26 resultados para Lexical semantic classes

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP)


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Due to both the widespread and multipurpose use of document images and the current availability of a high number of document images repositories, robust information retrieval mechanisms and systems have been increasingly demanded. This paper presents an approach to support the automatic generation of relationships among document images by exploiting Latent Semantic Indexing (LSI) and Optical Character Recognition (OCR). We developed the LinkDI (Linking of Document Images) service, which extracts and indexes document images content, computes its latent semantics, and defines relationships among images as hyperlinks. LinkDI was experimented with document images repositories, and its performance was evaluated by comparing the quality of the relationships created among textual documents as well as among their respective document images. Considering those same document images, we ran further experiments in order to compare the performance of LinkDI when it exploits or not the LSI technique. Experimental results showed that LSI can mitigate the effects of usual OCR misrecognition, which reinforces the feasibility of LinkDI relating OCR output with high degradation.

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The analysis of interviews with open-ended questions is a common practice amongst researchers in the field of Management. The difficulty therein is to convert the linguistic data into categories or quantitative values for subsequent statistical treatment. Proposals made to this end generally entail counting lexical occurrences which, since they are founded on previously established meanings, fail to include semantic associations made by interviews. This article aims to present an analysis tool comprising a set of techniques apt to generate linguistic units that can be statistically described, compared, modeled and inferred: the Quantitative Propositional Analysis (QPA), Its main difference from other such methods lies in the choice of a proposition - and not the lexical unit - as analysis unit. We present the application of this method through a study about the international expansion of retail firms.

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Given a continuous map f : K -> M from a 2-dimensional CW complex into a closed surface, the Nielsen root number N(f) and the minimal number of roots mu(f) of f satisfy N(f) <= mu(f). But, there is a number mu(C)(f) associated to each Nielsen root class of f, and an important problem is to know when mu(f) = mu(C)(f)N(f). In addition to investigate this problem, we determine a relationship between mu(f) and mu((f) over tilde), when (f) over tilde f is a lifting of f through a covering space, and we find a connection between this problems, with which we answer several questions related to them when the range of the maps is the projective plane.

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Since the beginning of Physical Education entrance in the brazilin public schools, the game has been frequently used as content, and in the course of time that practice seems to be intensified. In spite of many approaches of different purposes to justify its pedagogic usefulness, the game has been used as an indiscriminate way due to the fascination that it provides to the students. The present study searches for a description and analysis of children`s (10-12 years old) attitudes behaviors in games, on Physical Education classes, inside a public school. The study was accomplished with the researcher also attending as a teacher (action research). For the accomplishment of the study 55 children were filmed in four different games, of different kinds (exposed, transformed, and spontaneous). The classes` description and analysis were focused in the attitude axis and it was defined four topics for the discussion: Conflicts, Respect of rules, Expressiveness, and Competitiveness. The relationship between the individual with the game and its culture were pointed as the main characteristics in the configuration of the ludicrous activity atmosphere. It was also possible to observe specific situations of this relationship, once the games were limited to the social games (Piaget category), in a school atmosphere where children have students roles. Due to the obtained results, the study proposes a reflexive practice in which the students notice their own attitudes and try to adapt the game to their needs and not he other way around. In this perspective, the teacher has an important mediator roll, once he will be responsible to point out the students` difficulties and promote discussions in favor to provide teamwork.

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This paper contains a new proposal for the definition of the fundamental operation of query under the Adaptive Formalism, one capable of locating functional nuclei from descriptions of their semantics. To demonstrate the method`s applicability, an implementation of the query procedure constrained to a specific class of devices is shown, and its asymptotic computational complexity is discussed.

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The aim of this study was to analyze semantic and episodic memory deficits in children with mesial temporal sclerosis (MTS) and their correlation with clinical epilepsy variables. For this purpose, 19 consecutive children and adolescents with MTS (8 to 16 years old) were evaluated and their performance on five episodic memory tests (short- and long-term memory and learning) and four semantic memory tests was compared with that of 28 healthy volunteers. Patients performed worse on tests of immediate and delayed verbal episodic memory, visual episodic memory, verbal and visual learning, mental scanning for semantic clues, object naming, word definition, and repetition of sentences. Clinical variables such as early age at seizure onset, severity of epilepsy, and polytherapy impaired distinct types of memory. These data confirm that children with MTS have episodic memory deficits and add new information on semantic memory. The data also demonstrate that clinical variables contribute differently to episodic and semantic memory performance. (C) 2011 Elsevier Inc. All rights reserved.

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Episodic memory impairment is a well-recognized feature of mesial temporal lobe epilepsy. Semantic memory has received much less attention in this patient population. In this study, semantic memory aspects (word-picture matching, word definition, confrontation and responsive naming, and word list generation) in 19 patients with left and right temporal lobe epilepsy secondary to mesial temporal sclerosis (MTS) were compared with those of normal controls. Patients with LMTS showed impaired performance in word definition (compared to controls and RMTS) and in responsive naming (compared to controls). RMTS and LMTS patients performed worse than controls in word-picture matching. Both patients with left and right mesial temporal lobe epilepsy performed worse than controls in word list generation and in confrontation naming tests. Attentional-executive dysfunction may have contributed to these deficits. We conclude that patients with left and right NITS display impaired aspects of semantic knowledge. A better understanding of semantic processing difficulties in these patients will provide better insight into the difficulties with activities of daily living in this patient population. (C) 2007 Elsevier Inc. All rights reserved.

