Biomedical text retrieval in languages with a complex morphology

Biomedical Text Retrieval in Languages with a Complex Morphology Stefan Schulz a Martin Honeck a Udo Hahn b a Department of Medical Informatics, Freiburg University Hospital http://www.imbi.uni-freiburg.de/medinf b Text Knowledge Engineering Lab, Freiburg University http://www.coling.uni-freiburg.de Abstract Document retrieval in languages with a rich and complex morphology – par- ticularly in terms of derivation and (single-word) composition – suffers from serious performance degradation with the stemming-only query-term-to-text-word matching paradigm. We propose an alternative approach in which morpholog- ically complex word forms are segmented into relevant subwords (such as stems, named entities, acronyms), and subwords constitute the basic unit for indexing and retrieval. We evaluate our approach on a large biomedical document collection. 1 Introduction Morphological alterations of a search term have a negative impact on the recall performance of an information retrieval (IR) system (Choueka, 1990; J¨appinen and Niemist¨o, 1988; Kraaij and Pohlmann, 1996), since they preclude a direct match between the search term proper and its morphological vari- ants in the documents to be retrieved. In order to cope with such variation, morphological analysis is concerned with the reverse processing of inflec- tion (e.g., ‘search � ed’, ‘search � ing’)1, derivation (e.g., ‘search � er’ or ‘search � able’) and composi- tion (e.g., German ‘Blut � hoch � druck’ [‘high blood pressure’]). The goal is to map all occurring mor- phological variants to some canonical base form — e.g., ‘search’ in the examples from above. The efforts required for performing morphologi- cal analysis vary from language to language. For English, known for its limited number of inflec- tion patterns, lexicon-free general-purpose stem- 1‘ � ’ denotes the string concatenation operator. mers (Lovins, 1968; Porter, 1980) demonstrably im- prove retrieval performance. This has been reported for other languages, too, dependent on the general- ity of the chosen approach (J¨appinen and Niemist¨o, 1988; Choueka, 1990; Popovic and Willett, 1992; Ekmekc¸ioglu et al., 1995; Hedlund et al., 2001; Pirkola, 2001). When it comes to a broader scope of morphological analysis, including derivation and composition, even for the English language only re- stricted, domain-specific algorithms exist. This is particularly true for the medical domain. From an IR view, a lot of specialized research has already been carried out for medical applications, with em- phasis on the lexico-semantic aspects of dederiva- tion and decomposition (Pacak et al., 1980; Norton and Pacak, 1983; Wolff, 1984; Wingert, 1985; Du- jols et al., 1991; Baud et al., 1998). While one may argue that single-word com- pounds are quite rare in English (which is not the case in the medical domain either), this is certainly not true for German and other basically aggluti- native languages known for excessive single-word nominal compounding. This problem becomes even more pressing for technical sublanguages, such as medical German (e.g., ‘Blut � druck � mess � ger¨at’ translates to ‘device for measuring blood pressure’). The problem one faces from an IR point of view is that besides fairly standardized nominal compounds, which already form a regular part of the sublanguage proper, a myriad of …

Content truncated to 500 words