Semantic representation learning for discourse processing

Semantics;Representation learning;Deep learning;Discourse;Discourse processing;Sentiment analysis

Ji, Yangfeng ; Eisenstein, Jacob Computer Science Boots, Byron Dyer, Chris Riedl, Mark Smith, Noah ; Eisenstein, Jacob

University:Georgia Institute of Technology

Department:Computer Science

关键词: Semantics;    Representation learning;    Deep learning;    Discourse;    Discourse processing;    Sentiment analysis;   

Others  :  https://smartech.gatech.edu/bitstream/1853/55636/1/JI-DISSERTATION-2016.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Discourse processing is to identify coherent relations, such as contrast and causal relation, from well-organized texts. The outcomes from discourse processing can benefit both research and applications in natural language processing, such as recognizing the major opinion from a product review, or evaluating the coherence of student writings. Identifying discourse relations from texts is an essential task of discourse processing. Relation identification requires intensive semantic understanding of texts, especially when no word (e.g., but) can signal the relations. Most prior work relies on sparse representation constructed from surface-form features (including, word pairs, POS tags, etc.), which fails to encode enough semantic information. As an alternative, I propose to use distributed representations of texts, which are dense vectors and flexible enough to share information efficiently. The goal of my work is to develop new models with representation learning for discourse processing. Specifically, I present a unified framework in this thesis to be able to learn both distributed representation and discourse models jointly.The joint training not only learns the discourse models, but also helps to shape the distributed representation for the discourse models. Such that, the learned representation could encode necessary semantic information to facilitate the processing tasks. The evaluation shows that our systems outperform prior work with only surface-form representations. In this thesis, I also discuss the possibility of extending the representation learning framework into some other problems in discourse processing. The problems studied include (1) How to use representation learning to build a discourse model with only distant supervision? The investigation of this problem will help to reduce the dependency of discourse processing on the annotated data; (2) How to combine discourse processing with other NLP tasks, such as language modeling? The exploration of this problem is expected to show the value of discourse information, and draw more attention to the research of discourse processing. As the end of this thesis, it also demonstrates the benefit of using discourse information for document-level machine translation and sentiment analysis.