We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small.Database URL: http://biosemantics.org/chemdner-patents
This chapter gives a brief overview of text-mining techniques to extract knowledge from large text collections. It describes the basis pipeline of how to come from text to relationships between biological concepts and the problems that are encountered at each step in the pipeline. We first explain how words in text are recognized as concepts. Second, concepts are associated with each other using 2×2 contingency tables and test statistics. Third, we explain that it is possible to extract indirect links between concepts using the direct links taken from 2×2 table analyses. This we call implicit information extraction. Fourth, the validation techniques to evaluate a text-mining system such as ROC curves and retrospective studies are discussed. We conclude by examining how text information can be combined with other non-textual data sources such as microarray expression data and what the future directions are for text-mining within the Internet.
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