Dealing with Data Sparsity in Drug Named Entity Recognition

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Piliouras:2013:ICHI,

• author = "Dimitrios Piliouras and Ioannis Korkontzelos and Andrew Dowsey and Sophia Ananiadou",
• title = "Dealing with Data Sparsity in Drug Named Entity Recognition",
• booktitle = "IEEE International Conference on Healthcare Informatics (ICHI 2013)",
• year = "2013",
• month = sep,
• pages = "14--21",
• keywords = "genetic algorithms, genetic programming, artificial intelligence, drugs, medical computing, natural language processing, pattern classification, BioNLP tasks, automatic annotations, biomedical natural language processing tasks, data sparsity, drug named entity recognition, drug-NER, gold-standard data, manual annotations, voting system, Data models, Dictionaries, Drugs, Proteins, Training, Training data, data-sparsity",
• DOI = "doi:10.1109/ICHI.2013.9",
• abstract = "Drug Named Entity Recognition (drug-NER) is a critical step for complex Biomedical Natural Language Processing (BioNLP) tasks such as the extraction of pharmaco-genomic, pharmaco-dynamic and pharmaco-kinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning (ML) techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a detrimental limitation. In this study, we attempt to improve the performance of drug NER without relying exclusively on manual annotations. Instead, we use either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we use a voting system to combine a number of heterogeneous models to enhance performance. Moreover, 11 regular-expressions that capture common drug suffixes were evolved via genetic-programming. We evaluate our approach against state-of-the-art recognisers trained on manual annotations, automatic annotations and a mixture of both. Aggregate classifiers are shown to improve performance, achieving a maximum F-score of 95percent. In addition, combined models trained on mixed data are shown to achieve comparable performance to models trained exclusively on gold-standard data.",
• notes = "DrugBank, PK PharmacoKinetic corpus, 360 articles, maximum-entropy maxent.sf openNLP, 8 features per token, ANN perceptron. p17 Silver data='anotated by direct string matching dictionary entries', AcroMine negative, p18 GP Evolving strin-simularity patterns (ie regular expressions) USAN stem grouping restrictiing to _last_ 4,5, o6 characters gives 'major positive effect'. 200*(pop=10000,generations=80). anti-bloat (max tree depth=10 no space character in terminal set) p19 best-evolved GP tree (fig and Table 3). p19 'gold-standard data not' needed for drug-NER. 2013 and still says 'data sparsity is pervasive...' p20 data not split into ttraining and holdout sets. Also known as \cite{6680456}",

}

Genetic Programming entries for Dimitrios Piliouras Ioannis Korkontzelos Andrew Dowsey Sophia Ananiadou

Citations