Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
An unsupervised framework for extracting and normalizing product attributes from multiple web sites
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised learning of attribute-value pairs from product descriptions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Porting a lexicalized-grammar parser to the biomedical domain
Journal of Biomedical Informatics
Semantic annotation of biosystematics literature without training examples
Journal of the American Society for Information Science and Technology
Ontology-based information extraction: An introduction and a survey of current approaches
Journal of Information Science
Competency evaluation of plant character ontologies against domain literature
Journal of the American Society for Information Science and Technology
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Adapting a probabilistic disambiguation model of an HPSG parser to a new domain
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Evaluating the botanical coverage of PATO using an unsupervised learning algorithm
Proceedings of the 2012 iConference
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Biodiversity information organization is looking beyond the traditional document-level metadata approach and has started to look into factual content in textual documents to support more intelligent and semantic-based access. This article reports the development and evaluation of CharaParser, a software application for semantic annotation of morphological descriptions. CharaParser annotates semistructured morphological descriptions in such a detailed manner that all stated morphological characters of an organ are marked up in Extensible Markup Language format. Using an unsupervised machine learning algorithm and a general purpose syntactic parser as its key annotation tools, CharaParser requires minimal additional knowledge engineering work and seems to perform well across different description collections and/or taxon groups. The system has been formally evaluated on over 1,000 sentences randomly selected from Volume 19 of Flora of North American and Part H of Treatise on Invertebrate Paleontology. CharaParser reaches and exceeds 90% in sentence-wise recall and precision, exceeding other similar systems reported in the literature. It also significantly outperforms a heuristic rule-based system we developed earlier. Early evidence that enriching the lexicon of a syntactic parser with domain terms alone may be sufficient to adapt the parser for the biodiversity domain is also observed and may have significant implications. © 2012 Wiley Periodicals, Inc.