Abstract
BackgroundDetecting uncertain and negative assertions is essential in most BioMedical Text Mining tasks where, in general, the aim is to derive factual knowledge from textual data. This article reports on a corpus annotation project that has produced a freely available resource for research on handling negation and uncertainty in biomedical texts (we call this corpus the BioScope corpus).ResultsThe corpus consists of three parts, namely medical free texts, biological full papers and biological scientific abstracts. The dataset contains annotations at the token level for negative and speculative keywords and at the sentence level for their linguistic scope. The annotation process was carried out by two independent linguist annotators and a chief linguist – also responsible for setting up the annotation guidelines – who resolved cases where the annotators disagreed. The resulting corpus consists of more than 20.000 sentences that were considered for annotation and over 10% of them actually contain one (or more) linguistic annotation suggesting negation or uncertainty.ConclusionStatistics are reported on corpus size, ambiguity levels and the consistency of annotations. The corpus is accessible for academic purposes and is free of charge. Apart from the intended goal of serving as a common resource for the training, testing and comparing of biomedical Natural Language Processing systems, the corpus is also a good resource for the linguistic analysis of scientific and clinical texts.
Highlights
Detecting uncertain and negative assertions is essential in most BioMedical Text Mining tasks where, in general, the aim is to derive factual knowledge from textual data
We elaborate on the overall characteristics of the corpus we developed, including a brief description of the texts that constitute the BioScope corpus and some general statistics concerning the size of each part, distribution of negation/hedge cues and ambiguity levels, we
In this paper we reported on the construction of a corpus annotated for negations, speculations and their linguistic scopes
Summary
Detecting uncertain and negative assertions is essential in most BioMedical Text Mining tasks where, in general, the aim is to derive factual knowledge from textual data. Detecting uncertain and negative assertions is essential in most Text Mining tasks where, in general, the aim is to derive factual knowledge from textual data This is especially so for many tasks in the biomedical (medical and biological) domain, where these language forms are used (page number not for citation purposes). For example, the clinical coding of medical reports, where the coding of a negative or uncertain disease diagnosis may result in an over-coding financial penalty Another example from the biological domain is interaction extraction, where the aim is to mine text evidence for biological entities with certain relations between them. A general conclusion is that for text mining, extracted information that is within the scope of some negative/speculative (hedge or soft negation) keyword should either be discarded or presented separately from factual information
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