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

We approached the problems of event detection, argument identification, and negation and speculation detection as one of concept recognition and analysis. Our methodology involved using the OpenDMAP semantic parser with manually-written rules. We achieved state-of-the-art precision for two

The second BioNLP Shared Task on Event Extraction (BioNLP-ST’11) follows up the previous shared task competition with a focus on generalization with respect to text types, event types and subject domains. In this spirit, we re-engineered and extended our event extraction system, emphasizing

This paper presents our approach (referred to as BioEvent) for protein-level complex event extraction, developed for the GENIA task (Kim et al., 2011b) of the BioNLP Shared Task 2011 (Kim et al., 2011a). We developed a double layered machine learning approach which utilizes a state

Building on technical advances from the BioNLP 2009 Shared Task Challenge, the 2011 challenge sets forth to generalize techniques to other complex biological event extraction tasks. In this paper, we present the implementation and evaluation of a signaturebased machine-learning technique to predict

We approached the problems of event detection, argument identification, and negation and speculation de-tection in the BioNLP’09 information extraction challenge through concept recognition and analysis. Our method-ology involved using the OpenDMAP semantic parser with manually-written rules

Abstract. Programmed cell death (PCD) plays a key role in developmental biology and in maintenance of the steady state in continuously renewing tissues. Currently, its existence is inferred mainly from gel electrophoresis of a pooled DNA extract as PCD was shown to be associated with DNA

that solves the optimization problem in polynomial time for a large class of problems. The proposed method has important applications in areas such as computational biology, natural language processing, information retrieval/extraction, and optical character recognition. Experiments from various domains

‘‘emotion’ ’ and ‘‘feelings’ ’ neverspecific circuits and mechanisms in the brain that underlie emotional phenomena. an organism detects and responds to significant events in the course of surviving and/or maintaining well-being—for example,the earliest days in modern biology and psychology (e.g., Dar-

to regional neural activity. Magnetic resonance imaging (MRI) is a widely accepted modality for providing anatomical information. Current research (1) involves extending MRI methods to provide information about biological function, in addition to the concomitant anatomical information. In addition