Predictive models of autism spectrum disorder based on brain regional cortical thickness.

Feature selection and classification model construction on type 2 diabetic patients' data

Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 80%. However, due to the problems of spam drift and information fabrication, these machine-learning based methods cannot efficiently detect spam activities in real-life scenarios. Moreover, the blacklisting method cannot catch up with the variations of spamming activities as manually inspecting suspicious URLs is extremely time-consuming. In this paper, we proposed a novel technique based on deep learning techniques to address the above challenges. The syntax of each tweet will be learned through WordVector Training Mode. We then constructed a binary classifier based on the preceding representation dataset. In experiments, we collected and implemented a 10-day real Tweet datasets in order to evaluate our proposed method. We first studied the performance of different classifiers, and then compared our method to other existing text-based methods. We found that our method largely outperformed existing methods. We further compared our method to non-text-based detection techniques. According to the experiment results, our proposed method was more accurate.

Twitter spam detection based on deep learning

Texts are the media content primarily available on Web sites and applications. However, this heavy use of texts creates an accessibility barrier to those who cannot read fluently in their mother tongue due to both text length and linguistic complexity. To offer an accessible alternative to these readers, shorter and simplified versions of text content should be provided. Taking that into consideration, this paper introduces Facilita, an assistive technology to help lower-literacy users to understand the text content of Web applications. Facilita generates an accessible content from Web pages automatically, using summarization and simplification techniques. It is also important to consider interface design requirements, since Facilita's target audience (the functionally illiterate) is often classified as computer illiterate as well. Thus, interaction and user interface design were developed considering the limitations and skills of the functionally illiterate.

/pdf/facilita-reading-assistance-for-low-literacy-readers-1zbr1car59.pdf

Facilita: reading assistance for low-literacy readers

In this work, we present the construction process of a large Web corpus for Brazilian Portuguese, aiming to achieve a size comparable to the state of the art in other languages. We also discuss our updated sentence-level approach for the strict removal of duplicated content. Following the pipeline methodology, more than 60 million pages were crawled and filtered, with 3.5 million being selected. The obtained multi-domain corpus, named brWaC, is composed by 2.7 billion tokens, and has been annotated with tagging and parsing information. The incidence of non-unique long sentences, an indication of replicated content, which reaches 9% in other Web corpora, was reduced to only 0.5%. Domain diversity was also maximized, with 120,000 different websites contributing content. We are making our new resource freely available for the research community, both for querying and downloading, in the expectation of aiding in new advances for the processing of Brazilian Portuguese.

The brWaC Corpus: A New Open Resource for Brazilian Portuguese

Combining Classifiers to Improve Part of Speech Tagging: A Case Study for Brazilian Portuguese

In this paper we present experiments on summarization and text simplification for poor readers, more specifically, functional illiteracy readers. We test several summarizers and use summaries as the basis of simplification strategies. We show that each simplification approach has different effects on readers of varied levels of literacy, but that all of them do improve text understanding at some level.

/pdf/automatic-summarization-for-text-simplification-evaluating-i0okhuc9yi.pdf

Automatic summarization for text simplification: evaluating text understanding by poor readers

Measuring the Web in Portuguese

In order to improve Web Information Retrieval, we have, in a previous work (Aires et al., 2004), investigated the use of stylistic features of Web texts in Portuguese to classify web pages according to users’ needs, using in most of the experiments the classification algorithm J48 (the Weka implementation of C4.5). From that study, we concluded that it was possible to identify some of the categories reliably, but we should investigate whether it was possible to get even better classification schemes using other algorithms. Language is a different domain, and the fact that C4.5 has been used successfully in other applications (even others dealing with written language) does not imply that it is also the best solution for our problem. In this paper, we document the replication of the experiments presented in Aires et al (2004), using all relevant Weka algorithms, also providing more information on the linguistic features used and on the issues concerning algorithm choice.

/pdf/which-classification-algorithm-works-best-with-stylistic-4mziiuk98k.pdf

Which classification algorithm works best with stylistic features of Portuguese in order to classify web texts according to users. needs

Resumo. Neste artigo apresentamos uma panorâmica da actividade da Linguateca na criacao e disponibilizacao de recursos e ferramentas para a lingua portuguesa. Comecamos por uma descricao dos objectivos e pressupostos da Linguateca e uma breve historia da sua intervencao, e finalizamos com algumas consideracoes sobre a melhor forma de prosseguir na organizacao da area.

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Rachel Aires

Papers

Combining Classifiers to Improve Part of Speech Tagging: A Case Study for Brazilian Portuguese

Automatic summarization for text simplification: evaluating text understanding by poor readers

Measuring the Web in Portuguese

Which classification algorithm works best with stylistic features of Portuguese in order to classify web texts according to users. needs

Linguateca: um Centro de Recursos Distribuído para o Processamento Computacional da Língua Portuguesa