This article is part of the supplement: Selected articles from the BioNLP Shared Task 2011
Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach
1 MIG INRA UR1077 Domaine de Vilvert, F-78352 Jouy-en-Josas, France
2 LaTTiCe UMR 8094 CNRS Université Paris 3, 1 rue Maurice Arnoux, F-92120 Montrouge, France
3 LIG - Université Joseph Fourier, 385, rue de la Bibliothèque, BP 53, F-38400 Saint-Martin-d'Hères, France
Citation and License
BMC Bioinformatics 2012, 13(Suppl 11):S8 doi:10.1186/1471-2105-13-S11-S8Published: 26 June 2012
Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field.
We present a new method for extracting relationships between bacteria and their locations using the Alvis framework. Recognition of bacteria and their locations was achieved using a pattern-based approach and domain lexical resources. For the detection of environment locations, we propose a new approach that combines lexical information and the syntactic-semantic analysis of corpus terms to overcome the incompleteness of lexical resources. Bacteria location relations extend over sentence borders, and we developed domain-specific rules for dealing with bacteria anaphors.
We participated in the BioNLP 2011 Bacteria Biotope (BB) task with the Alvis system. Official evaluation results show that it achieves the best performance of participating systems. New developments since then have increased the F-score by 4.1 points.
We have shown that the combination of semantic analysis and domain-adapted resources is both effective and efficient for event information extraction in the bacteria biotope domain. We plan to adapt the method to deal with a larger set of location types and a large-scale scientific article corpus to enable microbiologists to integrate and use the extracted knowledge in combination with experimental data.