Corpus annotation for mining biomedical events from literature

<p>Abstract</p> <p>Background</p> <p>Advanced Text Mining (TM) such as semantic enrichment of papers, event or relation extraction, and intelligent Question Answering have increasingly attracted attention in the bio-medical domain. For such attempts to succeed, text annotation from the biological point of view is indispensable. However, due to the complexity of the task, semantic annotation has never been tried on a large scale, apart from relatively simple term annotation.</p> <p>Results</p> <p>We have completed a new type of semantic annotation, event annotation, which is an addition to the existing annotations in the GENIA corpus. The corpus has already been annotated with POS (Parts of Speech), syntactic trees, terms, etc. The new annotation was made on half of the GENIA corpus, consisting of 1,000 Medline abstracts. It contains 9,372 sentences in which 36,114 events are identified. The major challenges during event annotation were (1) to design a scheme of annotation which meets specific requirements of text annotation, (2) to achieve biology-oriented annotation which reflect biologists' interpretation of text, and (3) to ensure the homogeneity of annotation quality across annotators. To meet these challenges, we introduced new concepts such as Single-facet Annotation and Semantic Typing, which have collectively contributed to successful completion of a large scale annotation.</p> <p>Conclusion</p> <p>The resulting event-annotated corpus is the largest and one of the best in quality among similar annotation efforts. We expect it to become a valuable resource for NLP (Natural Language Processing)-based TM in the bio-medical domain.</p

Event based text mining for integrated network construction

The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining techniques have been increasingly useful to extract such information from the literature in an automatic way, but up to now the main focus of text mining in the systems biology field has been restricted mostly to the discovery of protein-protein interactions. Here, we take this approach one step further, and use machine learning techniques combined with text mining to extract a much wider variety of interactions between biological entities. Each particular interaction type gives rise to a separate network, represented as a graph, all of which can be subsequently combined to yield a so-called integrated network representation. This provides a much broader view on the biological system as a whole, which can then be used in further investigations to analyse specific properties of the networ

Combining syntactic and ontological knowledge to extract biologically relevant relations from scientific papers

Bringing biologists and text miners closer together is a major aim towards the general usage of literature mining tools. Our contribution to this aim is an end-user tool for the extraction of problem-specific biologically relevant relations. Development efforts are being focused on easy-to-use text mining workflows including commonly available entity recognisers and syntactic processors, and the construction of a userfriendly environment that enables problemspecific tailoring by biologists.Fundação para a Ciência e a Tecnologia (FCT)Systems Biology as a Driver for Industrial Biotechnology (SYSINBIO