Deep Learning in Lexical Analysis and Parsing

Wanxiang Che, Yue Zhang


Abstract
Neural networks, also with a fancy name deep learning, just right can overcome the above “feature engineering” problem. In theory, they can use non-linear activation functions and multiple layers to automatically find useful features. The novel network structures, such as convolutional or recurrent, help to reduce the difficulty further. These deep learning models have been successfully used for lexical analysis and parsing. In this tutorial, we will give a review of each line of work, by contrasting them with traditional statistical methods, and organizing them in consistent orders.
Anthology ID:
I17-5001
Volume:
Proceedings of the IJCNLP 2017, Tutorial Abstracts
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
1–2
URL:
https://www.aclweb.org/anthology/I17-5001
DOI:
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PDF:
https://www.aclweb.org/anthology/I17-5001.pdf