Year: 2008
Type: conference-paper
Abstract: . Word Space Models provide a convenient way of modelling word mean-ing in terms of a word’s contexts in a corpus. This paper investigates the influence of the type of context features on the kind of semantic information that the models cap-ture. In particular, we make a distinction between semantic similarity and semantic relatedness. It is shown that the strictness of the context definition correlates with the models ’ ability to identify semantically similar words: syntactic approaches perform better than bag-of-word models, and small context windows are better than larger ones. For semantic relatedness, however, syntactic features and small context win-dows are at a clear disadvantage. Second-order bag-of-word models perform below average across the board.
Source: Lirias
Author: Yves Peirsman
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2519330991