The growing availability of on-line textual sources and the potential number of applications of knowledge acquisition from textual data has lead to an increase in Information Extraction (IE) research. Some examples of these applications are the generation of data bases from documents, as well as the acquisition of knowledge useful for emerging technologies like question answering, information integration, and others related to text mining. However, one of the main drawbacks of the application of IE refers to its intrinsic domain dependence. For the sake of reducing the high cost of manually adapting IE applications to new domains, experiments with different Machine Learning (ML) techniques have been carried out by the research community. This survey describes and compares the main approaches to IE and the different ML techniques used to achieve Adaptive IE technology.
This article deals with the problem of applying observability techniques to structural system identification, understanding as such the problem of identifying which is the subset of characteristics of the structure, such as Young's modulus, area, inertia, and/or product of them (flexural or axial stiffnesses) that can be uniquely defined when an adequate subset of deflections, forces, and/or moments in the nodes is provided. Compared with other standard observability problems, two issues arise here. First, nonlinear unknown variables (products or quotients of elemental variables) appear and second, the mechanical and geometrical properties of the structure are "coupled" with the deflections and/or rotations at the nodes. To solve these problems, an algebraic method that adapts the standard observability problem to deal with structural system identification is proposed in this article. The results obtained show, for the very first time, how observability techniques can be efficiently used for the identification of structural systems. Some examples are given to illustrate the proposed methodology and to demonstrate its power. C 2013 Computer-Aided Civil and Infrastructure Engineering.
Nowadays, engineers are widely using accelerometers to record the vibration of structures for structural verification purposes. The main obstacle for using these data acquisition systems is their high cost, which limits its use to unique structures with a relatively high structural health monitoring budget. In this paper, a Cost Hyper-Efficient Arduino Product (CHEAP) has been developed to accurately measure structural accelerations. CHEAP is a system that is composed of five low-cost accelerometers that are connected to an Arduino microcontroller as their data acquisition system. Test results show that CHEAP not only has a significantly lower price (14 times cheaper in the worst-case scenario) compared with other systems used for comparison but also shows better accuracy on low frequencies for low acceleration amplitudes. Moreover, the final output results of Fast Fourier Transformation (FFT) assessments showed a better observable resolution for CHEAP than the studied control systems.
This work is focused on research in machine learning for coreference resolution. Coreference resolution is a natural language processing task that consists of determining the expressions in a discourse that refer to the same entity.\ud The main contributions of this article are (i) a new approach to coreference resolution\ud based on constraint satisfaction, using a hypergraph to represent the problem and solving it by relaxation labeling; and (ii) research towards improving coreference resolution performance using world knowledge extracted from Wikipedia.\ud The developed approach is able to use an entity-mention classification model with more\ud expressiveness than the pair-based ones, and overcome the weaknesses of previous approaches in the state of the art such as linking contradictions, classifications without context, and lack of information evaluating pairs. Furthermore, the approach allows the incorporation of new information by adding constraints, and research has been done in order to use world knowledge to improve performances.\ud RelaxCor, the implementation of the approach, achieved results at the state-of-the-art level, and participated in international competitions: SemEval-2010 and CoNLL-2011. RelaxCor achieved second place in CoNLL-2011.Postprint (published version
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