic Competition given a question and several candidate answers, then sort the candidate answers to get the best answer. We treated this subtask as a binary classification task, sorted the answers by calculating similarity between the question and each answer. In this work, we proposed a neural selection model trained on the training dataset. Our network architecture is based on the combination of Bi-LSTM and Attention mechanism, extended with biomedical word embeddings. Based on this fact, our model achieve state-of-the-art results on answer selection of medical community.","PeriodicalId":243749,"journal":{"name":"2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"End-to-end Answer Selection via Attention-Based Bi-LSTM Network\",\"authors\":\"Yuqi Ren, Tongxuan Zhang, Xikai Liu, Hongfei Lin\",\"doi\":\"10.1109/HOTICN.2018.8606015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many people ask medical questions online, finding the most suitable answer from candidate answers is an important research area in health care. The IEEE HotICN Knowledge Graph Academic Competition given a question and several candidate answers, then sort the candidate answers to get the best answer. We treated this subtask as a binary classification task, sorted the answers by calculating similarity between the question and each answer. In this work, we proposed a neural selection model trained on the training dataset. Our network architecture is based on the combination of Bi-LSTM and Attention mechanism, extended with biomedical word embeddings. Based on this fact, our model achieve state-of-the-art results on answer selection of medical community.\",\"PeriodicalId\":243749,\"journal\":{\"name\":\"2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HOTICN.2018.8606015\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HOTICN.2018.8606015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
End-to-end Answer Selection via Attention-Based Bi-LSTM Network
Many people ask medical questions online, finding the most suitable answer from candidate answers is an important research area in health care. The IEEE HotICN Knowledge Graph Academic Competition given a question and several candidate answers, then sort the candidate answers to get the best answer. We treated this subtask as a binary classification task, sorted the answers by calculating similarity between the question and each answer. In this work, we proposed a neural selection model trained on the training dataset. Our network architecture is based on the combination of Bi-LSTM and Attention mechanism, extended with biomedical word embeddings. Based on this fact, our model achieve state-of-the-art results on answer selection of medical community.