Parallel Text Mining for Large Text Processing

There is an urgent need to develop new text mining solutions using High Performance Computing (HPC) and grid environments to tackle the exponential growth in textual data. Problem sizes are increasing by the day by addition of new text documents. Therefore the aim of this work is to lay the foundations for mining large text datasets (i.e. full text articles) in reasonable timeframes. The task of labelling sequence data such as part-ofspeech (POS) tagging, chunking (shallow parsing) and named entity recognition is one of the most important tasks in Text Mining. This work focuses on state-of-the-art GENIA tagger and STEPP parser. GENIA is a POS tagger which is specifically tuned for biomedical text and STEPP is a full parser. A parallel version of GENIA and STEPP has been developed and performance has been compared on a number of different architectures. The focus has been particularly on scalability: scaling to 512 processors has been achieved. Furthermore, a parallel text mining framework has been proposed that enables scaling to 10000 processors for massively parallel Text Mining applications. The processing times have been reduced dramatically for the given datasets from over 70 days to hours (towards 3 orders of magnitude reduction). The parallel implementation is done using Message Passing Interface (MPI) to achieve portable code. The resulting parallel applications have been tested on a number of architectures and the entire collection of Medline text abstracts together with 125000 full text articles have been used for the tests.