Incorporating Context Information for the Extraction of Terms

Incorporating Context Information for the Extraction of Terms Katerina T. Frantzi Dept. of Computing Manchester Metropolitan University Manchester, M1 5GD, U.K. K. Frantzi@doc. mmu. ac. uk Abstract The information used for the extraction of terms can be considered as rather 'inter- nal', i.e. coming from the candidate string itself. This paper presents the incorpora- tion of 'external' information derived from the context of the candidate string. It is embedded to the C-value approach for automatic term recognition (ATR), in the form of weights constructed from statisti- cal characteristics of the context words of the candidate string. 1 Introduction &: Related Work The applications of term recognition (specialised dic- tionary construction and maintenance, human and machine translation, text categorization, etc.), and the fact that new terms appear with high speed in some domains (e.g. in computer science), enforce the need for automating the extraction of terms. ATR also gives the potential to work with large amounts of real data, that it would not be able to handle man- ually. We should note that by ATR we neither mean dictionary string matching, nor term interpretation (which deals with the relations between terms and concepts). Terms may consist of either one or more words. When the aim is the extraction of single-word terms, domain-dependent linguistic information (i.e. mor- phology) is used (Ananiadou, 1994). Multi-word ATR usually uses linguistic information in the form of a grammar that mainly allows noun phrases or compounds to be extracted as candidate terms: (Bourigault, 1992) extracts maximal-length noun phrases and their subgroups (depending on their grammatical structure and position) as candidate terms. (Dagan and Church, 1994), accept sequen- cies of nouns, which give them high precision, but not such a good recall as that of (Justeson and Katz, 1995), which allow some prepositions (i.e. oj~ to be part of the extracted candidate terms. (Frantzi and Ananiadou, 1996), stand between these two ap- proaches, allowing the extracted compounds to con- tain adjectives but no prepositions. (Daille et al., 1994) also allow adjectives to be part of the two- word English terms they extract. From the above, only (Bourigault, 1992) does not use any statistical information. (Justeson and Katz, 1995) and (Dagan and Church, 1994) use the fre- quency of occurrence of the candidate string as a measure of its likelihood to be a term. (Daille et al., 1994) agree that frequency of occurrence "presents the best histogram", but also suggest the likeli- hood ratio for the extraction of two-word English terms. (Frantzi and Ananiadou, 1996), besides the frequency of occurrence, also consider the frequency of the candidate string as a part of longer candidate terms, as well as the number of these longer candi- date terms it is found nested in. In this paper, we extend C-value, the statisti- cal measure proposed by (Frantzi and Ananiadou, 1996), incorporating information gained from the textual context of the candidate term. 2 Context information for terms The idea of incorporating context information for term extraction came from that "Extended term units are …

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