Open Access
Machine learning and natural language processing
Lluís Màrquez Villodre
- 01 Jul 2000
59
TL;DR: Four algorithms for supervised learning, which belong to different families, are compared in a benchmark corpus for the WSD task and both qualitative and quantitative conclusions are drawn.
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Abstract: In this report, some collaborative work between the fields of Machine Learning (ML) and Natural Language Processing (NLP)
is presented. The document is structured in two parts. The first part includes a superficial but comprehensive survey covering
the state-of-the-art of machine learning techniques applied to natural language learning tasks. In the second part, a particular
problem, namely Word Sense Disambiguation (WSD), is studied in more detail. In doing so, four algorithms for supervised
learning, which belong to different families, are compared in a benchmark corpus for the WSD task. Both qualitative and
quantitative conclusions are drawn.
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