Automatic Negation and Speculation Detection in Veterinary Clinical Text

Automatic Negation and Speculation Detection in Veterinary Clinical Text Katharine Cheng Timothy Baldwin Karin Verspoor School of Computing and Information Systems The University of Melbourne Australia katharinec@student.unimelb.edu.au tb@ldwin.net karin.verspoor@unimelb.edu.au Abstract The automatic detection of negation and speculation in clinical notes is vital when searching for genuine instances of a given phenomenon. This paper describes a new corpus of negation and speculation data, in the veterinary clinical note domain, and describes a series of experiments whereby we port a CRF-based method across from the BioScope corpus to this novel domain. 1 Introduction Negation and speculation are common in clinical texts, yet pose a challenge for natural language processing of these texts. Negation indicates the absence or opposite of something, and is defined within the previously released BioScope corpus (a collection of biomedical and clinical documents annotated for the task of negation/speculation de- tection) to be the “implication of the non-existence of something” (Szarvas et al., 2008). For exam- ple, the statement no abnormalities were found in the patient indicates the absence of abnormal- ities in the patient. Speculation is used to indi- cate uncertainty or the possibility of something, and is defined within BioScope to be statements of “the possible existence of something”. For ex- ample, there is possible bacterial infection indi- cates that an infection might be present, without any certainty that it is. Both are commonly used in clinical texts as a means of ruling out diagnos- tic possibilities and hypothesising. This paper will discuss a method for detect- ing negation and speculation over clinical records from the Veterinary Companion Animal Surveil- lance System (VetCompass) project.1 The Vet- Compass project is a database of veterinary clin- ical records for tracking animal health. The database may be used for research on the effects 1http://www.rvc.ac.uk/vetcompass and usage of a particular drug, or the prevalence and distribution of a disease. Such studies are typ- ically performed by querying for terms relevant to a drug or disease of interest, and analysing the re- trieved clinical records. However, results identi- fied using keyword matching are often speculative or negated mentions rather than true occurrences. By automatically detecting negation and specula- tion, we aim to suppress these results, and provide a higher-utility set of documents to the user. The task of negation/speculation detection is often defined in terms of two subtasks: (1) sig- nal (or cue) detection; and (2) scope detection. Negation/speculation signal (or cue) detection in- volves determining which words in a sentence indicate that a negation/speculation is occurring. Negation/speculation scope detection involves de- termining which words in a sentence the nega- tion/speculation applies to, under the constraints that: (a) the cue word is contained within the span of the scope; and (b) the span is contiguous. Con- sider two examples from the clinical notes subset of the BioScope corpus: (1) The lungs are well expanded, but [[NEG not hyperin- flated NEG]]. (2) Mild thoracic curvature, [[SPEC possibly positional SPEC]]. The cues here for negation and speculation are not and possibly, respectively, and the …

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