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Erschienen in:
Intelligent Systems in Modeling Phase of Information Mining Development Process Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Event Extraction and Classification by Neural Network Model

verfasst von : Bamfa Ceesay, Wen-Juan Hou

Erschienen in: Trends in Applied Knowledge-Based Systems and Data Science

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Citation-Based Extraction of Core Contents from Biomedical Articles
Nächstes Kapitel A Hybrid Approach to Sentiment Analysis with Benchmarking Results
Fußnoten
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Metadaten
Titel
Event Extraction and Classification by Neural Network Model
verfasst von
Bamfa Ceesay
Wen-Juan Hou
Copyright-Jahr
2016
Buch
Trends in Applied Knowledge-Based Systems and Data Science

Print ISBN: 978-3-319-42006-6

Electronic ISBN: 978-3-319-42007-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-42007-3_20