Enhancing automatic term recognition through recognition of variation

Enhancing automatic term recognition through recognition of variation Goran Nenadić* Department of Computation UMIST Manchester, UK, M60 1QD G.Nenadic@umist.ac.uk Sophia Ananiadou* Computer Science University of Salford Salford, UK, M5 4WT S.Ananiadou@salford.ac.uk John McNaught* Department of Computation UMIST Manchester, UK, M60 1QD J.McNaught@umist.ac.uk * Co-affiliation: National Centre for Text Mining, Manchester, UK Abstract Terminological variation is an integral part of the linguistic ability to realise a concept in many ways, but it is typically considered an obstacle to automatic term recognition (ATR) and term management. We present a method that integrates term variation in a hybrid ATR approach, in which term candidates are recognised by a set of linguistic filters and termhood assignment is based on joint frequency of occurrence of all term variants. We evaluate the effectiveness of incorporating specific types of term variation by comparing it to the performance of a baseline method that treats term variants as separate terms. We show that ATR precision is enhanced by considering joint termhoods of all term variants, while recall benefits by the introduction of new candidates through consideration of different variation types. On a biomedical test corpus we show that precision can be increased by 20–70% for the top ranked terms, while recall improves generally by 2–25%. 1 Introduction Terminological processing has long been recognised as one of the crucial aspects of systematic knowledge acquisition and of many NLP applications (IR, IE, corpus querying, etc.). However, term variation has been under-discussed and is rarely accounted for in such applications. When naming a new concept, scientists and specialists usually follow some predefined term formation patterns, a process which does not exclude the usage of term variations or alternative names for concepts. Term variations are very frequent: approximately one third of term occurrences are variants (Jacquemin, 2001). They occur not only in text, but also in controlled, manually curated terminological resources (e.g. UMLS (NLM, 2004)). The task of an automatic term recognition (ATR) system is not only to suggest the most likely candidate terms from text, but also to correlate them with synonymous term variants. In this paper, we briefly present an analysis of term variation phenomena, whose results are subsequently incorporated into a corpus-based ATR method in order to enhance its performance. The paper is organised as follows. In Section 2, we analyse the main types of term variation, and briefly examine how existing ATR systems treat them. Our approach to incorporating variants into ATR is presented in Section 3. In Section 4, we evaluate our approach by comparing it to a baseline method (the method without variation re- cognition), and we conclude the paper in Section 5. 2 Background Terms are linguistic units that are assigned to concepts and used by domain specialists to describe and refer to specific concepts in a domain. In this sense, terms are preferred designators of concepts. In text, however, concepts are frequently denoted by different surface realisations of preferred terms, which we denote as their term variants. Consequently, a concept can be linguistically represented using …

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