On building maps of web pages with a cellular automaton

We present in this paper a clustering algorithm which is based on a cellular automaton and which aims at displaying a map of web pages. We describe the main principles of methods that build such maps, and the main principles of cellular automata. We show how these principles can be applied to the problem of web pages clustering: the cells, which are organized in a 2D grid, can be either empty or may contain a page. The local transition function of cells favors the creation of groups of similar states (web pages) in neighbouring cells. We then present the visual results obtained with our method on standard data as well as on sets of documents. These documents are thus organized into a visual map which eases the browsing of these pages1st IFIP International Conference on Biologically Inspired Cooperative Computing - Web OrganizationRed de Universidades con Carreras en Informática (RedUNCI

On Building Maps of Web Pages with a Cellular Automaton

Abstract. We present in this paper a clustering algorithm which is based on a cellular automaton and which aims at displaying a map of web pages. We describe the main principles of methods that build such maps, and the main principles of cellular automata. We show how these principles can be applied to the problem of web pages clustering: the cells, which are organized in a 2D grid, can be either empty or may contain a page. The local transition function of cells favors the creation of groups of similar states (web pages) in neighbouring cells. We then present the visual results obtained with our method on standard data as well as on sets of documents. These documents are thus organized into a visual map which eases the browsing of these pages

On building maps of web pages with a cellular automaton

We present in this paper a clustering algorithm which is based on a cellular automaton and which aims at displaying a map of web pages. We describe the main principles of methods that build such maps, and the main principles of cellular automata. We show how these principles can be applied to the problem of web pages clustering: the cells, which are organized in a 2D grid, can be either empty or may contain a page. The local transition function of cells favors the creation of groups of similar states (web pages) in neighbouring cells. We then present the visual results obtained with our method on standard data as well as on sets of documents. These documents are thus organized into a visual map which eases the browsing of these pages1st IFIP International Conference on Biologically Inspired Cooperative Computing - Web OrganizationRed de Universidades con Carreras en Informática (RedUNCI

Path integration mediated systematic search: A Bayesian model

The systematic search behaviour is a backup system that increases the chances of desert ants finding their nest entrance after foraging when the path integrator has failed to guide them home accurately enough. Here we present a mathematical model of the systematic search that is based on extensive behavioural studies in North African desert ants . Cataglyphis fortis. First, a simple search heuristic utilising Bayesian inference and a probability density function is developed. This model, which optimises the short-term nest detection probability, is then compared to three simpler search heuristics and to recorded search patterns of . Cataglyphis ants. To compare the different searches a method to quantify search efficiency is established as well as an estimate of the error rate in the ants' path integrator. We demonstrate that the Bayesian search heuristic is able to automatically adapt to increasing levels of positional uncertainty to produce broader search patterns, just as desert ants do, and that it outperforms the three other search heuristics tested. The searches produced by it are also arguably the most similar in appearance to the ant's searches

