We explore a rule-based methodology for the BioNLP’09 Shared Task on Event Extraction, using dependency parsing as the underlying principle for extracting and characterizing events. We approach the speculation and negation detection task with the same principle. Evaluation results demonstrate the utility of this syntax-based approach and point out some shortcomings that need to be addressed in future work. 1

Finding negation signals and their scope in text is an important subtask in information extraction. In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system combines several classifiers and works in two phases. To investigate the robustness of the approach, the system is tested on the three subcorpora of the BioScope corpus representing different text types. It achieves the best results to date for this task, with an error reduction of 32.07% compared to current state of the art results. 1

{alan, olivier, demnerd, kwfung, mork, neveola, peters, wrogers}

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

Applications using automatically indexed clinical conditions must account for contextual features such as whether a condition is negated, historical or hypothetical, or experienced by someone other than the patient. We developed and evaluated an algorithm called ConText, an extension of the NegEx negation algorithm, which relies on trigger terms, pseudo-trigger terms, and termination terms for identifying the values of three contextual features. In spite of its simplicity, ConText performed well at identifying negation and hypothetical status. ConText performed moderately at identifying whether a condition was experienced by someone other than the patient and whether the condition occurred historically. 1

With the rapidly growing use of electronic health records, the possibility of large-scale clinical information extraction has drawn much attention. It is not, however, easy to extract information because these reports are written in natural language. To address this problem, this paper presents a system that converts a medical text into a table structure. This system’s core technologies are (1) medical event recognition modules and (2) a negative event identification module that judges whether an event actually occurred or not. Regarding the latter module, this paper also proposes an SVM-based classifier using syntactic information. Experimental results demonstrate empirically that syntactic information can contribute to the method’s accuracy. 1

Abstract. In this paper we describe the task Processing modality and negation for machine reading, which was organized as a pilot task of the Question Answering for Machine Reading Evaluation (QA4MRE) Lab at CLEF 2011. We define the aspects of meaning on which the task focused and we describe the dataset produced. 1