@inbook{f1bf75b8bb5c4e88b0f42ee750b39fe6,
title = "An open domain topic prediction model for answer selection",
abstract = "We present an open domain topic prediction model for the answer selection task. Different from previous unsupervised topic modeling methods, we automatically extract high quality and large scale 〈sentence, topic〉 pairs from Wikipedia as labeled data, and train an open domain topic prediction model based on convolutional neural network, which can predict the most possible topics for each given input sentence. To verify the usefulness of our proposed approach, we add the topic prediction model into an end-to-end open domain question answering system and evaluate it on the answer selection task, and improvements are obtained on both WikiQA and QASent datasets.",
keywords = "Answer selection, Question answering, Topic prediction",
author = "Zhao Yan and Nan Duan and Ming Zhou and Zhoujun Li and Jianshe Zhou",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",
year = "2016",
month = dec,
day = "1",
doi = "10.1007/978-3-319-50496-4\_26",
language = "英语",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "312--323",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "德国",
}