Volume 9 Supplement 11
Proceedings of the BioNLP 08 ACL Workshop: Themes in biomedical language processing
Research
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
-
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S2 -
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S3 -
Cascaded classifiers for confidence-based chemical named entity recognition
Chemical named entities represent an important facet of biomedical text.
Citation: BMC Bioinformatics 2008 9(Suppl 11):S4 -
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S5 -
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S6 -
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S7 -
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S8 -
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S9 -
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S10 -
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...
Citation: BMC Bioinformatics 2008 9(Suppl 11):S11
Annual Journal Metrics
-
Citation Impact 2023
Journal Impact Factor: 2.9
5-year Journal Impact Factor: 3.6
Source Normalized Impact per Paper (SNIP): 0.821
SCImago Journal Rank (SJR): 1.005
Speed 2023
Submission to first editorial decision (median days): 12
Submission to acceptance (median days): 146
Usage 2023
Downloads: 5,987,678
Altmetric mentions: 4,858