Objective: Evaluate the use of negation phrases and the frequency of negation in free-text clinical reports. Methods: A simple negation algorithm was applied to ten types of clinical reports (n=42,160) dictated during July2000. We counted how often each of 66 negation phrases was used to mark a

Objective: Automatically identifying findings or diseases described in clinical textual reports requires determining whether clinical observations are present or absent. We evaluate the use of negation phrases and the frequency of negation in free-text clinical reports. Methods:. A simple negation

Background: Research suggests that phrases with negative content can affect patients' response to medical procedures and how they cope with medical illnesses. We hypothesized that patients with lateral epicondylitis who describe their condition in positive phrases cope better than those who

. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual

In this paper, I give a semantic account of the grammaticality of the negative particle not with noun phrases in English. On the way to developing my solution, I explore a few previous attempts at this problem, including an extension of Horn’s discussion of the availability of NEG-Q readings (Horn

on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80 % up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7 % over

In this paper I investigate the interpretation of the Spanish free-choice (FC) indefinite cualquier (any). My goal is to explain the following: (1) cualquier licenses exception phrases: (1) Puedes comprar cualquier libro excepto uno sobre conejos

In internet advertising, negative key phrases are used in order to exclude the display of an advertisement to non-target audience. We describe a method for automatically identifying negative key phrases. We use Wikipedia as our sense inventory and as an annotated corpus from which we create context

here was to test a simple algorithm for determining whether a finding or disease mentioned within narrative medical reports is present or absent. We developed a simple regular expression algorithm called NegEx that implements several phrases indicating negation, filters out sentences containing phrases

are useful, as we discuss in this paper. We approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set