Móra, György

Móra György

University of Szeged

Approximate dictionary matching for biomedical informationextractionGyörgy MóraInformation extraction techniques arewidely used in various textmining fields like scientificliterature mining and Web mining. Named entity recognizers often make use of word lists,that they subsequently map to the text. There are efficient methods available for mappinglarge dictionaries to texts. The species name recognizer Linnaeus, for instance, uses regular-expression-like patterns generated from a simple list of organism names [3]. The patterns aregenerated in such a way that the system can recognize spelling variants, but their flexibilityis limited. Biomedical entities display a great spelling variability, hence the tagging of geneand protein names is essential for good information extraction in the biomedical domain. Wedeveloped a method for effectively matching large dictionaries containing millions of entitynames using a flexible matching procedure. Our system was developed for gene and proteinname matching, but it can also be used for other tasks where flexible matching is required.Our approach tries to overcome the difficulties of biomedical entity matching. The order ofthe words in these entities may be different and extra punctuation characters and words mayappear inside entity names. For instance, member 3 of the solute carrier family 6 can be written assolute carrier family 6, member 3. Numbers can be written as Roman or Arabic numerals, whichincreases the number of possible variations. Greek letters are often transcribed or a similarLatin letter is used. For instance, nuclear factor kappa beta can be written as NF-kappa B or NF-κβ. To match all linguistic forms of a word we determined the base form of all words usedin all dictionary entries and during the matching the base forms of the words in the texts arematched against the base form of the entries in the dictionary. Biomedical entities often havenonconventional plural or adjective forms. In our experiments, we used the LVGTools [1] lin-guistic package to determine the possible base forms of the words in the dictionary and in thetext. Most words have one possible base form (like dogs -> dog), but there are ambiguous caseswhere only a syntactic or semantic analysis can determine which base form belongs to the wordform in a given context. The LVGTools also handle the above-mentioned issues with Greek let-ters and Roman numerals as it generates all possible ’interpretations’ of the ambiguous word.Our approach utilizes the Lucene search engine [2] for indexing. The dictionary entries aresplit into words and the possible base forms of the words are determined. The entities areindexed with respect to the base forms and the number of words in the entity name. Whenthe text is tagged, the possible base forms of the words are also determined. If one of the baseforms of a word is contained in the index, the word is marked as a potential part of an entityname. Based on these annotations, a value for each word in each sentence is calculated whichtells us how many words the longest entity name can have, which the current word may bea part of. This value is used as a maximum value when the possible names are queried fromthe index. This restriction speeds up the process because an entity name may be as long as22 words. The tagging speed is approximately one document per second on biomedical fullarticles with over 7 million different entity names using a computer with a 2 GHz processor.The spans of the hits may overlap and a rule-based system filters out the matches to producethe longest non-overlapping annotations.We developed a dictionary lookup tagger, which can be effectively applied in biomedi-cal information extraction pipelines, to perform string matching with the necessary flexibility.We showed that this task can be completed in reasonable time with alternative indexing andsearching methods.37References[1] SPECIALIST Lexicon and Lexical Tools. National Library of Medicine (US), September 2009.[2] Cutting, D. Lucene, 2001.[3] Gerner, Martin, Nenadic, Goran, and Bergman, Casey M. LINNAEUS: a species nameidentification system for biomedical literature. BMC bioinformatics, 11(1):85, January 2010.38

Approximate dictionary matching for biomedical information

extraction

http://acta.bibl.u-szeged.hu/61630/1/cscs_2012_037-038.pdf

Approximate dictionary matching for biomedical information extraction

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