Identification of Multiword Expressions in Technical Domains: Investigating Statistical and Alignment-Based Approaches

Multiword Expressions (MWEs) are one of the stumbling blocks for more precise Natural Language Processing (NLP) systems. The lack of coverage of MWEs in resources can impact negatively on the performance of tasks and applications, and can lead to loss of information or communication errors; especial...

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Main Authors: Villavicencio, Aline, de Medeiros Caseli, Helena, Machado, Andre
Format: Conference Proceeding
Language:English
Subjects:
Application software
Computer science
Global warming
Humans
Informatics
Information resources
Lexical Acquisition
Multiword Expressions
Natural language processing
Natural languages
Performance loss
Vocabulary
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creator Villavicencio, Aline
de Medeiros Caseli, Helena
Machado, Andre
description Multiword Expressions (MWEs) are one of the stumbling blocks for more precise Natural Language Processing (NLP) systems. The lack of coverage of MWEs in resources can impact negatively on the performance of tasks and applications, and can lead to loss of information or communication errors; especially in technical domains where MWE are frequent. This paper investigates some approaches to the identification of MWEs in technical corpora based on: association measures, part-of-speech and lexical alignment information. We examine the influence of some factors on their performance such as sources of information for identification and evaluation. While the association measures emphasize recall, the alignment method focuses on precision.
doi_str_mv 10.1109/STIL.2009.33
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subjects Application software
Computer science
Global warming
Humans
Informatics
Information resources
Lexical Acquisition
Multiword Expressions
Natural language processing
Natural languages
Performance loss
Vocabulary
title Identification of Multiword Expressions in Technical Domains: Investigating Statistical and Alignment-Based Approaches
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