@inproceedings{wang-etal-2017-telecom,
title = "A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification",
author = "Wang, Jui-Yang and
Kuo, Min-Feng and
Han, Jen-Chieh and
Shih, Chao-Chuang and
Chen, Chun-Hsun and
Lee, Po-Ching and
Tsai, Richard Tzong-Han",
editor = "Park, Seong-Bae and
Supnithi, Thepchai",
booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
month = nov,
year = "2017",
address = "Tapei, Taiwan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/I17-3005",
pages = "17--20",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2017-telecom">
<titleInfo>
<title>A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jui-Yang</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Min-Feng</namePart>
<namePart type="family">Kuo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jen-Chieh</namePart>
<namePart type="family">Han</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chao-Chuang</namePart>
<namePart type="family">Shih</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chun-Hsun</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Po-Ching</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Richard</namePart>
<namePart type="given">Tzong-Han</namePart>
<namePart type="family">Tsai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the IJCNLP 2017, System Demonstrations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Seong-Bae</namePart>
<namePart type="family">Park</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thepchai</namePart>
<namePart type="family">Supnithi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Tapei, Taiwan</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">wang-etal-2017-telecom</identifier>
<location>
<url>https://aclanthology.org/I17-3005</url>
</location>
<part>
<date>2017-11</date>
<extent unit="page">
<start>17</start>
<end>20</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification
%A Wang, Jui-Yang
%A Kuo, Min-Feng
%A Han, Jen-Chieh
%A Shih, Chao-Chuang
%A Chen, Chun-Hsun
%A Lee, Po-Ching
%A Tsai, Richard Tzong-Han
%Y Park, Seong-Bae
%Y Supnithi, Thepchai
%S Proceedings of the IJCNLP 2017, System Demonstrations
%D 2017
%8 November
%I Association for Computational Linguistics
%C Tapei, Taiwan
%F wang-etal-2017-telecom
%X 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.
%U https://aclanthology.org/I17-3005
%P 17-20
Markdown (Informal)
[A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification](https://aclanthology.org/I17-3005) (Wang et al., IJCNLP 2017)
ACL
- Jui-Yang Wang, Min-Feng Kuo, Jen-Chieh Han, Chao-Chuang Shih, Chun-Hsun Chen, Po-Ching Lee, and Richard Tzong-Han Tsai. 2017. A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification. In Proceedings of the IJCNLP 2017, System Demonstrations, pages 17–20, Tapei, Taiwan. Association for Computational Linguistics.