Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries
暂无分享,去创建一个
Xiaolong Wang | Qingcai Chen | Buzhou Tang | Shengyu Liu | Xiaolong Wang | Qingcai Chen | Buzhou Tang | Shengyu Liu
[1] Min Li,et al. High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge , 2010, J. Am. Medical Informatics Assoc..
[2] Yoshua Bengio,et al. Hierarchical Probabilistic Neural Network Language Model , 2005, AISTATS.
[3] Zhiyong Lu,et al. tmChem: a high performance approach for chemical named entity recognition and normalization , 2015, Journal of Cheminformatics.
[4] Isabel Segura-Bedmar,et al. Drug name recognition and classification in biomedical texts. A case study outlining approaches underpinning automated systems. , 2008, Drug discovery today.
[5] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[6] Jun Zhao,et al. How to Generate a Good Word Embedding , 2015, IEEE Intelligent Systems.
[7] Yoshua Bengio,et al. A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..
[8] Ulf Leser,et al. WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugs , 2013, *SEMEVAL.
[9] Burr Settles,et al. Biomedical Named Entity Recognition using Conditional Random Fields and Rich Feature Sets , 2004, NLPBA/BioNLP.
[10] Jason Weston,et al. Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..
[11] Paloma Martínez,et al. Combining dictionaries and ontologies for drug name recognition in biomedical texts , 2013, DTMBIO '13.
[12] Martijn J. Schuemie,et al. A dictionary to identify small molecules and drugs in free text , 2009, Bioinform..
[13] Francisco M. Couto,et al. LASIGE: using Conditional Random Fields and ChEBI ontology , 2013, SemEval@NAACL-HLT.
[14] José Luís Oliveira,et al. A document processing pipeline for annotating chemical entities in scientific documents , 2015, Journal of Cheminformatics.
[15] Fernando Pereira,et al. Identifying gene and protein mentions in text using conditional random fields , 2005, BMC Bioinformatics.
[16] Christopher D. Manning,et al. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.
[17] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[18] Xiaolong Wang,et al. Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks , 2014, BioMed research international.
[19] Xiaolong Wang,et al. A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature , 2015, Journal of Cheminformatics.
[20] Andrew Y. Ng,et al. Improving Word Representations via Global Context and Multiple Word Prototypes , 2012, ACL.
[21] Hongfei Lin,et al. Drug name recognition in biomedical texts: a machine-learning-based method. , 2014, Drug discovery today.
[22] Yoshua Bengio,et al. Word Representations: A Simple and General Method for Semi-Supervised Learning , 2010, ACL.
[23] David S. Wishart,et al. DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..
[24] Lukás Burget,et al. Recurrent neural network based language model , 2010, INTERSPEECH.
[25] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[26] Wei Li,et al. Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons , 2003, CoNLL.
[27] Xiaohui Liang,et al. CHEMDNER system with mixed conditional random fields and multi-scale word clustering , 2015, Journal of Cheminformatics.
[28] Peter W. Foltz,et al. An introduction to latent semantic analysis , 1998 .
[29] Paloma Martínez,et al. SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013) , 2013, *SEMEVAL.
[30] Daniel Sánchez-Cisneros,et al. UEM-UC3M: An Ontology-based named entity recognition system for biomedical texts. , 2013, *SEMEVAL.
[31] Patrick F. Reidy. An Introduction to Latent Semantic Analysis , 2009 .
[32] Andrew McCallum,et al. Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..
[33] Geoffrey E. Hinton,et al. A Scalable Hierarchical Distributed Language Model , 2008, NIPS.
[34] Siddhartha Jonnalagadda,et al. Enhancing clinical concept extraction with distributional semantics , 2012, J. Biomed. Informatics.
[35] Hua Xu,et al. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries , 2011, J. Am. Medical Informatics Assoc..
[36] Andrew McCallum,et al. Chinese Segmentation and New Word Detection using Conditional Random Fields , 2004, COLING.
[37] Olivier Bodenreider,et al. The NLM Indexing Initiative , 2000, AMIA.
[38] Jari Björne,et al. UTurku: Drug Named Entity Recognition and Drug-Drug Interaction Extraction Using SVM Classification and Domain Knowledge , 2013, SemEval@NAACL-HLT.
[39] Maria Kvist,et al. Identifying adverse drug event information in clinical notes with distributional semantic representations of context , 2015, J. Biomed. Informatics.
[40] Son Doan,et al. Recognition of medication information from discharge summaries using ensembles of classifiers , 2012, BMC Medical Informatics and Decision Making.
[41] Dan Roth,et al. Design Challenges and Misconceptions in Named Entity Recognition , 2009, CoNLL.
[42] Robert L. Mercer,et al. Class-Based n-gram Models of Natural Language , 1992, CL.
[43] Erik F. Tjong Kim Sang,et al. Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition , 2003, CoNLL.
[44] Maria Kvist,et al. Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study , 2014, J. Biomed. Informatics.
[45] Alfonso Valencia,et al. CHEMDNER: The drugs and chemical names extraction challenge , 2015, Journal of Cheminformatics.
[46] Isabel Segura-Bedmar,et al. The 1st DDIExtraction-2011 challenge task: Extraction of Drug-Drug Interactions from biomedical texts , 2011 .
[47] Sophia Ananiadou,et al. Optimising chemical named entity recognition with pre-processing analytics, knowledge-rich features and heuristics , 2015, Journal of Cheminformatics.
[48] Andrew McCallum,et al. Lexicon Infused Phrase Embeddings for Named Entity Resolution , 2014, CoNLL.
[49] Rafael Muñoz,et al. UMCC_DLSI: Semantic and Lexical features for detection and classification Drugs in biomedical texts , 2013, SemEval@NAACL-HLT.
[50] Maksim Tkatchenko,et al. Named entity recognition: Exploring features , 2012, KONVENS.
[51] Son Doan,et al. Application of information technology: MedEx: a medication information extraction system for clinical narratives , 2010, J. Am. Medical Informatics Assoc..
[52] Fei Xia,et al. A cascade of classifiers for extracting medication information from discharge summaries , 2011, J. Biomed. Semant..
[53] Sabine Buchholz,et al. Introduction to the CoNLL-2000 Shared Task Chunking , 2000, CoNLL/LLL.
[54] Beatrice Santorini,et al. Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.