Design And Development Of A Named Entity Based Question Answering System For Malayalam Language

This is a Named Entity Based Question Answering System for Malayalam Language. Although a vast amount of information is available today in digital form, no effective information access mechanism exists to provide humans with convenient information access. Information Retrieval and Question Answering systems are the two mechanisms available now for information access. Information systems typically return a long list of documents in response to a user’s query which are to be skimmed by the user to determine whether they contain an answer. But a Question Answering System allows the user to state his/her information need as a natural language question and receives most appropriate answer in a word or a sentence or a paragraph. This system is based on Named Entity Tagging and Question Classification. Document tagging extracts useful information from the documents which will be used in finding the answer to the question. Question Classification extracts useful information from the question to determine the type of the question and the way in which the question is to be answered. Various Machine Learning methods are used to tag the documents. Rule-Based Approach is used for Question Classification. Malayalam belongs to the Dravidian family of languages and is one of the four major languages of this family. It is one of the 22 Scheduled Languages of India with official language status in the state of Kerala. It is spoken by 40 million people. Malayalam is a morphologically rich agglutinative language and relatively of free word order. Also Malayalam has a productive morphology that allows the creation of complex words which are often highly ambiguous. Document tagging tools such as Parts-of-Speech Tagger, Phrase Chunker, Named Entity Tagger, and Compound Word Splitter are developed as a part of this research work. No such tools were available for Malayalam language. Finite State Transducer, High Order Conditional Random Field, Artificial Immunity System Principles, and Support Vector Machines are the techniques used for the design of these document preprocessing tools. This research work describes how the Named Entity is used to represent the documents. Single sentence questions are used to test the system. Overall Precision and Recall obtained are 88.5% and 85.9% respectively. This work can be extended in several directions. The coverage of non-factoid questions can be increased and also it can be extended to include open domain applications. Reference Resolution and Word Sense Disambiguation techniques are suggested as the future enhancements

Studies on a syntax based approach for translation between structurally different languages and the development of a prototype for Malayalam to English translation

This thesis summarizes the results on the studies on a syntax based approach for translation between Malayalam, one of Dravidian languages and English and also on the development of the major modules in building a prototype machine translation system from Malayalam to English. The development of the system is a pioneering effort in Malayalam language unattempted by previous researchers. The computational models chosen for the system is first of its kind for Malayalam language. An in depth study has been carried out in the design of the computational models and data structures needed for different modules: morphological analyzer , a parser, a syntactic structure transfer module and target language sentence generator required for the prototype system. The generation of list of part of speech tags, chunk tags and the hierarchical dependencies among the chunks required for the translation process also has been done. In the development process, the major goals are: (a) accuracy of translation (b) speed and (c) space. Accuracy-wise, smart tools for handling transfer grammar and translation standards including equivalent words, expressions, phrases and styles in the target language are to be developed. The grammar should be optimized with a view to obtaining a single correct parse and hence a single translated output. Speed-wise, innovative use of corpus analysis, efficient parsing algorithm, design of efficient Data Structure and run-time frequency-based rearrangement of the grammar which substantially reduces the parsing and generation time are required. The space requirement also has to be minimised

Principle-Based Parsing

During the past few years, there has been much discussion of a shift from rule-based systems to principle-based systems for natural language processing. This paper outlines the major computational advantages of principle-based parsing, its differences from the usual rule-based approach, and surveys several existing principle-based parsing systems used for handling languages as diverse as Warlpiri, English, and Spanish, as well as language translation.

A Government-Binding Based Parser for Warlpiri, a Free-Word Order Language

Free-word order languages have long posed significant problems for standard parsing algorithms. This thesis presents an implemented parser, based on Government-Binding (GB) theory, for a particular free-word order language, Warlpiri, an aboriginal language of central Australia. The words in a sentence of a free-word order language may swap about relatively freely with little effect on meaning: the permutations of a sentence mean essentially the same thing. It is assumed that this similarity in meaning is directly reflected in the syntax. The parser presented here properly processes free word order because it assigns the same syntactic structure to the permutations of a single sentence. The parser also handles fixed word order, as well as other phenomena. On the view presented here, there is no such thing as a "configurational" or "non-configurational" language. Rather, there is a spectrum of languages that are more or less ordered. The operation of this parsing system is quite different in character from that of more traditional rule-based parsing systems, e.g., context-free parsers. In this system, parsing is carried out via the construction of two different structures, one encoding precedence information and one encoding hierarchical information. This bipartite representation is the key to handling both free- and fixed-order phenomena. This thesis first presents an overview of the portion of Warlpiri that can be parsed. Following this is a description of the linguistic theory on which the parser is based. The chapter after that describes the representations and algorithms of the parser. In conclusion, the parser is compared to related work. The appendix contains a substantial list of test cases ??th grammatical and ungrammatical ??at the parser has actually processed.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Integration of knowledge-based, qualitative and numeric tools for real time dynamic systems supervision

