Distant Supervision for Sentiment Attitude Extraction

Nicolay Rusnachenko, Natalia Loukachevitch, Elena Tutubalina


Abstract
News articles often convey attitudes between the mentioned subjects, which is essential for understanding the described situation. In this paper, we describe a new approach to distant supervision for extracting sentiment attitudes between named entities mentioned in texts. Two factors (pair-based and frame-based) were used to automatically label an extensive news collection, dubbed as RuAttitudes. The latter became a basis for adaptation and training convolutional architectures, including piecewise max pooling and full use of information across different sentences. The results show that models, trained with RuAttitudes, outperform ones that were trained with only supervised learning approach and achieve 13.4% increase in F1-score on RuSentRel collection.
Anthology ID:
R19-1118
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1022–1030
Language:
URL:
https://aclanthology.org/R19-1118
DOI:
10.26615/978-954-452-056-4_118
Bibkey:
Cite (ACL):
Nicolay Rusnachenko, Natalia Loukachevitch, and Elena Tutubalina. 2019. Distant Supervision for Sentiment Attitude Extraction. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1022–1030, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Distant Supervision for Sentiment Attitude Extraction (Rusnachenko et al., RANLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/R19-1118.pdf