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Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all(More)
Association rules are one of the most used tools to discover relationships among attributes in a database. Nowadays, there are many efficient techniques to obtain these rules, although most of them require that the values of the attributes be discrete. To solve this problem, these techniques discretize the numeric attributes, but this implies a loss of(More)
Text Classification tasks are becoming increasingly popular in the field of Information Access. Being approached as Machine Learning problems, the definition of suitable attributes for each task is approached in an ad-hoc way. We believe that a more principled framework is required, and we present initial insights on attribute engineering for Text(More)
Drug-Drug Interaction (DDI) extraction from the pharmacological literature is an emergent challenge in the text mining area. In this paper we describe a DDI extraction system based on a machine learning approach. We propose distinct solutions to deal with the high dimensionality of the problem and the unbalanced representation of classes in the dataset. On(More)
One of the most promising approaches to Cross-Language Information Retrieval is the utilization of lexical-semantic resources for concept-indexing documents and queries. We have followed this approach in a proposal of an Information Access system designed for medicine professionals, aiming at easing the preparation of clinical cases, and the development of(More)
One of the areas which is presently awakening more interest among researchers and users of Information Retrieval systems is the retrieval of documents containing images which are relevant to a need for information. In this case, the main objective is not the retrieval of the documents relevant to the user's need for information, but the achievement of the(More)
In this paper, we present a machine learning system that identify the negation and speculation signals in biomedical texts, in particular, in the BioScope corpus. The objective of this research is to compare the efficiency of this approach focused on machine learning with which it is based on regular expressions. Among the systems that follow the latter(More)
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