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 co-occurrence 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.
|Title of host publication||LREC 2018 - 11th International Conference on Language Resources and Evaluation|
|Editors||Hitoshi Isahara, Bente Maegaard, Stelios Piperidis, Christopher Cieri, Thierry Declerck, Koiti Hasida, Helene Mazo, Khalid Choukri, Sara Goggi, Joseph Mariani, Asuncion Moreno, Nicoletta Calzolari, Jan Odijk, Takenobu Tokunaga|
|Publisher||European Language Resources Association (ELRA)|
|Number of pages||6|
|State||Published - 2019|
|Event||11th International Conference on Language Resources and Evaluation, LREC 2018 - Miyazaki, Japan|
Duration: 7 May 2018 → 12 May 2018
|Name||LREC 2018 - 11th International Conference on Language Resources and Evaluation|
|Conference||11th International Conference on Language Resources and Evaluation, LREC 2018|
|Period||7/05/18 → 12/05/18|
Bibliographical notePublisher Copyright:
© LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.
- Distributional similarity
- Morphologically Rich Language