IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient string matching: an aid to bibliographic search
Communications of the ACM
Introduction to algorithms
Fast and Practical Approximate String Matching
CPM '92 Proceedings of the Third Annual Symposium on Combinatorial Pattern Matching
Learning to match and cluster large high-dimensional data sets for data integration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Biomedical named entity recognition using two-phase model based on SVMs
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Analysis of Statistical Question Classification for Fact-Based Questions
Information Retrieval
The MIPS mammalian protein--protein interaction database
Bioinformatics
Rich features based Conditional Random Fields for biological named entities recognition
Computers in Biology and Medicine
A Cascaded Approach to Biomedical Named Entity Recognition Using a Unified Model
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Generating complex ontology instances from documents
Journal of Algorithms
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Incorporating lexical knowledge into biomedical NE recognition
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Annotating multiple types of biomedical entities: a single word classification approach
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Named entity recognition in biomedical texts using an HMM model
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploiting context for biomedical entity recognition: from syntax to the web
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Adapting an NER-system for German to the biomedical domain
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploring deep knowledge resources in biomedical name recognition
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Brief Communication: Two-phase biomedical named entity recognition using CRFs
Computational Biology and Chemistry
Classifying what-type questions by head noun tagging
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Scalable biomedical Named Entity Recognition: investigation of a database-supported SVM approach
International Journal of Bioinformatics Research and Applications
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The majority of biological functions of any living being are related to Protein-Protein Interactions (PPI). PPI discoveries are reported in form of research publications whose volume grows day after day. Consequently, automatic PPI information extraction systems are a pressing need for biologists. In this paper we are mainly concerned with the named entity detection module of PPIES (the PPI information extraction system we are implementing) which recognizes twelve entity types relevant in PPI context. It is composed of two sub-modules: a dictionary look-up with extensive normalization and acronym detection, and a Conditional Random Field classifier. The dictionary look-up module has been tested with Interaction Method Task (IMT), and it improves by approximately 10% the current solutions that do not use Machine Learning (ML). The second module has been used to create a classifier using the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA'04) data set. It does not use any external resources, or complex or ad hoc post-processing, and obtains 77.25%, 75.04% and 76.13 for precision, recall, and F1-measure, respectively, improving all previous results obtained for this data set.