Research on Advanced Natural Language Processing and Text Mining

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The objective of the project was to apply the methodology of combining statistical modeling with structure-based symbolic processing, which had proven successful in sentence parsing, to more challenging tasks such as deep semantic processing, knowledge-based information extraction and contextual processing. We have achieved significant results in (1) efficient and robust deep parsing based on a linguistically sound formalism, (2) a large scale semantically annotated corpus for the biology domain (GENIA corpus), (3) information extraction programs (named entity recognizers and event recognizers) for the biology domain which combine the deep parsing in (1) and structural machine learning algorithms, and (4) Workflow software for data-centered parallel processing.The GENIA corpus in (2) has been recognized as the gold standard corpus for research of text mining for biology and has been used by many groups in the world. It was adopted as the training and test corpus for international shared task competition twice (BioNLP 09 and BioNLP 11). The extraction programs developed in (3) successfully showed the state of the art performance in these international shared task competitions. The system based on (1) and (4) showed that the technology developed by this project was practical for processing the real world text. We successfully processed the whole of MEDLINE (20 million abstracts, more than 2 billion sentences) and indexed them semantically in less than a week. The processing results of MEDLINE has been made publicly available through an intelligent document retrieval system (MEDIE)