Part-of-Speech Tagging in Molecular Biology Scientific Abstracts Using Morphological and Contextual Statistical Information
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چکیده
In this paper a probabilistic tagger for molecular biology related abstracts is presented and evaluated. The system consists of three modules: a rule based molecular-biology names detector, an unknown words handler, and a Hidden Markov model based tagger which are used to annotate the corpus with an extended set of grammatical and molecular biology tags. The complete system has been evaluated using 500 randomly selected abstracts from the MEDLINE database. The F-score for the molecular-biology names detector was 0.95, and the annotation rate was greater than 93% in all experiments using the Viterbi algorithm. The best annotation rate of 97.34% is achieved using a Pubmed dictionary.
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تاریخ انتشار 2004