@inproceedings{a951dc3663c34f3698d964d84a70f265,
title = "Learning topic-oriented word embedding for query classification",
abstract = "In this paper, we propose a topic-oriented word embedding approach to address the query classification problem. First, the topic information is encoded to generate query categories. Then, the user clickthrough information is also incorporated in the modified word embedding algorithms. After that, the short and ambiguous queries are enriched to be classified in a supervised learning way. The unique contributions are that we present four neural network strategies based on the proposed model. The experiments are designed on two open data sets, namely Baidu and Sogou, which are two famous commercial search companies. Our evaluation results show that the proposed approach is promising on both large data sets. Under the four proposed strategies, we achieve the high performance as 95. 73\% in terms of Precision, 97. 79\% in terms of the F1 measure.",
keywords = "Query classification, Supervised learning, Word embedding, Word2vec",
author = "Hebin Yang and Qinmin Hu and Liang He",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 ; Conference date: 19-05-2015 Through 22-05-2015",
year = "2015",
doi = "10.1007/978-3-319-18038-0\_15",
language = "英语",
isbn = "9783319180373",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "188--198",
editor = "Tu-Bao Ho and Hiroshi Motoda and Hiroshi Motoda and Ee-Peng Lim and Tru Cao and David Cheung and Zhi-Hua Zhou",
booktitle = "Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings",
address = "德国",
}