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        <title>Improving Opinion-Target Extraction with Character-Level Word Embeddings</title>
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            <title>Proceedings of the First Workshop on Subword and Character Level Models in NLP</title>
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        <name type="personal">
            <namePart type="given">Manaal</namePart>
            <namePart type="family">Faruqui</namePart>
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    <abstract>Fine-grained sentiment analysis is receiving increasing attention in recent years. Extracting opinion target expressions (OTE) in reviews is often an important step in fine-grained, aspect-based sentiment analysis. Retrieving this information from user-generated text, however, can be difficult. Customer reviews, for instance, are prone to contain misspelled words and are difficult to process due to their domain-specific language. In this work, we investigate whether character-level models can improve the performance for the identification of opinion target expressions. We integrate information about the character structure of a word into a sequence labeling system using character-level word embeddings and show their positive impact on the system’s performance. Specifically, we obtain an increase by 3.3 points F1-score with respect to our baseline model. In further experiments, we reveal encoded character patterns of the learned embeddings and give a nuanced view of the performance differences of both models.</abstract>
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%0 Conference Proceedings
%T Improving Opinion-Target Extraction with Character-Level Word Embeddings
%A Jebbara, Soufian
%A Cimiano, Philipp
%Y Faruqui, Manaal
%Y Schuetze, Hinrich
%Y Trancoso, Isabel
%Y Yaghoobzadeh, Yadollah
%S Proceedings of the First Workshop on Subword and Character Level Models in NLP
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F jebbara-cimiano-2017-improving
%X Fine-grained sentiment analysis is receiving increasing attention in recent years. Extracting opinion target expressions (OTE) in reviews is often an important step in fine-grained, aspect-based sentiment analysis. Retrieving this information from user-generated text, however, can be difficult. Customer reviews, for instance, are prone to contain misspelled words and are difficult to process due to their domain-specific language. In this work, we investigate whether character-level models can improve the performance for the identification of opinion target expressions. We integrate information about the character structure of a word into a sequence labeling system using character-level word embeddings and show their positive impact on the system’s performance. Specifically, we obtain an increase by 3.3 points F1-score with respect to our baseline model. In further experiments, we reveal encoded character patterns of the learned embeddings and give a nuanced view of the performance differences of both models.
%R 10.18653/v1/W17-4124
%U https://aclanthology.org/W17-4124
%U https://doi.org/10.18653/v1/W17-4124
%P 159-167

Markdown (Informal)

[Improving Opinion-Target Extraction with Character-Level Word Embeddings](https://aclanthology.org/W17-4124) (Jebbara & Cimiano, SCLeM 2017)

• Improving Opinion-Target Extraction with Character-Level Word Embeddings (Jebbara & Cimiano, SCLeM 2017)

ACL

• Soufian Jebbara and Philipp Cimiano. 2017. Improving Opinion-Target Extraction with Character-Level Word Embeddings. In Proceedings of the First Workshop on Subword and Character Level Models in NLP, pages 159–167, Copenhagen, Denmark. Association for Computational Linguistics.