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Radical Enhanced Chinese Word Embedding Read chapter Read first chapter

2018 | OriginalPaper | Chapter

TSABCNN: Two-Stage Attention-Based Convolutional Neural Network for Frame Identification

Authors : Hongyan Zhao, Ru Li, Fei Duan, Zepeng Wu, Shaoru Guo

Published in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Publisher: Springer International Publishing

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Abstract

As an essential sub-task of frame-semantic parsing, Frame Identification (FI) is a fundamentally important research topic in shallow semantic parsing. However, most existing work is based on sophisticated, hand-crafted features which might not be compatible with FI procedure. Besides that, they usually heavily rely on available natural language processing (NLP) toolkits and various lexical resources. Thus existing methods with hand-crafted features may not achieve satisfactory performance. In this paper, we propose a two-stage attention-based convolutional neural network (TSABCNN) to alleviate this problem and capture the most important context features for FI task. In order to dynamically adjust the weight of each feature, we build two levels of attention over instances at input layer and pooling layer respectively. Furthermore, the proposed model is an end-to-end learning framework which does not need any complicated NLP toolkits and feature engineering, and can be applied to any language. Experiments results on FrameNet and Chinese FrameNet (CFN) show the effectiveness of the proposed approach for the FI task.

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Metadata
Title
TSABCNN: Two-Stage Attention-Based Convolutional Neural Network for Frame Identification
Authors
Hongyan Zhao
Ru Li
Fei Duan
Zepeng Wu
Shaoru Guo
Copyright Year
2018
Book
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Print ISBN: 978-3-030-01715-6

Electronic ISBN: 978-3-030-01716-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01716-3_24

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