We describe a machine learning system for the recognition of names in biomedical texts. The system makes extensive use of local and syntactic features within the text, as well as external resources including the web and gazetteers. It achieves an Fscore of 70% on the Coling 2004 NLPBA/BioNLP shared task of identifying five biomedical named entities in the GENIA corpus.

The task of named entity annotation of unseen text has recently been successfully automated with near-human performance. But the full task involves more than annotation, i.e. identifying the scope of each (continuous) text span and its class (such as place name). It also involves grounding the named entity (i.e. establishing its denotation with respect to the world or a model). The latter aspect has so far been neglected.In this paper, we show how geo-spatial named entities can be grounded using geographic coordinates, and how the results can be visualized using off-the-shelf software. We use this to compare a "textual surrogate" of a newspaper story, with a "visual surrogate" based on geographic coordinates. § ¤ 3 45, 2, 345¦ ;

No abstract

No abstract

No abstract

No abstract

Current research in developmental biology aims to link developmental genetic pathways with the processes going on at cellular and tissue level. Normal processes will only take place under specific sequential conditions at the level of the pathways. Disrupting or altering pathways may mean disrupted or altered development. This paper is part of a larger work exploring methods of detecting and extracting information on developmental events from free text and on their relations in space and time.

No abstract