An investigation into feature construction to assist word sense disambiguation

Identifying the correct sense of a word in context is crucial for many tasks in natural language processing (machine translation is an example). State-of-the art methods for Word Sense Disambiguation (WSD) build models using hand-crafted features that usually capturing shallow linguistic information. Complex background knowledge, such as semantic relationships, are typically either not used, or used in specialised manner, due to the limitations of the feature-based modelling techniques used. On the other hand, empirical results from the use of Inductive Logic Programming (ILP) systems have repeatedly shown that they can use diverse sources of background knowledge when constructing models. In this paper, we investigate whether this ability of ILP systems could be used to improve the predictive accuracy of models for WSD. Specifically, we examine the use of a general-purpose ILP system as a method to construct a set of features using semantic, syntactic and lexical information. This feature-set is then used by a common modelling technique in the field (a support vector machine) to construct a classifier for predicting the sense of a word. In our investigation we examine one-shot and incremental approaches to feature-set construction applied to monolingual and bilingual WSD tasks. The monolingual tasks use 32 verbs and 85 verbs and nouns (in English) from the SENSEVAL-3 and SemEval-2007 benchmarks; while the bilingual WSD task consists of 7 highly ambiguous verbs in translating from English to Portuguese. The results are encouraging: the ILP-assisted models show substantial improvements over those that simply use shallow features. In addition, incremental feature-set construction appears to identify smaller and better sets of features. Taken together, the results suggest that the use of ILP with diverse sources of background knowledge provide a way for making substantial progress in the field of WSD.

NUCLEAR SPIN 3/2 ELECTRIC QUADRUPOLE RELAXATION AS A QUANTUM COMPUTATION PROCESS

In this work we applied a quantum circuit treatment to describe the nuclear spin relaxation. From the Redfield theory, we obtain a description of the quadrupolar relaxation as a computational process in a spin 3/2 system, through a model in which the environment is comprised by five qubits and three different quantum noise channels. The interaction between the environment and the spin 3/2 nuclei is described by a quantum circuit fully compatible with the Redfield theory of relaxation. Theoretical predictions are compared to experimental data, a short review of quantum channels and relaxation in NMR qubits is also present.

Polymeric electro-mechanic devices applied to antibiotic-controlled release

Polymeric electroactive blends formed by electropolymerized aniline inside a non-conductive polyacrylamide porous matrix were already shown as suitable materials for the electrocontrolled release of model compounds like safranin. In this paper the intermolecular interactions between the two components of the blend are put in evidence by Raman spectroscopy measurements. Also, in situ optical microscopy was used to follow changes occurring in the polyaniline/polyacrylamide blend during pyrocathecol violet release tests. These two sets of experiments show the possibility of controlling electrochemically the release of both, safranin (a cation) and pyrocathecol violet (an anion) and allow to infer a release mechanism based on the electromechanical properties of the blends explaining the dependence of the release kinetics on the applied potential. Tetracycline release curves for different potentials and pHs are shown and the obtained profiles are in agreement with those expected for a device acting as an electrochemically driven pump due to the artificial muscle properties of the conducting phase of the blends. (c) 2007 Elsevier B.V. All rights reserved.