Multiple Evidence for Term Extraction in Broad Domains

Proceedings of Recent Advances in Natural Language Processing, pages 710–715, Hissar, Bulgaria, 12-14 September 2011. Multiple Evidence for Term Extraction in Broad Domains Boris Dobrov Research Computing Center of Lomonosov Moscow State University, Moscow, Russia dobroff@mail.cir.ru Natalia Loukachevitch Research Computing Center of Lomonosov Moscow State University, Moscow, Russia louk_nat@mail.cir.ru Abstract The paper describes the method of extraction of two-word domain terms combining their features. The features are computed from three sources: the occurrence statistics in a domain-specific text collection, the statistics of global search engines, and a domain- specific thesaurus. The evaluation of the approach is based on manually created thesauri. We show that the use of multiple features considerably improves the automatic extraction of domain-specific terms. We compare the quality of the proposed method in two different domains. 1 Introduction The important stage of domain specific knowledge acquisition is recognition of terms, representing domain concepts in documents. Automatic extraction of domain terms from texts is a subject of constant interest in automatic document processing. The special difficulty is the automatic extraction of multiword terms (Zhang et. al. 2008; Wong et. al. 2008). Contemporary information systems usually contain documents related to broad domains, which requires development of large terminological resources. Term extraction to develop such resources should be based on processing of large amount of documents. Besides, existing terminological resources need periodic updates. For many years, researchers tried to find the best statistical feature for term extraction. Now machine learning methods allow for the combination of many features (Vivaldi et.al, 2001, Pecina and Schlesinger, 2008, Foo and Merkel, 2010). In (Vivaldi et. al., 2001) features for extraction of medical terms are combined using boosting algorithm. The features include information from EuroWordNet, Greek and Latin word forms, statistical measures. Some of the features are rather domain-dependent. (Foo and Merkel, 2010) study applicacability of rule-based machine-learning algorithm Ripper for term extraction from patent texts. In (Pecina and Schlesinger, 2008) the combi- nation of statistical characteristics of phrases, based on the Czech text collection, is used to ex- tract several types of collocations (such as phras- al verbs, idioms, terms). The authors used over 80 features and obtained 20% improvement compared with the best individual feature. But the authors of this paper indicate that efficiency of different features is very variable and depends on a collection, types of expressions and so on. In this paper we describe an experiment to extract two-word terms (noun groups) based on a combination of three types of features: features based on a domain-specific text collection, features obtained from an Internet search engine, features obtained from a domain-specific thesau- rus. Working with a thesaurus, we simulate the situation when the thesaurus partially exists. We want to study its potential to recognise new terms. The important point of our research is to study the stability of the term extraction model among different domains. 2 Description of Experiment: Data and Evaluation We conduct our study in two domains. The first domain is the very broad domain of natural sciences and …

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