Rene giving a talk at NLDB 2010 in Cardiff. We presented our work on Semantic Content Access using Domain-Independent NLP Ontologies.Rene@NLDB2010

For those interested in performing NLP on source code, in particular Javadoc comments, we just released a Doclet at the NLP Frameworks workshop last week.

Its main feature is that it creates an XML corpus from Java source code that is optimised for processing in an NLP Framework (GATE in our case, but it should work for any framework that takes XML as input).

At the LREC workshop New Challenges for NLP Frameworks we released a new component for GATE: The Predicate-Argument Extractor (PAX).

I presented two papers at ASWC 2008 in Bangkok. One was my own: Ralf Krestel, Ling Chen -
"The Art of Tagging: Measuring the Quality of Tags" and the other was Rene's: Rene Witte, Thomas Gitzinger -
"Semantic Assistants – User-Centric Natural Language Processing Services for Desktop Clients". Attached a photo of myself explaining the future work for the semantic assistants :) Nice conference, nice people, nice country and - compared to Canada and Germany - very nice weather!
Presentation at ASWC 08

CALL FOR PAPERS, TUTORIALS, PANELS

SEMAPRO 2009: The Third International Conference on Advances in Semantic Processing
October 11-16, 2009 - Sliema, Malta

General page: http://www.iaria.org/conferences2009/SEMAPRO09.html
Call for Papers: http://www.iaria.org/conferences2009/CfPSEMAPRO09.html
Submission deadline: May 20, 2009

Coinciding with the presentation of our paper on Minding the Source: Automatic Tagging of Reported Speech in Newspaper Articles at LREC 2008, we are happy to announce the first public release of our free/open source Reported Speech Tagging Components.

The Multi-Lingual Noun Phrase Extractor (MuNPEx) is a fast, robust, customizable, and well-tested noun phrase (NP) chunker component developed for the GATE architecture, implemented in JAPE. It currently supports English, German, French, and Spanish (in beta). It provides detailed features for each NP annotation, with DET (determiner), MOD/MOD2 (pre/post-head modifiers), and HEAD noun slots, as well as (optional) text offset information.

MuNPEx requires a part-of-speech (POS) tagger to work and can additionally use detected named entities (NEs) to improve chunking performance. Please read the documentation (and source code) for more details.