Natural language processing for biomedical text currently focuses mostly on entity and relation extraction. These entities and relations are usually pre-specified entities, e.g., proteins, and pre-specified relations, e.g., inhibit relations. A shallow parser that captures the relations between noun phrases automatically from free text has been developed and evaluated. It uses heuristics and a noun phraser to capture entities of interest in the text. Cascaded finite state automata structure the relations between individual entities. The automata are based on closed-class English words and model generic relations not limited to specific words. The parser also recognizes coordinating conjunctions and captures negation in text, a feature usually ignored by others. Three cancer researchers evaluated 330 relations extracted from 26 abstracts of interest to them. There were 296 relations correctly extracted from the abstracts resulting in 90% precision of the relations and an average of 11 correct relations per abstract.

We describe here the context of the LLL challenge of Genic Interaction extraction, the background of its organization and the data sets. We discuss then the results of the participating systems.

this paper is to treat the protein as a vector of terms from relevant Medline documents. This approach derives from the vector-based model common in information retrieval 8. The term weights of a vector are a functions of their frequencies within the document collection as a whole and the frequency within the relevant documents. Given a set of protein term-vectors the task is to find some function that partitions the space according to the localisation of the protein. For this task we employ support vector machines (SVM) 9 Support vector machines are a mathematical method for performing si- multaneous dimension reduction and binary classification 9. SVMs have been applied to the problems of pattern recognition 10, regression estimation l0 and information retrieval ,?. Because SVMs cope well with high dimensionality and are very fast to train, they are particularly suited to problems in text data-mining/information retrieval. Kwok studied the use of SVMs in text catagorization of Reuters newswire documents 2. In this paper, we apply an analogous approach to Medline/SWISS-PROT documents

Motivation: New high-throughput technologies have accelerated the accumulation of knowledge about genes and proteins. However, much knowledge is still stored as written natural language text. Therefore, we have developed a new method, GAPSCORE, to identify gene and protein names in text. GAPSCORE scores words based on a statistical model of gene names that quantifies their appearance, morphology and context.

Many natural language processing approaches at various complexity levels have been reported for extracting biochemical interactions from MEDLINE. While some algorithms using simple template matching are unable to deal with the complex syntactic structures, others exploiting sophisticated parsing techniques are hindered by greater computational cost. This study investigates link grammar parsing for extracting biochemical interactions. Link grammar parsing can handle many syntactic structures and is computationally relatively efficient. We experimented on a sample MEDLINE corpus. Although the parser was originally developed for conversational English and made many mistakes in parsing sentences from the biochemical domain, it nevertheless achieved better overall performance than a co-occurrence-only method. Customizing the parser for the biomedical domain is expected to improve its performance further. 1.

doi:10.1093/bioinformatics/bti493