Named entity extraction from Portuguese web text

No contexto de Processamento de Linguagem Natural, a tarefa de Reconhecimento de Entidades Mencionadas (REM) foca-se na extração e classificação de entidades mencionadas de texto livre, como notícias, que geralmente tem uma estrutura de frases particular. A deteção de entidades suporta tarefas mais complexas, como Extração de Relações ou Pesquisa Orientada a Entidades, como no motor de pesquisa do ANT.Há algumas ferramentas de REM focadas na língua portuguesa, tais como o Palavras ou o NERP-CRF, mas o seu F-measure ainda está abaixo do obtido usando outras ferramentas disponíveis, por exemplo com base num corpus inglês anotado, treinado com o Stanford CoreNLP ou com o OpenNLP.O ANT é um motor de busca orientado a entidades da Universidade do Porto (UP). Este sistema de pesquisa indexa as informação disponível no SIGARRA, o sistema de informação da UP. Atualmente usa seletores construídos manualmente para extrair entidades, baseados em XPath ou CSS, que são dependentes da estrutura da página. Além disso, não funcionam em texto livre, especialmente nas notícias do SIGARRA. Um método baseado em aprendizagem computacional permite a automatização da tarefa de extração, tornando-a escalável, independente da estrutura, diminuindo o esforço de trabalho exigido e o tempo consumido.Nesta dissertação, eu avaliei ferramentas de REM existentes para selecionar a melhor abordagem e configuração a utilizar em relação à língua portuguesa, particularmente no domínio das notícias do SIGARRA. A avaliação foi feita com base em dois conjuntos de dados, a coleção HAREM, e um subconjunto manualmente anotado de notícias do SIGARRA, que foram usados para calcular o desempenho das ferramentas usando precision, recall e F-measure. A expansão da base de conhecimento existente ajudará a indexar as páginas do SIGARRA proporcionando uma experiência de pesquisa orientada a entidades mais rica e com nova informação, bem como um melhor esquema de classificação baseado no contexto adicional disponibilizado ao motor de busca. A comunidade científica também beneficia deste trabalho, com múltiplos manuais detalhados resultantes da análise sistemática das ferramentas, em particular para a língua Portuguesa.Primeiramente, eu analisei a performance base de algumas ferramentas selecionadas (Stanford CoreNLP, OpenNLP, spaCy e NLTK) com a coleção HAREM, obtendo os melhores resultados com o Stanford CoreNLP, seguido do OpenNLP. De seguida, efetuei um estudo aos hiperparametros, de modo a selecionar a melhor configuração para cada ferramenta, conseguindo alcançar melhorias em cada ferramenta, principalmente no classificador de Entropia Máxima do NLTK, em que houve melhorias no F-measure de 1.11% para 35.24%. Finalmente, usando a melhor configuração, eu repeti o treino com o dataset das notícias do SIGARRA, tendo obtido F-measures de 86.64%, para o Stanford CoreNLP. Para além disso, dado que também foi o melhor na performance base, leva-me à conclusão que o Stanford CoreNLP é a melhor opção para este contexto.In the context of Natural Language Processing, the Named Entity Recognition (NER) task focuses on extracting and classifying named entities from free text, such as news, which usually has a particular phrasing structure. Entity detection supports more complex tasks, such as Relation Extraction or Entity-Oriented Search, for instance the ANT search engine.There are some NER tools focused on the Portuguese language, such as Palavras or NERP-CRF, but their F-measure is still below the F-measure obtained by other available tools, for instance based on an annotated English corpus, trained with Stanford CoreNLP or with OpenNLP.ANT is an entity-oriented search engine for the University of Porto (UP). This search system indexes the information available in SIGARRA, the information system of the UP. Currently it uses handcrafted selectors to extract entities, based on XPath or CSS, which are dependent on the structure of the page. Furthermore, it does not work on free text, specially on SIGARRA's news. Using a machine learning method allows for the automation of the extraction task, making it scalable, structure independent and lowering the required work effort and consumed time.In this dissertation, I evaluate existing NER tools in order to select the best approach and configuration for the Portuguese language, particularly in the domain of SIGARRA's news. The evaluation was done based on two datasets, the HAREM collection, and a manually annotated subset of SIGARRA's news, which are used to assess the tools' performance using precision, recall and F-measure. Expanding the existing knowledge base will help index SIGARRA pages by providing a richer entity-oriented search experience with new information, as well as a better ranking scheme based on the additional context made available to the search engine. The scientific community also benefits from this work, with several detailed manuals resulting of the systematic analysis of available tools, in particular for the Portuguese language.First, I carried an out-of-the-box performance analysis of some selected tools (Stanford CoreNLP, OpenNLP, spaCy and NLTK) with the HAREM dataset, obtaining the best results for Stanford CoreNLP, followed by OpenNLP. Then, I performed a hyperparamenter study in order to select the best configuration for each tool, having achieved better-than-default results in each tool, particularly for NLTK's Maximum Entropy classifier, increasing the F-measure from 1.11% to 35.24%. Finally, using the best configuration, I repeated the training process with the SIGARRA News Corpus, having achieved F-measures as high as 86.64%, for Stanford CoreNLP. Furthermore, given this was also the out-of-the box winner, it leads me to conclude that Stanford CoreNLP is the best option for this particular context