Publication
Title
Multimodular text normalization of Dutch user-generated content
Author
Abstract
As social media constitutes a valuable source for data analysis for a wide range of applications, the need for handling such data arises. However, the nonstandard language used on social media poses problems for natural language processing (NLP) tools, as these are typically trained on standard language material. We propose a text normalization approach to tackle this problem. More specifically, we investigate the usefulness of a multimodular approach to account for the diversity of normalization issues encountered in user-generated content (UGC). We consider three different types of UGC written in Dutch (SNS, SMS, and tweets) and provide a detailed analysis of the performance of the different modules and the overall system. We also apply an extrinsic evaluation by evaluating the performance of a part-of-speech tagger, lemmatizer, and named-entity recognizer before and after normalization.
Language
English
Source (journal)
ACM Transactions on Intelligent Systems and Technology (TIST)
Publication
2016
ISSN
2157-6904
2157-6912
Volume/pages
7:4(2016), p. 1-22
Article Reference
61
ISI
000380322200018
Medium
E-only publicatie
Full text (Publishers DOI)
Full text (publishers version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 02.09.2016
Last edited 12.05.2017
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