Citation Impact
3.242 - 2-year Impact Factor
3.213 - 5-year Impact Factor
1.156 - Source Normalized Impact per Paper (SNIP)
1.626 - SCImago Journal Rank (SJR)
Usage
4,058,323 downloads
Social Media Impact
6067 mentions
Volume 9 Supplement 11
Edited by Dina Demner-Fushman, K Bretonnel Cohen, Sophia Ananiadou, John Pestian, Jun'ichi Tsujii and Bonnie Webber
Natural Language Processing in Biomedicine (BioNLP) ACL Workshop 2008. Go to conference site.
Columbus, OH, USA19 June 2008
Citation: BMC Bioinformatics 2008 9(Suppl 11):S1
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S2
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S3
Chemical named entities represent an important facet of biomedical text.
Citation: BMC Bioinformatics 2008 9(Suppl 11):S4
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S5
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S6
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S7
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S8
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S9
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S10
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S11
Citation Impact
3.242 - 2-year Impact Factor
3.213 - 5-year Impact Factor
1.156 - Source Normalized Impact per Paper (SNIP)
1.626 - SCImago Journal Rank (SJR)
Usage
4,058,323 downloads
Social Media Impact
6067 mentions