Event extraction and classification by neural network model

Bamfa Ceesay; Wen Juan Hou

doi:10.1007/978-3-319-42007-3_20

Event extraction and classification by neural network model

Bamfa Ceesay, Wen Juan Hou^*

^*Corresponding author for this work

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

To understand and automatically extract information about events presented in a text, semantically meaningful units expressing these events are important. Extracting events and classifying them into event types and subtypes using Natural Language Processing techniques poses a challenging research problem. There is no clear-cut definitions to what an event from a text is and what the optimal representation of semantic units within a given text is. In addition, events in a text can be classified into types and subtypes of events; and a single event can have multiple mentions in a given sentence. In this paper, we propose a model to determine events within a given text and classify them into event types or subtypes and REALIS by the distributional semantic role labeling and neural embedding techniques. For the task of the event nugget detection, we trained a three-layer network to determine the event mentions from texts achieving F1-score of 77.37 % for macro average and 71.10 % for micro average, respectively.

Original language	English
Title of host publication	Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings
Editors	Moonis Ali, Hamido Fujita, Jun Sasaki, Masaki Kurematsu, Ali Selamat
Publisher	Springer Verlag
Pages	229-241
Number of pages	13
ISBN (Print)	9783319420066
DOIs	https://doi.org/10.1007/978-3-319-42007-3_20
Publication status	Published - 2016
Event	29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016 - Morioka, Japan Duration: 2016 Aug 2 → 2016 Aug 4

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9799
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016
Country/Territory	Japan
City	Morioka
Period	2016/08/02 → 2016/08/04

Keywords

Event classification
Event extraction
Event nuggets
Neural embedding
Semantic role labeling

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-42007-3_20

Cite this

Ceesay, B., & Hou, W. J. (2016). Event extraction and classification by neural network model. In M. Ali, H. Fujita, J. Sasaki, M. Kurematsu, & A. Selamat (Eds.), Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings (pp. 229-241). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9799). Springer Verlag. https://doi.org/10.1007/978-3-319-42007-3_20

Event extraction and classification by neural network model. / Ceesay, Bamfa; Hou, Wen Juan.
Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. ed. / Moonis Ali; Hamido Fujita; Jun Sasaki; Masaki Kurematsu; Ali Selamat. Springer Verlag, 2016. p. 229-241 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9799).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ceesay, B & Hou, WJ 2016, Event extraction and classification by neural network model. in M Ali, H Fujita, J Sasaki, M Kurematsu & A Selamat (eds), Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9799, Springer Verlag, pp. 229-241, 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, 2016/08/02. https://doi.org/10.1007/978-3-319-42007-3_20

Ceesay B, Hou WJ. Event extraction and classification by neural network model. In Ali M, Fujita H, Sasaki J, Kurematsu M, Selamat A, editors, Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. Springer Verlag. 2016. p. 229-241. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-42007-3_20

Ceesay, Bamfa ; Hou, Wen Juan. / Event extraction and classification by neural network model. Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. editor / Moonis Ali ; Hamido Fujita ; Jun Sasaki ; Masaki Kurematsu ; Ali Selamat. Springer Verlag, 2016. pp. 229-241 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "To understand and automatically extract information about events presented in a text, semantically meaningful units expressing these events are important. Extracting events and classifying them into event types and subtypes using Natural Language Processing techniques poses a challenging research problem. There is no clear-cut definitions to what an event from a text is and what the optimal representation of semantic units within a given text is. In addition, events in a text can be classified into types and subtypes of events; and a single event can have multiple mentions in a given sentence. In this paper, we propose a model to determine events within a given text and classify them into event types or subtypes and REALIS by the distributional semantic role labeling and neural embedding techniques. For the task of the event nugget detection, we trained a three-layer network to determine the event mentions from texts achieving F1-score of 77.37 % for macro average and 71.10 % for micro average, respectively.",

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N2 - To understand and automatically extract information about events presented in a text, semantically meaningful units expressing these events are important. Extracting events and classifying them into event types and subtypes using Natural Language Processing techniques poses a challenging research problem. There is no clear-cut definitions to what an event from a text is and what the optimal representation of semantic units within a given text is. In addition, events in a text can be classified into types and subtypes of events; and a single event can have multiple mentions in a given sentence. In this paper, we propose a model to determine events within a given text and classify them into event types or subtypes and REALIS by the distributional semantic role labeling and neural embedding techniques. For the task of the event nugget detection, we trained a three-layer network to determine the event mentions from texts achieving F1-score of 77.37 % for macro average and 71.10 % for micro average, respectively.

AB - To understand and automatically extract information about events presented in a text, semantically meaningful units expressing these events are important. Extracting events and classifying them into event types and subtypes using Natural Language Processing techniques poses a challenging research problem. There is no clear-cut definitions to what an event from a text is and what the optimal representation of semantic units within a given text is. In addition, events in a text can be classified into types and subtypes of events; and a single event can have multiple mentions in a given sentence. In this paper, we propose a model to determine events within a given text and classify them into event types or subtypes and REALIS by the distributional semantic role labeling and neural embedding techniques. For the task of the event nugget detection, we trained a three-layer network to determine the event mentions from texts achieving F1-score of 77.37 % for macro average and 71.10 % for micro average, respectively.

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Event extraction and classification by neural network model

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