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.- Anthology ID:
- W17-4124
- Volume:
- Proceedings of the First Workshop on Subword and Character Level Models in NLP
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Manaal Faruqui, Hinrich Schuetze, Isabel Trancoso, Yadollah Yaghoobzadeh
- Venue:
- SCLeM
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 159–167
- Language:
- URL:
- https://aclanthology.org/W17-4124
- DOI:
- 10.18653/v1/W17-4124
- Cite (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.
- Cite (Informal):
- Improving Opinion-Target Extraction with Character-Level Word Embeddings (Jebbara & Cimiano, SCLeM 2017)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W17-4124.pdf