A filter algorithm: comparison with NLP solvers

The purpose of this work is to present an algorithm to solve nonlinear constrained optimization problems, using the filter method with the inexact restoration (IR) approach. In the IR approach two independent phases are performed in each iteration—the feasibility and the optimality phases. The first one directs the iterative process into the feasible region, i.e. finds one point with less constraints violation. The optimality phase starts from this point and its goal is to optimize the objective function into the satisfied constraints space. To evaluate the solution approximations in each iteration a scheme based on the filter method is used in both phases of the algorithm. This method replaces the merit functions that are based on penalty schemes, avoiding the related difficulties such as the penalty parameter estimation and the non-differentiability of some of them. The filter method is implemented in the context of the line search globalization technique. A set of more than two hundred AMPL test problems is solved. The algorithm developed is compared with LOQO and NPSOL software packages.

NLP:n sovittamista johtamisen alueelle

NLP = Neuro-Linguistic Programming

In your hands : NLP in ELT.

La programación neuro-lingüística es una colección de técnicas, modelos y estrategias que ayudan a la comunicación y al crecimiento personal, así como al aprendizaje. Aquí, se trata de la aplicación de estas técnicas dirigidas a la enseñanza y aprendizaje de idiomas, en este caso, del inglés como lengua extranjera. Y esta destinado,a profesores que quieran mejorar su labor docente y el rendimiento de sus alumnos.

Learning generators : NLP and learning styles in english text books. 'Generadores de aprendizaje : PNL y estilos de aprendizaje en libros de texto de ingl??s'

Resumen basado en el de la publicaci??n

Reusability of dictionaries in the compilation of NLP lexicons

This paper discusses particular linguistic challenges in the task of reusing published dictionaries, conceived as structured sources of lexical information, in the compilation process of a machine-tractable thesaurus-like lexical database for Brazilian Portuguese. After delimiting the scope of the polysemous term thesaurus, the paper focuses on the improvement of the resulting object by a small team, in a form compatible with and inspired by WordNet guidelines, comments on the dictionary entries, addresses selected problems found in the process of extracting the relevant lexical information form the selected dictionaries, and provides some strategies to overcome them.

Information Processing in a Cognitive Model of NLP

A model of the cognitive process of natural language processing has been developed using the formalism of generalized nets. Following this stage-simulating model, the treatment of information inevitably includes phases, which require joint operations in two knowledge spaces – language and semantics. In order to examine and formalize the relations between the language and the semantic levels of treatment, the language is presented as an information system, conceived on the bases of human cognitive resources, semantic primitives, semantic operators and language rules and data. This approach is applied for modeling a specific grammatical rule – the secondary predication in Russian. Grammatical rules of the language space are expressed as operators in the semantic space. Examples from the linguistics domain are treated and several conclusions for the semantics of the modeled rule are made. The results of applying the information system approach to the language turn up to be consistent with the stages of treatment modeled with the generalized net.

studio e sperimentazione di tecniche di valutazione della significatività statistica in modelli nlp

Negli ultimi anni, il natural language processing ha subito una forte evoluzione, principalmente dettata dai paralleli avanzamenti nell’area del deep-learning. Con dimensioni architetturali in crescita esponenziale e corpora di addestramento sempre più comprensivi, i modelli neurali sono attualmente in grado di generare testo in maniera indistinguibile da quello umano. Tuttavia, a predizioni accurate su task complessi, si contrappongono metriche frequentemente arretrate, non capaci di cogliere le sfumature semantiche o le dimensioni di valutazione richieste. Tale divario motiva ancora oggi l’adozione di una valutazione umana come metodologia standard, ma la natura pervasiva del testo sul Web rende evidente il bisogno di sistemi automatici, scalabili, ed efficienti sia sul piano dei tempi che dei costi. In questa tesi si propone un’analisi delle principali metriche allo stato dell’arte per la valutazione di modelli pre-addestrati, partendo da quelle più popolari come Rouge fino ad arrivare a quelle che a loro volta sfruttano modelli per valutare il testo. Inoltre, si introduce una nuova libreria – denominata Blanche– finalizzata a raccogliere in un unico ambiente le implementazioni dei principali contributi oggi disponibili, agevolando il loro utilizzo da parte di sviluppatori e ricercatori. Infine, si applica Blanche per una valutazione ad ampio spettro dei risultati generativi ottenuti all’interno di un reale caso di studio, incentrato sulla verbalizzazione di eventi biomedici espressi nella letteratura scientifica. Una particolare attenzione è rivolta alla gestione dell’astrattività, un aspetto sempre più cruciale e sfidante sul piano valutativo.

Biologically Plausible Connectionist Prediction of Natural Language Thematic Relations

In Natural Language Processing (NLP) symbolic systems, several linguistic phenomena, for instance, the thematic role relationships between sentence constituents, such as AGENT, PATIENT, and LOCATION, can be accounted for by the employment of a rule-based grammar. Another approach to NLP concerns the use of the connectionist model, which has the benefits of learning, generalization and fault tolerance, among others. A third option merges the two previous approaches into a hybrid one: a symbolic thematic theory is used to supply the connectionist network with initial knowledge. Inspired on neuroscience, it is proposed a symbolic-connectionist hybrid system called BIO theta PRED (BIOlogically plausible thematic (theta) symbolic-connectionist PREDictor), designed to reveal the thematic grid assigned to a sentence. Its connectionist architecture comprises, as input, a featural representation of the words (based on the verb/noun WordNet classification and on the classical semantic microfeature representation), and, as output, the thematic grid assigned to the sentence. BIO theta PRED is designed to ""predict"" thematic (semantic) roles assigned to words in a sentence context, employing biologically inspired training algorithm and architecture, and adopting a psycholinguistic view of thematic theory.

