Event network embedding

Zeng, Qi

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https://hdl.handle.net/2142/114027 Copy

Description

Title

Event network embedding

Author(s)

Zeng, Qi

Issue Date

2021-12-09

Director of Research (if dissertation) or Advisor (if thesis)

Ji, Heng

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Date of Ingest

2022-04-29T21:47:48Z

Keyword(s)

natural language processing

Abstract

Current methods for event representation ignore related events in a corpus-level global context. For a deep and comprehensive understanding of complex events, we introduce a new task, Event Network Embedding, which aims to represent events by capturing the connections among events. We propose a novel framework, Global Event Network Embedding (GENE), that encodes the event network with a multi-view graph encoder while preserving the graph topology and node semantics. The graph encoder is trained by minimizing both structural and semantic losses. We develop a new series of structured probing tasks, and show that our approach effectively outperforms baseline models on node typing, argument role classification, and event coreference resolution. As a direct application, We introduce a new task, Unsupervised Event Schema Graph Matching, which aims to align event instance graphs and event schema graphs by finding node correspondence. We develop the first benchmark and collect a dataset of 3,740 event instance graphs from IED-scenario news articles, 75 of which are paired with 4 event schema graphs by human annotation. Our analysis on this task sheds light on the shortcomings of current state-of-the-art models on this event understanding task.

Graduation Semester

2021-12

Type of Resource

Thesis

Permalink

http://hdl.handle.net/2142/114027

Copyright and License Information

Copyright 2021 Qi Zeng

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