Improving Document Clustering by Removing Unnatural Language

Myungha Jang, Jinho D. Choi, James Allan


Abstract
Technical documents contain a fair amount of unnatural language, such as tables, formulas, and pseudo-code. Unnatural language can bean important factor of confusing existing NLP tools. This paper presents an effective method of distinguishing unnatural language from natural language, and evaluates the impact of un-natural language detection on NLP tasks such as document clustering. We view this problem as an information extraction task and build a multiclass classification model identifying unnatural language components into four categories. First, we create a new annotated corpus by collecting slides and papers in various for-mats, PPT, PDF, and HTML, where unnatural language components are annotated into four categories. We then explore features available from plain text to build a statistical model that can handle any format as long as it is converted into plain text. Our experiments show that re-moving unnatural language components gives an absolute improvement in document cluster-ing by up to 15%. Our corpus and tool are publicly available
Anthology ID:
W17-4416
Volume:
Proceedings of the 3rd Workshop on Noisy User-generated Text
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
122–130
Language:
URL:
https://www.aclweb.org/anthology/W17-4416
DOI:
10.18653/v1/W17-4416
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-4416.pdf