Knowledge-poor and Knowledge-rich Approaches for Multilingual Terminology Extraction

In this paper, we present two terminology extraction tools in order to compare a knowledge-poor and a knowledge-rich approach. Both tools process single and multi-word terms and are designed to handle multilingualism. We run an evaluation on six languages and two di erent domains using crawled comparable corpora and hand-crafted reference term lists. We discuss the three main results achieved for terminology extraction. The rst two evaluation scenarios concern the knowledge-rich framework. Firstly, we compare performances for each of the languages depending on the ranking that is applied: speci city score vs. the number of occurrences. Secondly, we examine the relevancy of the term variant identi cation to increase the precision ranking for any of the languages. The third evaluation scenario compares both tools and demonstrates that a probabilistic term extraction approach, developed with minimal e ort, achieves satisfactory results when compared to a rule-based method.