Year: 2018
Type: conference-paper
Abstract: Automatic thesaurus construction for Modern Hebrew is a complicated task, due to its high degree of inflectional ambiguity.Linguistics tools, including morphological analyzers, part-of-speech taggers and parsers often have limited in performance on Morphologically Rich Languages (MRLs) such as Hebrew.In this paper, we adopted a schematic methodology for generating a cooccurrence based thesaurus in a MRL and extended the methodology to create distributional similarity thesaurus.We explored three alternative levels of morphological term representations, surface form, lemma, and multiple lemmas, all complemented by the clustering of morphological variants.First, we evaluated both the co-occurrence based method and the distributional similarity method using Hebrew WordNet as our gold standard.However, due to Hebrew WordNet's low coverage, we completed our analysis with a manual evaluation.The results showed that for Modern Hebrew corpus-based thesaurus construction, the most directly applied statistical collection, using linguistics tools at the lemma level, is not optimal.
Authors: Chaya Liebeskind, Ido Dagan, Jonathan Schler
Institutions: Bar-Ilan University, Jerusalem College of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2806157788