A model for Anticipatory Event Detection

Event detection is a very important area of research that discovers new events reported in a stream of text documents. Previous research in event detection has largely focused on finding the first story and tracking the events of a specific topic. A topic is simply a set of related events defined by...

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Main Authors: HE, Qi, CHANG, Kuiyu, LIM, Ee Peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2006
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Online Access:https://ink.library.smu.edu.sg/sis_research/897
https://ink.library.smu.edu.sg/context/sis_research/article/1896/viewcontent/1116d3f50c86204ae98e5086d8ade80b38df.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-18962018-06-22T02:16:13Z A model for Anticipatory Event Detection HE, Qi CHANG, Kuiyu LIM, Ee Peng Event detection is a very important area of research that discovers new events reported in a stream of text documents. Previous research in event detection has largely focused on finding the first story and tracking the events of a specific topic. A topic is simply a set of related events defined by user supplied keywords with no associated semantics and little domain knowledge. We therefore introduce the Anticipatory Event Detection (AED) problem: given some user preferred event transition in a topic, detect the occurence of the transition for the stream of news covering the topic. We confine the events to come from the same application domain, in particular, mergers and acquisitions. Our experiments showed that classical cosine similarity method fails for the AED task, whereas our conceptual model-based approach, through the use of domain knowledge and named entity type assignments, seems promising. We show experimentally that an AED voting classifier operating on a vector representation with name entities replaced by types performed AED successfully. 2006-11-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/897 info:doi/10.1007/11901181_14 https://ink.library.smu.edu.sg/context/sis_research/article/1896/viewcontent/1116d3f50c86204ae98e5086d8ade80b38df.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Modeling Voting Conceptual analysis Semantics Keyword Anticipation Classification Similarity Tracking Streaming Information system Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Modeling
Voting
Conceptual analysis
Semantics
Keyword
Anticipation
Classification
Similarity
Tracking
Streaming
Information system
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Modeling
Voting
Conceptual analysis
Semantics
Keyword
Anticipation
Classification
Similarity
Tracking
Streaming
Information system
Databases and Information Systems
Numerical Analysis and Scientific Computing
HE, Qi
CHANG, Kuiyu
LIM, Ee Peng
A model for Anticipatory Event Detection
description Event detection is a very important area of research that discovers new events reported in a stream of text documents. Previous research in event detection has largely focused on finding the first story and tracking the events of a specific topic. A topic is simply a set of related events defined by user supplied keywords with no associated semantics and little domain knowledge. We therefore introduce the Anticipatory Event Detection (AED) problem: given some user preferred event transition in a topic, detect the occurence of the transition for the stream of news covering the topic. We confine the events to come from the same application domain, in particular, mergers and acquisitions. Our experiments showed that classical cosine similarity method fails for the AED task, whereas our conceptual model-based approach, through the use of domain knowledge and named entity type assignments, seems promising. We show experimentally that an AED voting classifier operating on a vector representation with name entities replaced by types performed AED successfully.
format text
author HE, Qi
CHANG, Kuiyu
LIM, Ee Peng
author_facet HE, Qi
CHANG, Kuiyu
LIM, Ee Peng
author_sort HE, Qi
title A model for Anticipatory Event Detection
title_short A model for Anticipatory Event Detection
title_full A model for Anticipatory Event Detection
title_fullStr A model for Anticipatory Event Detection
title_full_unstemmed A model for Anticipatory Event Detection
title_sort model for anticipatory event detection
publisher Institutional Knowledge at Singapore Management University
publishDate 2006
url https://ink.library.smu.edu.sg/sis_research/897
https://ink.library.smu.edu.sg/context/sis_research/article/1896/viewcontent/1116d3f50c86204ae98e5086d8ade80b38df.pdf
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