Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction

Xin Li, Wai Lam


Abstract
We propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences. Two LSTMs equipped with extended memories and neural memory operations are designed for jointly handling the extraction tasks of aspects and opinions via memory interactions. Sentimental sentence constraint is also added for more accurate prediction via another LSTM. Experiment results over two benchmark datasets demonstrate the effectiveness of our framework.
Anthology ID:
D17-1310
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2886–2892
Language:
URL:
https://www.aclweb.org/anthology/D17-1310
DOI:
10.18653/v1/D17-1310
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1310.pdf
Video:
 https://vimeo.com/238232213