Proceedings of the BioNLP 08 ACL Workshop: Themes in biomedical language processing

Themes in biomedical natural language processing: BioNLP08

Authors: Dina Demner-Fushman, Sophia Ananiadou, K Bretonnel Cohen, John Pestian, Jun'ichi Tsujii and Bonnie Webber

All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning

Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier appr...

Authors: Antti Airola, Sampo Pyysalo, Jari Björne, Tapio Pahikkala, Filip Ginter and Tapio Salakoski

Mining clinical relationships from patient narratives

The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order to support clinical research, evidence-ba...

Authors: Angus Roberts, Robert Gaizauskas, Mark Hepple and Yikun Guo

Cascaded classifiers for confidence-based chemical named entity recognition

Chemical named entities represent an important facet of biomedical text.

Authors: Peter Corbett and Ann Copestake

How to make the most of NE dictionaries in statistical NER

When term ambiguity and variability are very high, dictionary-based Named Entity Recognition (NER) is not an ideal solution even though large-scale terminological resources are available. Many researches on stati...

Authors: Yutaka Sasaki, Yoshimasa Tsuruoka, John McNaught and Sophia Ananiadou

Distinguishing the species of biomedical named entities for term identification

Term identification is the task of grounding ambiguous mentions of biomedical named entities in text to unique database identifiers. Previous work on term identification has focused on studying species-specifi...

Authors: Xinglong Wang and Michael Matthews

Disambiguation of biomedical text using diverse sources of information

Like text in other domains, biomedical documents contain a range of terms with more than one possible meaning. These ambiguities form a significant obstacle to the automatic processing of biomedical texts. Pre...

Authors: Mark Stevenson, Yikun Guo, Robert Gaizauskas and David Martinez

Accelerating the annotation of sparse named entities by dynamic sentence selection

Previous studies of named entity recognition have shown that a reasonable level of recognition accuracy can be achieved by using machine learning models such as conditional random fields or support vector mach...

Authors: Yoshimasa Tsuruoka, Jun'ichi Tsujii and Sophia Ananiadou

The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes

Detecting uncertain and negative assertions is essential in most BioMedical Text Mining tasks where, in general, the aim is to derive factual knowledge from textual data. This article reports on a corpus annot...

Authors: Veronika Vincze, György Szarvas, Richárd Farkas, György Móra and János Csirik

Recognizing speculative language in biomedical research articles: a linguistically motivated perspective

Due to the nature of scientific methodology, research articles are rich in speculative and tentative statements, also known as hedges. We explore a linguistically motivated approach to the problem of recognizi...

Authors: Halil Kilicoglu and Sabine Bergler

Automatic inference of indexing rules for MEDLINE

Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to...

Authors: Aurélie Névéol, Sonya E Shooshan and Vincent Claveau