@inproceedings{xiong-etal-2016-distance,
title = "Distance Metric Learning for Aspect Phrase Grouping",
author = "Xiong, Shufeng and
Zhang, Yue and
Ji, Donghong and
Lou, Yinxia",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1235",
pages = "2492--2502",
abstract = "Aspect phrase grouping is an important task in aspect-level sentiment analysis. It is a challenging problem due to polysemy and context dependency. We propose an Attention-based Deep Distance Metric Learning (ADDML) method, by considering aspect phrase representation as well as context representation. First, leveraging the characteristics of the review text, we automatically generate aspect phrase sample pairs for distant supervision. Second, we feed word embeddings of aspect phrases and their contexts into an attention-based neural network to learn feature representation of contexts. Both aspect phrase embedding and context embedding are used to learn a deep feature subspace for measure the distances between aspect phrases for K-means clustering. Experiments on four review datasets show that the proposed method outperforms state-of-the-art strong baseline methods.",
}
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<abstract>Aspect phrase grouping is an important task in aspect-level sentiment analysis. It is a challenging problem due to polysemy and context dependency. We propose an Attention-based Deep Distance Metric Learning (ADDML) method, by considering aspect phrase representation as well as context representation. First, leveraging the characteristics of the review text, we automatically generate aspect phrase sample pairs for distant supervision. Second, we feed word embeddings of aspect phrases and their contexts into an attention-based neural network to learn feature representation of contexts. Both aspect phrase embedding and context embedding are used to learn a deep feature subspace for measure the distances between aspect phrases for K-means clustering. Experiments on four review datasets show that the proposed method outperforms state-of-the-art strong baseline methods.</abstract>
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%0 Conference Proceedings
%T Distance Metric Learning for Aspect Phrase Grouping
%A Xiong, Shufeng
%A Zhang, Yue
%A Ji, Donghong
%A Lou, Yinxia
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F xiong-etal-2016-distance
%X Aspect phrase grouping is an important task in aspect-level sentiment analysis. It is a challenging problem due to polysemy and context dependency. We propose an Attention-based Deep Distance Metric Learning (ADDML) method, by considering aspect phrase representation as well as context representation. First, leveraging the characteristics of the review text, we automatically generate aspect phrase sample pairs for distant supervision. Second, we feed word embeddings of aspect phrases and their contexts into an attention-based neural network to learn feature representation of contexts. Both aspect phrase embedding and context embedding are used to learn a deep feature subspace for measure the distances between aspect phrases for K-means clustering. Experiments on four review datasets show that the proposed method outperforms state-of-the-art strong baseline methods.
%U https://aclanthology.org/C16-1235
%P 2492-2502
Markdown (Informal)
[Distance Metric Learning for Aspect Phrase Grouping](https://aclanthology.org/C16-1235) (Xiong et al., COLING 2016)
ACL
- Shufeng Xiong, Yue Zhang, Donghong Ji, and Yinxia Lou. 2016. Distance Metric Learning for Aspect Phrase Grouping. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2492–2502, Osaka, Japan. The COLING 2016 Organizing Committee.