BibSonomy ::  user :: pitman ::

MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.

Finding important information in unstructured text From Language and Information Technologies Jump to: navigation, search A vast majority of the information we deal with in everyday life consists of raw, unstructured text, where the most important facts or concepts are not always readily available, but hidden in the myriad of details that accompany them. To handle and digest the sheer amount of information we are exposed to in this information age, more sophisticated procedures are required to unveil the important parts of a text, and to allow us to process more information in less time. The goal of this project is to develop robust and accurate techniques to automatically extract important information from unstructured text, in the form of keyphrases (keyphrase extraction) or entire sentences (extractive summarization). Funded by Google [edit]

I am investigating computational models for linguistic structures and processes, with application to language technologies and to the documentation of endangered languages. My current focus is on efficient query for databases of hierarchically annotated data. After completing a PhD on computational phonology at the University of Edinburgh in 1990, I worked on a series of European research projects and conducted linguistic fieldwork in Cameroon with SIL. In 1998 I moved to the University of Pennsylvania, becoming Associate Director of the LDC, and working on models and tools for linguistic annotation. In 2002 I returned home to Australia and established the Melbourne University Language Technology Group. In 2007 I was awarded the Kelvin Medal for excellence in teaching. Key Activities: Coordinating first year Informatics; developing the Natural Language Toolkit; writing a textbook on NLP; leading the Language Technology Group; working on an NSF project on Querying Linguistic Databases; and editing Cambridge Studies in Natural Language Processing and the ACL Anthology. Key Publications: Natural Language Processing in Python; Computational phonology: A constraint-based approach (Cambridge); A formal framework for linguistic annotation (Speech Communication); Seven dimensions of portability for language documentation and description (Language); Designing and evaluating an XPath dialect for linguistic queries (ICDE).