The proposal presented in this thesis is to provide designers of knowledge based supervisory systems of dynamic systems with a framework to facilitate their tasks avoiding interface problems among tools, data flow and management. The approach is thought to be useful to both control and process engineers in assisting their tasks. The use of AI technologies to diagnose and perform control loops and, of course, assist process supervisory tasks such as fault detection and diagnose, are in the scope of this work. Special effort has been put in integration of tools for assisting expert supervisory systems design. With this aim the experience of Computer Aided Control Systems Design (CACSD) frameworks have been analysed and used to design a Computer Aided Supervisory Systems (CASSD) framework. In this sense, some basic facilities are required to be available in this proposed framework: ·

Optimal piecewise locally linear modeling

Associative memory networks such as Radial Basis Functions, Neurofuzzy and Fuzzy Logic used for modelling nonlinear processes suffer from the curse of dimensionality (COD), in that as the input dimension increases the parameterization, computation cost, training data requirements, etc. increase exponentially. Here a new algorithm is introduced for the construction of a Delaunay input space partitioned optimal piecewise locally linear models to overcome the COD as well as generate locally linear models directly amenable to linear control and estimation algorithms. The training of the model is configured as a new mixture of experts network with a new fast decision rule derived using convex set theory. A very fast simulated reannealing (VFSR) algorithm is utilized to search a global optimal solution of the Delaunay input space partition. A benchmark non-linear time series is used to demonstrate the new approach.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

Do real balance effects invalidate the Taylor principle in closed and open economies?

This paper examines the determinacy implications of forecast-based monetary policy rules that set the interest rate in response to expected future inflation in a Neo-Wicksellian model that incorporates real balance effects. We show that the presence of such effects in closed economies restricts the ability of the Taylor principle to prevent indeterminacy of the rational expectations equilibrium. The problem is exacerbated in open economies, particularly if the policy rule reacts to consumer-price, rather than domestic-price, inflation. However, determinacy can be restored in both closed and open economies with the addition of monetary policy inertia.

Optimised dose titration for Duodopatreatment based on simulation experiments– implementation in a decision supportsystem

The aim of this work was to design a set of rules for levodopa infusion dose adjustment in Parkinson’s disease based on a simulation experiments. Using this simulator, optimal infusions dose in different conditions were calculated. There are seven conditions (-3 to +3)appearing in a rating scale for Parkinson’s disease patients. By finding mean of the differences between conditions and optimal dose, two sets of rules were designed. The set of rules was optimized by several testing. Usefulness for optimizing the titration procedure of new infusion patients based on rule-based reasoning was investigated. Results show that both of the number of the steps and the errors for finding optimal dose was shorten by new rules. At last, the dose predicted with new rules well on each single occasion of majority of patients in simulation experiments.

Rule versus interaction function : evaluating regional aggregations of commuting flows in Sweden

The aim of this paper is to evaluate the performance of two divergent methods for delineating commuting regions, also called labour market areas, in a situation that the base spatial units differ largely in size as a result of an irregular population distribution. Commuting patterns in Sweden have been analyzed with geographical information system technology by delineating commuting regions using two regionalization methods. One, a rule-based method, uses one-way commuting flows to delineate local labour market areas in a top-down procedure based on the selection of predefined employment centres. The other method, the interaction-based Intramax analysis, uses two-way flows in a bottom-up procedure based on numerical taxonomy principles. A comparison of these methods will expose a number of strengths and weaknesses. For both methods, the same data source has been used. The performance of both methods has been evaluated for the country as a whole using resident employed population, self-containment levels and job ratios for criteria. A more detailed evaluation has been done in the Goteborg metropolitan area by comparing regional patterns with the commuting fields of a number of urban centres in this area. It is concluded that both methods could benefit from the inclusion of additional control measures to identify improper allocations of municipalities.

The ex-Ante non-optimality of the Dempster-Schafer updating rule for ambiguous beliefs

The most widely used updating rule for non-additive probalities is the Dempster-Schafer rule. Schmeidles and Gilboa have developed a model of decision making under uncertainty based on non-additive probabilities, and in their paper “Updating Ambiguos Beliefs” they justify the Dempster-Schafer rule based on a maximum likelihood procedure. This note shows in the context of Schmeidler-Gilboa preferences under uncertainty, that the Dempster-Schafer rule is in general not ex-ante optimal. This contrasts with Brown’s result that Bayes’ rule is ex-ante optimal for standard Savage preferences with additive probabilities.

