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        <title>Hybrid Models for Aspects Extraction without Labelled Dataset</title>
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        <namePart type="given">Wai-Howe</namePart>
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            <title>Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)</title>
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            <namePart type="given">James</namePart>
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    <abstract>One of the important tasks in opinion mining is to extract aspects of the opinion target. Aspects are features or characteristics of the opinion target that are being reviewed, which can be categorised into explicit and implicit aspects. Extracting aspects from opinions is essential in order to ensure accurate information about certain attributes of an opinion target is retrieved. For instance, a professional camera receives a positive feedback in terms of its functionalities in a review, but its overly high price receives negative feedback. Most of the existing solutions focus on explicit aspects. However, sentences in reviews normally do not state the aspects explicitly. In this research, two hybrid models are proposed to identify and extract both explicit and implicit aspects, namely TDM-DC and TDM-TED. The proposed models combine topic modelling and dictionary-based approach. The models are unsupervised as they do not require any labelled dataset. The experimental results show that TDM-DC achieves F1-measure of 58.70%, where it outperforms both the baseline topic model and dictionary-based approach. In comparison to other existing unsupervised techniques, the proposed models are able to achieve higher F1-measure by approximately 3%. Although the supervised techniques perform slightly better, the proposed models are domain-independent, and hence more versatile.</abstract>
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    <identifier type="doi">10.18653/v1/D19-6611</identifier>
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%0 Conference Proceedings
%T Hybrid Models for Aspects Extraction without Labelled Dataset
%A Khong, Wai-Howe
%A Soon, Lay-Ki
%A Goh, Hui-Ngo
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F khong-etal-2019-hybrid
%X One of the important tasks in opinion mining is to extract aspects of the opinion target. Aspects are features or characteristics of the opinion target that are being reviewed, which can be categorised into explicit and implicit aspects. Extracting aspects from opinions is essential in order to ensure accurate information about certain attributes of an opinion target is retrieved. For instance, a professional camera receives a positive feedback in terms of its functionalities in a review, but its overly high price receives negative feedback. Most of the existing solutions focus on explicit aspects. However, sentences in reviews normally do not state the aspects explicitly. In this research, two hybrid models are proposed to identify and extract both explicit and implicit aspects, namely TDM-DC and TDM-TED. The proposed models combine topic modelling and dictionary-based approach. The models are unsupervised as they do not require any labelled dataset. The experimental results show that TDM-DC achieves F1-measure of 58.70%, where it outperforms both the baseline topic model and dictionary-based approach. In comparison to other existing unsupervised techniques, the proposed models are able to achieve higher F1-measure by approximately 3%. Although the supervised techniques perform slightly better, the proposed models are domain-independent, and hence more versatile.
%R 10.18653/v1/D19-6611
%U https://aclanthology.org/D19-6611
%U https://doi.org/10.18653/v1/D19-6611
%P 63-68

Markdown (Informal)

[Hybrid Models for Aspects Extraction without Labelled Dataset](https://aclanthology.org/D19-6611) (Khong et al., 2019)

• Hybrid Models for Aspects Extraction without Labelled Dataset (Khong et al., 2019)

ACL

• Wai-Howe Khong, Lay-Ki Soon, and Hui-Ngo Goh. 2019. Hybrid Models for Aspects Extraction without Labelled Dataset. In Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), pages 63–68, Hong Kong, China. Association for Computational Linguistics.