Abstract
There is an urgent need to develop new text mining solutions using high performance computing (HPC) and grid environments to tackle exponential growth in text data. Problem sizes are increasing by the day by addition of new text documents. The task of labelling sequence data such as part-of-speech (POS) tagging, chunking (shallow parsing) and named entity recognition is one of the most important tasks in text mining. Genia is a POS tagger which is specifically tuned for biomedical text. Genia is built with maximum entropy modelling and state of the art tagging algorithm. A parallel version of genia tagger application has been implemented and performance has been compared on a number of different architectures. The focus has been particularly on scalability of the application. Scaling of 512 processors has been achieved and a method to scale to 10000 processors is proposed for massively parallel text mining applications. The parallel implementation of genia tagger is done using MPI for achieving portable code.
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