Title
Machine Reading for Extraction of Bacteria and Habitat Taxonomies.
Abstract
There is a vast amount of scientific literature available from various resources such as the internet. Automating the extraction of knowledge from these resources is very helpful for biologists to easily access this information. This paper presents a system to extract the bacteria and their habitats, as well as the relations between them. We investigate to what extent current techniques are suited for this task and test a variety of models in this regard. We detect entities in a biological text and map the habitats into a given taxonomy. Our model uses a linear chain Conditional Random Field (CRF). For the prediction of relations between the entities, a model based on logistic regression is built. Designing a system upon these techniques, we explore several improvements for both the generation and selection of good candidates. One contribution to this lies in the extended flexibility of our ontology mapper that uses an advanced boundary detection and assigns the taxonomy elements to the detected habitats. Furthermore, we discover value in the combination of several distinct candidate generation rules. Using these techniques, we show results that are significantly improving upon the state of art for the BioNLP Bacteria Biotopes task.
Year
DOI
Venue
2015
10.1007/978-3-319-27707-3_15
BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, BIOSTEC 2015
Field
DocType
Volume
Conditional random field,Ontology,Data mining,Scientific literature,Habitat,Computer science,Biomedical text mining,Artificial intelligence,Machine reading,Machine learning,Relationship extraction,The Internet
Conference
574
ISSN
Citations 
PageRank 
1865-0929
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Parisa Kordjamshidi114318.52
Wouter Massa200.34
Thomas Provoost300.34
Marie-Francine Moens41750139.27