Shallow semantic analyzers, such as semantic role labelers and sense taggers, are increasing in accuracy and becoming commonplace. However, they only provide limited and local representations of words and individual predicate-argument structures.
This talk will address some of the current opportunities and challenges in producing deeper, richer representations of coherent eventualities. Available resources, such as VerbNet, that can assist in this process will also be discussed, as well as some of their limitations.
Martha Palmer is a Full Professor at the University of Colorado with joint appointments in Linguistics and Computer Science and is an Institute of Cognitive Science Faculty Fellow. She recently won a Boulder Faculty Assembly 2010 Research Award.
Martha's research has been focused on trying to capture elements of the meanings of words that can comprise automatic representations of complex sentences and documents. Supervised machine learning techniques rely on vast amounts of annotated training data so she and her students are engaged in providing data with word sense tags and semantic role labels for English, Chinese, Arabic, Hindi, and Urdu, funded by DARPA and NSF. They also train automatic sense taggers and semantic role labelers, and extract bilingual lexicons from parallel corpora. A more recent focus is the application of these methods to biomedical journal articles and clinical notes, funded by NIH.
Martha is a co-editor for the Journal of Natural Language Engineering and for LiLT, Linguistic Issues in Language Technology, and on the CLJ Editorial Board. She is a past President of the Association for Computational Linguistics, past Chair of SIGLEX and SIGHAN, and was the Director of the 2011 Linguistics Institute held in Boulder, Colorado.