Improving Opinion-Target Extraction with Character-Level Word Embeddings

Soufian Jebbara, Philipp Cimiano


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W17-4124.pdf