NONLINEAR PERIODIZATION MAXIMIZES STRENGTH GAINS IN SPLIT RESISTANCE TRAINING ROUTINES

Monteiro, AG, Aoki, MS, Evangelista, AL, Alveno, DA, Monteiro, GA, Picarro, IDC, and Ugrinowitsch, C. Nonlinear periodization maximizes strength gains in split resistance training routines. J Strength Cond Res 23(4): 1321-1326, 2009-The purpose of our study was to compare strength gains after 12 weeks of nonperiodized (NP), linear periodized (LP), and nonlinear periodized (NLP) resistance training models using split training routines. Twenty-seven strength-trained men were recruited and randomly assigned to one of 3 balanced groups: NP, LP, and NLP. Strength gains in the leg press and in the bench press exercises were assessed. There were no differences between the training groups in the exercise pre-tests (p > 0.05) (i.e., bench press and leg press). The NLP group was the only group to significantly increase maximum strength in the bench press throughout the 12-week training period. In this group, upper-body strength increased significantly from pre-training to 4 weeks (p < 0.0001), from 4 to 8 weeks (p = 0.004), and from 8 weeks to the post-training (p < 0.02). The NLP group also exhibited an increase in leg press 1 repetition maximum at each time point (pre-training to 4 weeks, 4-8 week, and 8 weeks to post-training, p < 0.0001). The LP group demonstrated strength increases only after the eight training week (p = 0.02). There were no further strength increases from the 8-week to the post-training test. The NP group showed no strength increments after the 12-week training period. No differences were observed in the anthropometric profiles among the training models. In summary, our data suggest that NLP was more effective in increasing both upper- and lower-body strength for trained subjects using split routines.

Aprendizagem Cooperativa : um contributo para a diferenciação pedagógica inclusiva

A dissertação incidiu na diferenciação pedagógica inclusiva, especificamente, num grupo/turma com diferentes anos de escolaridade, com aprendizagens diferentes, e alunos com Necessidades Educativas Especiais (NEE) de índole comportamental e cognitivo, tendo como objectivo constituir alternativa ao problema. As professoras titulares de turma e de educação especial, em cooperação, planificaram e implementaram este estudo, cujo ponto de partida consistia em promover o desenvolvimento pessoal e social, tendo por base um livro pertencente ao Plano Nacional de Leitura (PNL). Foram desenvolvidas estruturas cooperativas de forma a melhorar comportamentos e aprendizagens em todos os alunos e em especial dos alunos com NEE. Neste processo de observação e reflexão, a experiência foi conceptualizada e analisada com o objectivo de contribuir para a mudança. A análise de dados do trabalho individual e de grupo dos alunos, onde se inferiu uma maior ajuda, autonomia, responsabilidade, envolvimento e interesse, pelas actividades, permitiu-nos verificar que os objectivos foram alcançados na sua maioria O estudo permitiu evidenciar a diminuição de conflitos comportamentais, contribuindo para um clima de entreajuda englobando todos os alunos, onde a partilha de conhecimentos coadjuvou para melhorar as aprendizagens de todos.

Solving MPCC problem with the hyperbolic penalty function

The main goal of this work is to solve mathematical program with complementarity constraints (MPCC) using nonlinear programming techniques (NLP). An hyperbolic penalty function is used to solve MPCC problems by including the complementarity constraints in the penalty term. This penalty function [1] is twice continuously differentiable and combines features of both exterior and interior penalty methods. A set of AMPL problems from MacMPEC [2] are tested and a comparative study is performed.

Solving a signalized traffic intersection problem with an hyperbolic penalty function

Mathematical Program with Complementarity Constraints (MPCC) finds many applications in fields such as engineering design, economic equilibrium and mathematical programming theory itself. A queueing system model resulting from a single signalized intersection regulated by pre-timed control in traffic network is considered. The model is formulated as an MPCC problem. A MATLAB implementation based on an hyperbolic penalty function is used to solve this practical problem, computing the total average waiting time of the vehicles in all queues and the green split allocation. The problem was codified in AMPL.

Inexact restoration approaches to solve mathematical program with complementarity constraints

Mathematical Program with Complementarity Constraints (MPCC) finds applica- tion in many fields. As the complementarity constraints fail the standard Linear In- dependence Constraint Qualification (LICQ) or the Mangasarian-Fromovitz constraint qualification (MFCQ), at any feasible point, the nonlinear programming theory may not be directly applied to MPCC. However, the MPCC can be reformulated as NLP problem and solved by nonlinear programming techniques. One of them, the Inexact Restoration (IR) approach, performs two independent phases in each iteration - the feasibility and the optimality phases. This work presents two versions of an IR algorithm to solve MPCC. In the feasibility phase two strategies were implemented, depending on the constraints features. One gives more importance to the complementarity constraints, while the other considers the priority of equality and inequality constraints neglecting the complementarity ones. The optimality phase uses the same approach for both algorithm versions. The algorithms were implemented in MATLAB and the test problems are from MACMPEC collection.