FinnWordNet - WordNet på finska via översättning

FinnWordNet is a WordNet for Finnish that conforms to the framework given in Fellbaum (1998) and Vossen (ed.) (1998). FinnWordNet is open source and currently contains 117,000 synsets. A classic WordNet consists of synsets, or sets of partial synonyms whose shared meaning is described and exemplified by a gloss, a common part of speech and a hyperonym. Synsets in a WordNet are arranged in hierarchical partial orderings according to semantic relations like hyponymy/hyperonymy. Together the gloss, part of speech and hyperonym fix the meaning of a word and constrain the possible translations of a word in a given synset. The Finnish group has opted for translating Princeton WordNet 3.0 synsets wholesale into Finnish by professional translators, because the translation process can be controlled with regard to quality, coverage, cost and speed of translation. The project was financed by FIN-CLARIN at the University of Helsinki. According to our preliminary evaluation, the translation process was diligent and the quality is on a par with the original Princeton WordNet.

Do wordnets also improve human performance on NLP tasks?

FinnWordNet is a wordnet for Finnish that complies with the format of the Princeton WordNet (PWN) (Fellbaum, 1998). It was built by translating the PrincetonWordNet 3.0 synsets into Finnish by human translators. It is open source and contains 117000 synsets. The Finnish translations were inserted into the PWN structure resulting in a bilingual lexical database. In natural language processing (NLP), wordnets have been used for infusing computers with semantic knowledge assuming that humans already have a sufficient amount of this knowledge. In this paper we present a case study of using wordnets as an electronic dictionary. We tested whether native Finnish speakers benefit from using a wordnet while completing English sentence completion tasks. We found that using either an English wordnet or a bilingual English Finnish wordnet significantly improves performance in the task. This should be taken into account when setting standards and comparing human and computer performance on these tasks.