O rateio de ICMS por desempenho de municípios no Ceará e seu impacto em indicadores do sistema de avaliação da educação

De todo ICMS arrecadado pelos estados brasileiros, 25% é distribuído aos municípios. Os estados são responsáveis por definir as regras de distribuição de 25% destes 25% do ICMS que é transferido aos municípios, os outros 75% seguem o critério do Valor Adicionado Fiscal. Alguns estados alteraram suas leis para que a distribuição seja realizada em função do desempenho dos municípios em algumas áreas com o intuito de incentiva-lo a melhorarem sua performance em busca de uma maior fatia do ICMS. Seguindo esta lógica está o estado do Ceará onde 100% do ICMS distribuído segundo regras estaduais é calculado a partir do desempenho dos municípios em indicadores de resultado nas áreas da educação (72%), saúde (20%) e meio ambiente (8%). Este estudo tem como objetivo estimar o efeito que a mudança da Lei de distribuição do ICMS do Ceará teve em indicadores de resultado da área da educação: IDEB e Prova Brasil. Para tanto, foi utilizado o método da Dupla Diferença por meio da construção de grupos de controle e tratamento. Assim, comparou-se a evolução do desempenho, anteriormente e posteriormente à mudança, de municípios cearenses com municípios parecidos de estados vizinhos, porém, não submetidos a mesma regra de distribuição de ICMS. De forma complementar, foram feitas outras duas analises separando os municípios do estado do Ceará entre ganhadores e perdedores de recursos de ICMS com a mudança na Lei e entre os detentores dos melhores e piores desempenhos de PIB per capita. Os resultados apontam impactos positivos no desempenho dos municípios cearenses tanto no IDEB quanto na Prova Brasil. Mesmo os municípios que perderam recursos com mudança das regras de distribuição de ICMS, melhoraram sua performance na educação. Os municípios mais pobres do estado, que apresentam desempenho pior do que os municípios mais ricos, aumentaram o desempenho reduzindo a diferença de proficiência se comparada aos municípios mais ricos. Neste sentido, há indícios de que a mudança na Lei do ICMS implementada pelo estado do Ceará gerou impactos positivos no desempenho dos municípios no IDEB e na Prova Brasil.

In search of exchange rate predictability : a study about accuracy, consistency, and granger causality of forecasts generated by a Taylor Rule Model

Este estudo investiga o poder preditivo fora da amostra, um mês à frente, de um modelo baseado na regra de Taylor para previsão de taxas de câmbio. Revisamos trabalhos relevantes que concluem que modelos macroeconômicos podem explicar a taxa de câmbio de curto prazo. Também apresentamos estudos que são céticos em relação à capacidade de variáveis macroeconômicas preverem as variações cambiais. Para contribuir com o tema, este trabalho apresenta sua própria evidência através da implementação do modelo que demonstrou o melhor resultado preditivo descrito por Molodtsova e Papell (2009), o “symmetric Taylor rule model with heterogeneous coefficients, smoothing, and a constant”. Para isso, utilizamos uma amostra de 14 moedas em relação ao dólar norte-americano que permitiu a geração de previsões mensais fora da amostra de janeiro de 2000 até março de 2014. Assim como o critério adotado por Galimberti e Moura (2012), focamos em países que adotaram o regime de câmbio flutuante e metas de inflação, porém escolhemos moedas de países desenvolvidos e em desenvolvimento. Os resultados da nossa pesquisa corroboram o estudo de Rogoff e Stavrakeva (2008), ao constatar que a conclusão da previsibilidade da taxa de câmbio depende do teste estatístico adotado, sendo necessária a adoção de testes robustos e rigorosos para adequada avaliação do modelo. Após constatar não ser possível afirmar que o modelo implementado provém previsões mais precisas do que as de um passeio aleatório, avaliamos se, pelo menos, o modelo é capaz de gerar previsões “racionais”, ou “consistentes”. Para isso, usamos o arcabouço teórico e instrumental definido e implementado por Cheung e Chinn (1998) e concluímos que as previsões oriundas do modelo de regra de Taylor são “inconsistentes”. Finalmente, realizamos testes de causalidade de Granger com o intuito de verificar se os valores defasados dos retornos previstos pelo modelo estrutural explicam os valores contemporâneos observados. Apuramos que o modelo fundamental é incapaz de antecipar os retornos realizados.