@inproceedings{akhtar-etal-2019-language,
title = "Language-Agnostic Model for Aspect-Based Sentiment Analysis",
author = "Akhtar, Md Shad and
Kumar, Abhishek and
Ekbal, Asif and
Biemann, Chris and
Bhattacharyya, Pushpak",
editor = "Dobnik, Simon and
Chatzikyriakidis, Stergios and
Demberg, Vera",
booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
month = may,
year = "2019",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-0413",
doi = "10.18653/v1/W19-0413",
pages = "154--164",
abstract = "In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis. The proposed approach is based on Bidirectional Long Short-Term Memory (Bi-LSTM) network, which is further assisted with extra hand-crafted features. We define three different architectures for the successful combination of word embeddings and hand-crafted features. We evaluate the proposed approach for six languages (i.e. English, Spanish, French, Dutch, German and Hindi) and two problems (i.e. aspect term extraction and aspect sentiment classification). Experiments show that the proposed model attains state-of-the-art performance in most of the settings.",
}
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<abstract>In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis. The proposed approach is based on Bidirectional Long Short-Term Memory (Bi-LSTM) network, which is further assisted with extra hand-crafted features. We define three different architectures for the successful combination of word embeddings and hand-crafted features. We evaluate the proposed approach for six languages (i.e. English, Spanish, French, Dutch, German and Hindi) and two problems (i.e. aspect term extraction and aspect sentiment classification). Experiments show that the proposed model attains state-of-the-art performance in most of the settings.</abstract>
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%0 Conference Proceedings
%T Language-Agnostic Model for Aspect-Based Sentiment Analysis
%A Akhtar, Md Shad
%A Kumar, Abhishek
%A Ekbal, Asif
%A Biemann, Chris
%A Bhattacharyya, Pushpak
%Y Dobnik, Simon
%Y Chatzikyriakidis, Stergios
%Y Demberg, Vera
%S Proceedings of the 13th International Conference on Computational Semantics - Long Papers
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F akhtar-etal-2019-language
%X In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis. The proposed approach is based on Bidirectional Long Short-Term Memory (Bi-LSTM) network, which is further assisted with extra hand-crafted features. We define three different architectures for the successful combination of word embeddings and hand-crafted features. We evaluate the proposed approach for six languages (i.e. English, Spanish, French, Dutch, German and Hindi) and two problems (i.e. aspect term extraction and aspect sentiment classification). Experiments show that the proposed model attains state-of-the-art performance in most of the settings.
%R 10.18653/v1/W19-0413
%U https://aclanthology.org/W19-0413
%U https://doi.org/10.18653/v1/W19-0413
%P 154-164
Markdown (Informal)
[Language-Agnostic Model for Aspect-Based Sentiment Analysis](https://aclanthology.org/W19-0413) (Akhtar et al., IWCS 2019)
ACL
- Md Shad Akhtar, Abhishek Kumar, Asif Ekbal, Chris Biemann, and Pushpak Bhattacharyya. 2019. Language-Agnostic Model for Aspect-Based Sentiment Analysis. In Proceedings of the 13th International Conference on Computational Semantics - Long Papers, pages 154–164, Gothenburg, Sweden. Association for Computational Linguistics.