Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Overview of BioNLP'09 shared task on event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
An approach for the automatic recommendation of ontologies using collaborative knowledge
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
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A simple and accurate method for assigning broad semantic classes to text strings is presented. The method is to map text strings to terms in ontologies based on a pipeline of exact matches, normalized strings, headword matching, and stemming headwords. The results of three experiments evaluating the technique are given. Five semantic classes are evaluated against the CRAFT corpus of full-text journal articles. Twenty semantic classes are evaluated against the corresponding full ontologies, i.e. by reflexive matching. One semantic class is evaluated against a structured test suite. Precision, recall, and F-measure on the corpus when evaluating against only the ontologies in the corpus is micro-averaged 67.06/78.49/72.32 and macro-averaged 69.84/83.12/75.31. Accuracy on the corpus when evaluating against all twenty semantic classes ranges from 77.12% to 95.73%. Reflexive matching is generally successful, but reveals a small number of errors in the implementation. Evaluation with the structured test suite reveals a number of characteristics of the performance of the approach.