Joint perceptual decision-making: a case study in explanatory pluralism.

Traditionally different approaches to the study of cognition have been viewed as competing explanatory frameworks. An alternative view, explanatory pluralism, regards different approaches to the study of cognition as complementary ways of studying the same phenomenon, at specific temporal and spatial scales, using appropriate methodological tools. Explanatory pluralism has been often described abstractly, but has rarely been applied to concrete cases. We present a case study of explanatory pluralism. We discuss three separate ways of studying the same phenomenon: a perceptual decision-making task (Bahrami et al., 2010), where pairs of subjects share information to jointly individuate an oddball stimulus among a set of distractors. Each approach analyzed the same corpus but targeted different units of analysis at different levels of description: decision-making at the behavioral level, confidence sharing at the linguistic level, and acoustic energy at the physical level. We discuss the utility of explanatory pluralism for describing this complex, multiscale phenomenon, show ways in which this case study sheds new light on the concept of pluralism, and highlight good practices to critically assess and complement approaches

Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering

In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is encoded to a vector representation from an word-level to a chunk-level to effectively capture the entire meaning. In particular, by adapting the hierarchical structure, our model shows very small performance degradations in longer text comprehension while other state-of-the-art recurrent neural network models suffer from it. Additionally, the latent topic clustering module extracts semantic information from target samples. This clustering module is useful for any text related tasks by allowing each data sample to find its nearest topic cluster, thus helping the neural network model analyze the entire data. We evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic domain question answering dataset, which is related to Samsung products. The proposed model shows state-of-the-art results for ranking question-answer pairs.Comment: 10 pages, Accepted as a conference paper at NAACL 201

Impacto da OCPC 07 no enxugamento das notas explicativas das companhias brasileiras

This article aims to assess the impact of the New Guideline of the Brazilian Accounting Pronouncements Committee (Comitê de Pronunciamentos Contábeis – OCPC 07) on improving formal features (size, readability, and specificity) of Brazilian companies’ Notes. OCPC 07 is one of the world’s first guidelines issued in response to the current demand for the downsizing of companies’ Notes, which according to standard setters and market agents have become too extensive, thus characterizing a disclosure overload. This is a unique study on the subject. The results suggest the effectiveness, although limited, of the new standard in promoting a departure from the habits of secrecy and formalism rooted in centuries of legalaccounting civil law tradition, and indicate that there is still room for complementary improvement initiatives in the form of incentives for firms and increased enforcement. Three complementary methodological approaches are used: (1) an analysis of both the evolution of note size after OCPC 07 and the factors explaining that size and its variation; (2) an examination of indicators of readability, conciseness, and specificity of the note on accounting policies; and (3) a size comparison of the Notes of Brazilian and British companies, a benchmark of the common law tradition. An average reduction of 10% in Note size was found two years after the introduction of Guideline (Orientação) 07 by the (OCPC 07). This downsizing was not generalized, but instead identified only among firms in the Novo Mercado and among those audited by two of the Big Four. Even in firms that reduced their notes by at least 20%, no significant improvements in readability levels could be perceived, nor in habits of copy-pasting the auditors’ templates, which could signal a focus on firms’ real practices in the note on accounting policies. Brazilian Notes remain far from the benchmark and are still 40% longer than British ones, despite an equivalent number of pages being expected.Este artigo teve por objetivo avaliar o impacto da Orientação do Comitê de Pronunciamentos Contábeis 07 (OCPC 07) em melhorias de forma (tamanho, readability e especificidade) das notas explicativas (NEs) brasileiras. A OCPC 07 constitui um dos primeiros normativos emitidos no mundo em resposta à demanda atual por enxugamento das NEs, que teriam, segundo reguladores e agentes do mercado global, tornado-se exageradamente extensas pelo acúmulo de informações irrelevantes, caracterizando um disclosure overload. Trata-se de estudo inédito sobre o tema. Os resultados sugerem a eficácia, embora limitada, da introdução de um novo normativo na promoção do afastamento de hábitos de sigilo e formalismo arraigados em séculos de tradição contábil-legal de civil law; tal aponta a oportunidade de ações complementares sob a forma de incentivos às empresas e incremento do enforcement. São utilizadas três abordagens complementares: (i) analisa a evolução do tamanho das NEs com a OCPC 07 e os fatores explicativos do tamanho e de sua variação; (ii) examina indicadores de readability, concisão e especificidade da nota de políticas contábeis; e (iii) compara, exemplificativamente, o tamanho das NEs de empresas brasileiras e britânicas, benchmark de transparência da tradição common law. Verificou-se a redução média de 10% no tamanho das NEs após dois anos de vigência da OCPC 07. Contudo, esse enxugamento não foi generalizado, mas identificado apenas em empresas do Novo Mercado e em auditadas por duas das Big Four. Mesmo considerando apenas empresas que reduziram suas NEs em pelo menos 20%, não foram percebidas melhorias significativas nos níveis de readability, nem no hábito de copy-paste dos modelos do auditor que sinalizassem um foco nas práticas reais da empresa na nota de políticas contábeis. As NEs brasileiras permanecem distantes do benchmark, estando 40% maiores que as britânicas, contra a expectativa de tamanho equivalente

Understanding analogical reasoning : viewpoints from psychology and related disciplines

Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning

Automated Detection of Bilingual Obfuscated Abusive Words on Social Media Forums: A Case of Swahili and English Texts

The usage of social media has exponentially grown in recent years leaving the users with no limitations on misusing the platforms through abusive contents as deemed fit to them. This exacerbates abusive words exposure to innocent users, especially in social media forums, including children. In an attempt to alleviate the problem of abusive words proliferation on social media, researchers have proposed different methods to help deal with variants of the abusive words; however, obfuscated abusive words detection still poses challenges. A method that utilizes a combination of rule based approach and character percentage matching techniques is proposed to improve the detection rate for obfuscated abusive words. The evaluation results achieved F1 score percentage ratio of 0.97 and accuracy percentage ratio of 0.96 which were above the significance ratio of 0.5. Hence, the proposed approach is highly effective for obfuscated abusive words detection and prevention. Keywords:     Rule based approach, Character percentage matching techniques, Obfuscated abuse, Abuse detection, Abusive words, Social medi

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Leveraging graph-based semantic annotation for the identification of cause-effect relations

This research is related to language article in Indonesia that discuss about causality relationship research used as public health surveillance information monitoring system. Utilization of this research is suitability of feature selection, phrase annotation, paragraph annotation, medical element annotation and graph-based semantic annotation. Evaluation of system performance is done by intrinsic approach using the Naive Bayes Multinomial method. The results obtained sequentially for recall, precision and f-measure are 0.924, 0.905, and 0.910