Learning the Scope of Negation in Biomedical Texts

In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system consists of two memory-based engines, one that decides if the tokens in a sentence are negation signals, and another that finds the full scope of these negation signals. Our approach to negation detection differs in two main aspects from existing research on negation. First, we focus on finding the scope of negation signals, instead of determining whether a term is negated or not. Second, we apply supervised machine learning techniques, whereas most existing systems apply rule-based algorithms. As far as we know, this way of approaching the negation scope finding task is novel.

[1]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[2]  Richard Johansson,et al.  The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies , 2008, CoNLL.

[3]  János Csirik,et al.  The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts , 2008, BioNLP.

[4]  Sophia Ananiadou,et al.  Developing a Robust Part-of-Speech Tagger for Biomedical Text , 2005, Panhellenic Conference on Informatics.

[5]  Mitchell P. Marcus,et al.  Text Chunking using Transformation-Based Learning , 1995, VLC@ACL.

[6]  Ilya M. Goldin,et al.  Learning to Detect Negation with ‘Not’ in Medical Texts , 2003 .

[7]  Long H. Ngo,et al.  Implementation and Evaluation of Four Different Methods of Negation Detection , 2007 .

[8]  Sebastian Riedel,et al.  The CoNLL 2007 Shared Task on Dependency Parsing , 2007, EMNLP.

[9]  Massimo Poesio,et al.  Negation of protein-protein interactions: analysis and extraction , 2007, ISMB/ECCB.

[10]  Yang Huang,et al.  A novel hybrid approach to automated negation detection in clinical radiology reports. , 2007, Journal of the American Medical Informatics Association : JAMIA.

[11]  Jun'ichi Tsujii,et al.  Bidirectional Inference with the Easiest-First Strategy for Tagging Sequence Data , 2005, HLT.

[12]  Prakash M. Nadkarni,et al.  Research Paper: Use of General-purpose Negation Detection to Augment Concept Indexing of Medical Documents: A Quantitative Study Using the UMLS , 2001, J. Am. Medical Informatics Assoc..

[13]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[14]  Steven Salzberg,et al.  A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features , 2004, Machine Learning.

[15]  Peter L. Elkin,et al.  A controlled trial of automated classification of negation from clinical notes , 2005, BMC Medical Informatics Decis. Mak..

[16]  Sabine Buchholz,et al.  CoNLL-X Shared Task on Multilingual Dependency Parsing , 2006, CoNLL.

[17]  Svetla Boytcheva,et al.  Some Aspects of Negation Processing in Electronic Health Records , 2005 .

[18]  Lior Rokach,et al.  Negation recognition in medical narrative reports , 2008, Information Retrieval.

[19]  Walter Daelemans,et al.  Memory-Based Language Processing , 2009, Studies in natural language processing.

[20]  Beatrice Santorini.  Part-of-speech tagging guidelines for the penn treebank project , 1990 .

[21]  Roser Morante,et al.  A Combined Memory-Based Semantic Role Labeler of English , 2008, CoNLL.

[22]  Walter Daelemans,et al.  TiMBL: Tilburg Memory-Based Learner, version 2.0, Reference guide , 1998 .

[23]  Beatrice Santorini,et al.  Part-of-Speech Tagging Guidelines for the Penn Treebank Project (3rd Revision) , 1990 .

[24]  Panayiotis Bozanis,et al.  Advances in Informatics: 10th Panhellenic Conference on Informatics, PCI 2005, Volas, Greece, November 11-13, 2005, Proceedings (Lecture Notes in Computer Science) , 2005 .

[25]  Wendy W. Chapman,et al.  A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries , 2001, J. Biomed. Informatics.

[26]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[27]  Lior Rokach,et al.  Context-Sensitive Medical Information Retrieval , 2004, MedInfo.

[28]  Nigel Collier,et al.  The GENIA project: corpus-based knowledge acquisition and information extraction from genome research papers , 1999, EACL.

[29]  Walter Daelemans,et al.  Forgetting Exceptions is Harmful in Language Learning , 1998, Machine Learning.

[30]  Thomas G. Dietterich.  Machine Learning for Sequential Data: A Review , 2002, SSPR/SPR.