@inproceedings{luo-etal-2018-extra,
title = "{E}xt{RA}: Extracting Prominent Review Aspects from Customer Feedback",
author = "Luo, Zhiyi and
Huang, Shanshan and
Xu, Frank F. and
Lin, Bill Yuchen and
Shi, Hanyuan and
Zhu, Kenny",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1384",
doi = "10.18653/v1/D18-1384",
pages = "3477--3486",
abstract = "Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost every day. In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews. The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.",
}
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<abstract>Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost every day. In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews. The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.</abstract>
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%0 Conference Proceedings
%T ExtRA: Extracting Prominent Review Aspects from Customer Feedback
%A Luo, Zhiyi
%A Huang, Shanshan
%A Xu, Frank F.
%A Lin, Bill Yuchen
%A Shi, Hanyuan
%A Zhu, Kenny
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F luo-etal-2018-extra
%X Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost every day. In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews. The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.
%R 10.18653/v1/D18-1384
%U https://aclanthology.org/D18-1384
%U https://doi.org/10.18653/v1/D18-1384
%P 3477-3486
Markdown (Informal)
[ExtRA: Extracting Prominent Review Aspects from Customer Feedback](https://aclanthology.org/D18-1384) (Luo et al., EMNLP 2018)
ACL
- Zhiyi Luo, Shanshan Huang, Frank F. Xu, Bill Yuchen Lin, Hanyuan Shi, and Kenny Zhu. 2018. ExtRA: Extracting Prominent Review Aspects from Customer Feedback. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3477–3486, Brussels, Belgium. Association for Computational Linguistics.