Exploring the Vector Space Model for Finding Verb Synonyms in Portuguese
Luís Sarmento, Paula Carvalho, Eugénio Oliveira · 2009
We explore the performance of the Vector Space Model (VSM) in finding verb synonyms in Portuguese by analyzing the impact of three operating parameters: (i) the weighting function, (ii) the context window used for automatically extracting features, and (iii) the minimum number of vector features. We rely on distributional statistics taken from a large n-gram database to build feature vectors, using minimal linguistic pre-processing. Automatic evaluation of synonym candidates using gold-standard information from the OpenOffice and Wiktionary thesaurus shows that low frequency features carry most information regarding verb similarity, and that a [0, +2] window carries more information than a [-2, 0] window. We show that satisfactory precision levels require vectors with 50 or more non-nil components. Manual evaluation over a set of declarative verbs and psychological verbs show that VSM-based approaches achieve good precision in finding verb synonyms for Portuguese, evenwhen using minimal linguistic knowledge. This lead us to proposing a performance baseline for this task.