Normal view MARC view ISBD view

Comparing measures of semantic similarity / Ljubešić, Nikola ; Boras, Damir ; Bakarić, Nikola ; Njavro, Jasmina.

By: Ljubešić, Nikola, informatičar.
Contributor(s): Bakarić, Nikola [aut] | Njavro, Jasmina [aut] | Boras, Damir [aut].
Material type: ArticleArticleDescription: 675-682 str.Other title: Comparing measures of semantic similarity [Naslov na engleskom:].Subject(s): 5.04 | calculating semantic similarity, context, association measures, similarity measures hrv | calculating semantic similarity, context, association measures, similarity measures engOnline resources: Click here to access online In: 30th International Conference on Information Technology Interfaces (23-26.06.2008. ; Dubrovnik, Hrvatska) Proceedings of the 30th International Conference on Information Technology Interfaces str. 675-682Hljuz Dobrić, VesnaSummary: The aim of this paper is to compare different methods for automatic extraction of semantic similarity measures from corpora. The semantic similarity measure is proven to be very useful for many tasks in natural language processing like information retrieval, information extraction, machine translation etc. Additionally, one of the main problems in natural language processing is data sparseness since no language sample is large enough to seize all possible language combinations. In our research we experiment with four different measures of association with context and eight different measures of vector similarity. The results show that the Jensen-Shannon divergence and L1 and L2 norm outperform other measures of vector similarity regardless of the measure of association with context used. Maximum likelihood estimate and t-test show better results than other measures of association with context.
Tags from this library: No tags from this library for this title. Log in to add tags.
No physical items for this record

The aim of this paper is to compare different methods for automatic extraction of semantic similarity measures from corpora. The semantic similarity measure is proven to be very useful for many tasks in natural language processing like information retrieval, information extraction, machine translation etc. Additionally, one of the main problems in natural language processing is data sparseness since no language sample is large enough to seize all possible language combinations. In our research we experiment with four different measures of association with context and eight different measures of vector similarity. The results show that the Jensen-Shannon divergence and L1 and L2 norm outperform other measures of vector similarity regardless of the measure of association with context used. Maximum likelihood estimate and t-test show better results than other measures of association with context.

Projekt MZOS 130-1301679-1380

ENG

There are no comments for this item.

Log in to your account to post a comment.

Powered by Koha

//