Modelling semantic relations with distributitional semantics and deep learning : question answering, entailment recognition and paraphrase detection

Nesta dissertação apresenta-se uma abordagem à tarefa de modelar relações semânticas entre dois textos com base em modelos de semântica distribucional e em aprendizagem profunda. O presente trabalho tira partido de várias disciplinas da ciência cognitiva, com especial relevo para a computação, a linguística e a inteligência artificial, e com fortes influência da neurociência e da psicologia cognitiva. Os modelos de semântica distribucional (também conhecidos como ”word embeddings”) são usados para representar o significado das palavras. As representações semânticas das palavras podem ainda ser combinadas para obter o significado de um excerto de um texto recorrendo ao uso da aprendizagem profunda, isto é, com o apoio das redes neurais de convolução. Esta abordagen é utilizada para replicar a experiência realizada por Bogdanova et al. (2015) na tarefa de deteção de perguntas que podem ser respondidas as mesmas respostas tal como estas foram respondidas em fóruns on-line. Os resultados do desempenho obtidos pelas experiências apresentadas nesta dissertação são equivalentes ou melhores que os resultados obtidos no trabalho de referência mencionado acima. Apresentao também um estudo sobre o impacto do pré-processamento apropriado do texto, tendo em conta os resultados que podem ser obtidos pelas abordagens adotadas no trabalho de referência supramencionado. Este estudo é levado a cabo removendo-se certas pistas que podem levar o sistema, indevidamente, a detetar perguntas equivalentes. Essa remoção das pistas leva a uma diminuição significativa no desempenho do sistema desenvolvido no trabalho de referência. Nesta dissertação é ainda apresentado um estudo sobre o impacto que os word embeddings treinados previamente têm na tarefa de detetar perguntas semanticamente equivalentes. Substituindo-se, aleatoriamente, word embeddings previamente treinados por outros melhora-se o desempenho do sistema. Além disso, o modelo foi utilizado na tarefa de reconhecimento de implicações para Português, onde mostrou uma taxa de acerto similar à da baseline. Este trabalho também reporta os resultados da aplicação da abordagem adotada numa competição para a deteção de paráfrases em Russo. A configuração final apresenta duas melhorias: usa character embeddings em vez de word embeddings e usa vários filtros de convolução. Esta configuração foi testado na execução padrão da Tarefa 2 da competição relevante, e mostrou resultados competitivos.This dissertation presents an approach to the task of modelling semantic relations between two texts, which is based on distributional semantic models and deep learning. The present work takes advantage of various disciplines of cognitive science, mainly computation, linguistics and artificial intelligence, with strong influences from neuroscience and cognitive psychology. Distributional semantic models (also known as word embeddings) are used to represent the meaning of words. Word semantic representations can be further combined towards obtaining the meaning of a larger chunk of a text using a deep learning approach, namely with the support of convolutional neural networks. These approaches are used to replicate the experiment carried out, by Bogdanova et al. (2015), for the task of detecting questions that can be answered by exactly the same answer in online user forums. Performance results obtained by my experiments are comparable or better than the ones reported in that referenced work. I present also a study on the impact of appropriate text preprocessing with respect to the results that can be obtained by the approaches adopted in that referenced work. Removing certain clues that can unduly help the system to detect equivalent questions leads to a significant decrease in system’s performance supported by that referenced work. I also present a study of the impact that pre-trained word embeddings have in the task of detecting the semantically equivalent questions. Replacing pre-trained word embeddings by randomly initialised ones improves the performance of the system. Additionally, the model was applied to the task of entailment recognition for Portuguese and showed an accuracy on a level with the baseline. This dissertation also reports on the results of an experimental study on the application of the adopted approach to the shared task of sentence paraphrase detection in Russian. The final set up contained two improvements: it uses several convolutional filters and it uses character embeddings instead of word embeddings. It was tested in Task 2 standard run of the relevant shared task and it showed competitive results

Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review

A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their performance. Statistical comparisons are also a crucial part which allows for reliable conclusions to be drawn. In the present paper we gather and examine the approaches taken from different perspectives to summarise the assumptions made by these statistical tests, the conclusions reached and the steps followed to perform them correctly. In this paper, we conduct a survey on the current trends of the proposals of statistical analyses for the comparison of algorithms of computational intelligence and include a description of the statistical background of these tests. We illustrate the use of the most common tests in the context of the Competition on single-objective real parameter optimisation of the IEEE Congress on Evolutionary Computation (CEC) 2017 and describe the main advantages and drawbacks of the use of each kind of test and put forward some recommendations concerning their use.Spanish Ministry of Economy, Industry and CompetitivenessSpanish Ministry of Scienc

Optimisation of a weightless neural network using particle swarms

Among numerous pattern recognition methods the neural network approach has been the subject of much research due to its ability to learn from a given collection of representative examples. This thesis is concerned with the design of weightless neural networks, which decompose a given pattern into several sets of n points, termed n-tuples. Considerable research has shown that by optimising the input connection mapping of such n-tuple networks classification performance can be improved significantly. In this thesis the application of a population-based stochastic optimisation technique, known as Particle Swarm Optimisation (PSO), to the optimisation of the connectivity pattern of such “n-tuple” classifiers is explored. The research was aimed at improving the discriminating power of the classifier in recognising handwritten characters by exploiting more efficient learning strategies. The proposed "learning" scheme searches for ‘good’ input connections of the n-tuples in the solution space and shrinks the search area step by step. It refines its search by attracting the particles to positions with good solutions in an iterative manner. Every iteration the performance or fitness of each input connection is evaluated, so a reward and punishment based fitness function was modelled for the task. The original PSO was refined by combining it with other bio-inspired approaches like Self-Organized Criticality and Nearest Neighbour Interactions. The hybrid algorithms were adapted for the n-tuple system and the performance was measured in selecting better connectivity patterns. The Genetic Algorithm (GA) has been shown to be accomplishing the same goals as the PSO, so the performances and convergence properties of the GA were compared against the PSO to optimise input connections. Experiments were conducted to evaluate the proposed methods by applying the trained classifiers to recognise handprinted digits from a widely used database. Results revealed the superiority of the particle swarm optimised training for the n-tuples over other algorithms including the GA. Low particle velocity in PSO was favourable for exploring more areas in the solution space and resulted in better recognition rates. Use of hybridisation was helpful and one of the versions of the hybrid PSO was found to be the best performing algorithm in finding the optimum set of input maps for the n-tuple network

KDD 1999 generation faults : a review and analysis

DARPA 1998 was one of the first Intrusion Detection datasets that was made publicly available. The KDD 1999 dataset was derived from DARPA 1998 to be used by researchers in developing machine learning (ML), classification and clustering algorithms with a security focus. DARPA 1998 has been criticised in literature due to raised concerns of problems in the dataset. Many researchers have accused KDD 1999 of having similar concerns but insufficient published evidence has been found. In this paper, we review the KDD 1999 generation process and present new proofs of existing inconsistencies in KDD 1999. We then present the process used to link some of the KDD 1999 (TELNET) records back to their origins in DARPA 1998 and discuss the interesting results and findings of this experiment.PostprintPeer reviewe

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience