Pharmacovigilance (PV) is defined by the World Health Organization as the science and activities related to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem. An essential aspect in PV is to acquire knowledge about Drug-Drug Interactions (DDIs). The shared tasks on DDI-Extraction organized in 2011 and 2013 have pointed out the importance of this issue and provided benchmarks for: Drug Name Recognition, DDI extraction and DDI classification. In this paper, we present our text mining systems for these tasks and evaluate their results on the DDI-Extraction benchmarks. Our systems rely on machine learning techniques using both feature-based and kernel-based methods. The obtained results for drug name recognition are encouraging. For DDI-Extraction, our hybrid system combining a feature-based method and a kernel-based method was ranked second in the DDI-Extraction-2011 challenge, and our two-step system for DDI detection and classification was ranked first in the DDI-Extraction-2013 task at SemEval. We discuss our methods and results and give pointers to future work.
Motivated by very recent work on 2-connectivity in directed graphs, we revisit the problem of computing the 2-edge-and 2-vertex-connected components, and the maximal 2-edge-and 2-vertex-connected subgraphs of a directed graph G. We explore the design space for efficient algorithms in practice, based on recently proposed techniques, and conduct a thorough empirical study to highlight the merits and weaknesses of each technique.
This paper describes our participation in task 14 of SemEval 2015. This task focuses on the analysis of clinical texts and includes: (i) the recognition of the span of a disorder mention and (ii) its normalization to a unique concept identifier in the UMLS/SNOMED-CT terminology. We propose a two-step approach which relies first on Conditional Random Fields to detect textual mentions of disorders using different lexical, syntactic, orthographic and semantic features such as ontologies and, second, on a similarity measure and SNOMED to determine the relevant CUI. We present and discuss the obtained results on the development corpus and the official test corpus.
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