J Biomed Inform - Link-topic model for biomedical abbreviation disambiguation.

Tópicos

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Resumo

TRODUCTION: The ambiguity of biomedical abbreviations is one of the challenges in biomedical text mining systems. In particular, the handling of term variants and abbreviations without nearby definitions is a critical issue. In this study, we adopt the concepts of topic of document and word link to disambiguate biomedical abbreviations.METHODS: We newly suggest the link topic model inspired by the latent Dirichlet allocation model, in which each document is perceived as a random mixture of topics, where each topic is characterized by a distribution over words. Thus, the most probable expansions with respect to abbreviations of a given abstract are determined by word-topic, document-topic, and word-link distributions estimated from a document collection through the link topic model. The model allows two distinct modes of word generation to incorporate semantic dependencies among words, particularly long form words of abbreviations and their sentential co-occurring words; a word can be generated either dependently on the long form of the abbreviation or independently. The semantic dependency between two words is defined as a link and a new random parameter for the link is assigned to each word as well as a topic parameter. Because the link status indicates whether the word constitutes a link with a given specific long form, it has the effect of determining whether a word forms a unigram or a skipping/consecutive bigram with respect to the long form. Furthermore, we place a constraint on the model so that a word has the same topic as a specific long form if it is generated in reference to the long form. Consequently, documents are generated from the two hidden parameters, i.e. topic and link, and the most probable expansion of a specific abbreviation is estimated from the parameters.RESULTS: Our model relaxes the bag-of-words assumption of the standard topic model in which the word order is neglected, and it captures a richer structure of text than does the standard topic model by considering unigrams and semantically associated bigrams simultaneously. The addition of semantic links improves the disambiguation accuracy without removing irrelevant contextual words and reduces the parameter space of massive skipping or consecutive bigrams. The link topic model achieves 98.42% disambiguation accuracy on 73,505 MEDLINE abstracts with respect to 21 three letter abbreviations and their 139 distinct long forms.

Resumo Limpo

troduct ambigu biomed abbrevi one challeng biomed text mine system particular handl term variant abbrevi without nearbi definit critic issu studi adopt concept topic document word link disambigu biomed abbreviationsmethod newli suggest link topic model inspir latent dirichlet alloc model document perceiv random mixtur topic topic character distribut word thus probabl expans respect abbrevi given abstract determin wordtop documenttop wordlink distribut estim document collect link topic model model allow two distinct mode word generat incorpor semant depend among word particular long form word abbrevi sententi cooccur word word can generat either depend long form abbrevi independ semant depend two word defin link new random paramet link assign word well topic paramet link status indic whether word constitut link given specif long form effect determin whether word form unigram skippingconsecut bigram respect long form furthermor place constraint model word topic specif long form generat refer long form consequ document generat two hidden paramet ie topic link probabl expans specif abbrevi estim parametersresult model relax bagofword assumpt standard topic model word order neglect captur richer structur text standard topic model consid unigram semant associ bigram simultan addit semant link improv disambigu accuraci without remov irrelev contextu word reduc paramet space massiv skip consecut bigram link topic model achiev disambigu accuraci medlin abstract respect three letter abbrevi distinct long form

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