Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications

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Acerca del autor: James Pustejovsky teaches and does research in Artificial Intelligence and Computational Linguistics in the Computer Science Department at Brandeis University. His main areas of interest include: lexical meaning, computational semantics, temporal and spatial reasoning, and corpus linguistics. He is active in the development of standards for interoperability between language processing applications, and lead the creation of the recently adopted ISO standard for time annotation, ISO-TimeML. He is currently heading the development of a standard for annotating spatial information in language. More information on publications and research activities can be found at his webpage: pusto.com. Amber Stubbs is a Ph.D. candidate in Computer Science at Brandeis University in the Laboratory for Linguistics and Computation. Her dissertation is focused on creating an annotation methodology to aid in extracting high-level information from natural language files, particularly biomedical texts. Information about her publications and other projects can be found on her website: http://pages.cs.brandeis.edu/~astubbs/.