@inproceedings{45cf7999b3514cc9899025958ef54ee5,
title = "A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification",
abstract = "In the paper, we propose an information retrieval based (IR-based) Question Answering (QA) system to assist online customer service staffs respond users in the telecom domain. When user asks a question, the system retrieves a set of relevant answers and ranks them. Moreover, our system uses a novel reranker to enhance the ranking result of information retrieval. It employs the word2vec model to represent the sentences as vectors. It also uses a sub-category feature, predicted by the k-nearest neighbor algorithm. Finally, the system returns the top five candidate answers, making online staffs find answers much more efficiently.",
author = "Wang, {Jui Yang} and Kuo, {Ming Feng} and Han, {Jen Chieh} and Shih, {Chao Chuang} and Chen, {Chun Hsun} and Lee, {Po Ching} and Tsai, {Richard Tzong Han}",
note = "Publisher Copyright: {\textcopyright} 2017 AFNLP; 8th International Joint Conference on Natural Language Processing, IJCNLP 2017 ; Conference date: 27-11-2017 Through 01-12-2017",
year = "2017",
language = "???core.languages.en_GB???",
series = "8th International Joint Conference on Natural Language Processing - Proceedings of the IJCNLP 2017, System Demonstrations",
publisher = "Association for Computational Linguistics (ACL)",
pages = "17--20",
booktitle = "8th International Joint Conference on Natural Language Processing - Proceedings of the IJCNLP 2017, System Demonstrations",
}