Event extraction from biomedical text is a very important task in text mining and natural language processing. The overall task involves finding event-related expressions, classifying these into predefined categories and attaching arguments to these events. We perform event detection and event classification in one stepusing an ensemble of classifiers. For event argument extraction, we also use an ensemble of classification models. Our base models are developed using supervised machine learning that makes use of statistical, contextualand syntactic features. Our experimental result on the benchmark datasets of BioNLP-2011 shared task shows the recall, precision and F-measure values of 51.20%, 65.78% and 57.58%, respectively.

In this paper, we propose an alternate technique to improve the performance of the statistical machine translation (SMT) system. Here, the phrases are re-weighted in light of linguistic knowledge as both syntactic and semantic information. Syntactic knowledge helps to increase fluency whereas semantic similarity helps to incorporate semantic meaning, which is required for adequacy of translated sentences. The scores of the phrases from the phrase-table are re-balanced by expanding and diminishing the weights of the correct phrasesand the incorrect phrases, respectively. Additional knowledge in phrase-table helps in improving overall performance of translation quality. In this work, our proposed methodology achieves an impressive accuracy improvement in terms of BLEU, NIST and RIBES in different domain data. We achieve 58.54 BLEU points,0.7759 RIBES points and 9.684 NIST points for product domain catalog.