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Methods. We studied participants with acute and/or early HIV infection and TDR in 2 cohorts (San Francisco, California, and Sao Paulo, Brazil). We followed baseline mutations longitudinally and compared replacement rates between mutation classes with use of a parametric proportional hazards model. Results. Among 75 individuals with 195 TDR mutations, M184V/I became undetectable markedly faster than did nonnucleoside reverse-transcriptase inhibitor (NNRTI) mutations (hazard ratio, 77.5; 95% confidence interval [CI], 14.7-408.2; P < .0001), while protease inhibitor and NNRTI replacement rates were similar. Higher plasma HIV-1 RNA level predicted faster mutation replacement, but this was not statistically significant (hazard ratio, 1.71 log(10) copies/mL; 95% CI, .90-3.25 log(10) copies/mL; P = .11). We found substantial person-to-person variability in mutation replacement rates not accounted for by viral load or mutation class (P < .0001). Conclusions. The rapid replacement of M184V/I mutations is consistent with known fitness costs. The long-term persistence of NNRTI and protease inhibitor mutations suggests a risk for person-to-person propagation. Host and/or viral factors not accounted for by viral load or mutation class are likely influencing mutation replacement and warrant further study.

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This literature review retakes the discussion of the profiles and competences of the information area professional, specifically, in the health field. Therefore, the aim here is to outline the new fields of performance for the informationist and the profiles required in the health context.

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The viability of two different classes of Lambda(t)CDM cosmologies is tested by using the APM 08279+5255, an old quasar at redshift z = 3.91. In the first class of models, the cosmological term scales as Lambda(t) similar to R(-n). The particular case n = 0 describes the standard Lambda CDM model whereas n = 2 stands for the Chen and Wu model. For an estimated age of 2 Gyr, it is found that the power index has a lower limit n > 0.21, whereas for 3 Gyr the limit is n > 0.6. Since n can not be so large as similar to 0.81, the Lambda CDM and Chen and Wu models are also ruled out by this analysis. The second class of models is the one recently proposed by Wang and Meng which describes several Lambda(t)CDM cosmologies discussed in the literature. By assuming that the true age is 2 Gyr it is found that the epsilon parameter satisfies the lower bound epsilon > 0.11 while for 3 Gyr, a lower limit of epsilon > 0.52 is obtained. Such limits are slightly modified when the baryonic component is included.

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In contrast to the many studies on the venoms of scorpions, spiders, snakes and cone snails, tip to now there has been no report of the proteomic analysis of sea anemones venoms. In this work we report for the first time the peptide mass fingerprint and some novel peptides in the neurotoxic fraction (Fr III) of the sea anemone Bunodosoma cangicum venom. Fr III is neurotoxic to crabs and was purified by rp-HPLC in a C-18 column, yielding 41 fractions. By checking their molecular masses by ESI-Q-Tof and MALDI-Tof MS we found 81 components ranging from near 250 amu to approximately 6000 amu. Some of the peptidic molecules were partially sequenced through the automated Edman technique. Three of them are peptides with near 4500 amu belonging to the class of the BcIV, BDS-I, BDS-II, APETx1, APETx2 and Am-II toxins. Another three peptides represent a novel group of toxins (similar to 3200 amu). A further three molecules (similar to similar to 4900 amu) belong to the group of type 1 sodium channel neurotoxins. When assayed over the crab leg nerve compound action potentials, one of the BcIV- and APETx-like peptides exhibits an action similar to the type 1 sodium channel toxins in this preparation, suggesting the same target in this assay. On the other hand one of the novel peptides, with 3176 amu, displayed an action similar to potassium channel blockage in this experiment. In summary, the proteomic analysis and mass fingerprint of fractions from sea anemone venoms through MS are valuable tools, allowing us to rapidly predict the occurrence of different groups of toxins and facilitating the search and characterization of novel molecules without the need of full characterization of individual components by broader assays and bioassay-guided purifications. It also shows that sea anemones employ dozens of components for prey capture and defense. (C) 2008 Elsevier Inc. All rights reserved.

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This paper is about the use of natural language to communicate with computers. Most researches that have pursued this goal consider only requests expressed in English. A way to facilitate the use of several languages in natural language systems is by using an interlingua. An interlingua is an intermediary representation for natural language information that can be processed by machines. We propose to convert natural language requests into an interlingua [universal networking language (UNL)] and to execute these requests using software components. In order to achieve this goal, we propose OntoMap, an ontology-based architecture to perform the semantic mapping between UNL sentences and software components. OntoMap also performs component search and retrieval based on semantic information formalized in ontologies and rules.

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This paper presents an approach for assisting low-literacy readers in accessing Web online information. The oEducational FACILITAo tool is a Web content adaptation tool that provides innovative features and follows more intuitive interaction models regarding accessibility concerns. Especially, we propose an interaction model and a Web application that explore the natural language processing tasks of lexical elaboration and named entity labeling for improving Web accessibility. We report on the results obtained from a pilot study on usability analysis carried out with low-literacy users. The preliminary results show that oEducational FACILITAo improves the comprehension of text elements, although the assistance mechanisms might also confuse users when word sense ambiguity is introduced, by gathering, for a complex word, a list of synonyms with multiple meanings. This fact evokes a future solution in which the correct sense for a complex word in a sentence is identified, solving this pervasive characteristic of natural languages. The pilot study also identified that experienced computer users find the tool to be more useful than novice computer users do.

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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.

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There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.