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Information extraction of +/-effect events to support opinion inference

Choi, Yoonjung (2017) Information extraction of +/-effect events to support opinion inference. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Recently, work in NLP was initiated on a type of opinion inference that arises when opinions are expressed toward events which have positive or negative effects on entities, called +/-effect events. The ultimate goal is to develop a fully automatic system capable of recognizing inferred attitudes. To achieve its results, the inference system requires all instances of +/-effect events. Therefore, this dissertation focuses on +/-effect events to support opinion inference. To extract +/-effect events, we first need the list of +/-effect events. Due to significant sense ambiguity, our goal is to develop a sense-level rather than word-level lexicon. To handle sense-level information, WordNet is adopted. We adopt a graph-based method which is seeded by entries culled from FrameNet and then expanded by exploiting semantic relations in WordNet. We show that WordNet relations are useful for the polarity propagation in the graph model. In addition, to maximize the effectiveness of different types of information, we combine a graph-based method using WordNet relations and a standard classifier using gloss information. Further, we provide evidence that the model is an effective way to guide manual annotation to find +/-effect senses that are not in the seed set. To exploit the sense-level lexicons, we have to carry out word sense disambiguation. We present a knowledge-based +/-effect coarse-grained word sense disambiguation method based on selectional preferences via topic models. For more information, we first group senses, and then utilize topic models to model selectional preferences. Our experiments show that selectional preferences are helpful in our work. To support opinion inferences, we need to identify not only +/-effect events but also their affected entities automatically. Thus, we address both +/-effect event detection and affected entity identification. Since +/-effect events and their affected entities are closely related, instead of a pipeline system, we present a joint model to extract +/-effect events and their affected entities simultaneously. We demonstrate that our joint model is promising to extract +/-effect events and their affected entities jointly.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Choi, Yoonjungyjchoi@cs.pitt.eduyoc25
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWiebe,
Committee CoChairLitman,
Committee MemberHauskrecht,
Committee MemberHwa,
Committee MemberWarren,
Date: 19 January 2017
Date Type: Publication
Defense Date: 29 August 2016
Approval Date: 19 January 2017
Submission Date: 17 November 2016
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 176
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Sentiment Analysis, Implicit Opinion, Opinion Inference, Lexical Acquisition, Word Sense Disambiguation
Date Deposited: 19 Jan 2017 21:05
Last Modified: 20 Jan 2017 06:15


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