266 research outputs found

    Predicate Matrix: an interoperable lexical knowledge base for predicates

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    183 p.La Matriz de Predicados (Predicate Matrix en inglés) es un nuevo recurso léxico-semántico resultado de la integración de múltiples fuentes de conocimiento, entre las cuales se encuentran FrameNet, VerbNet, PropBank y WordNet. La Matriz de Predicados proporciona un léxico extenso y robusto que permite mejorar la interoperabilidad entre los recursos semánticos mencionados anteriormente. La creación de la Matriz de Predicados se basa en la integración de Semlink y nuevos mappings obtenidos utilizando métodos automáticos que enlazan el conocimiento semántico a nivel léxico y de roles. Asimismo, hemos ampliado la Predicate Matrix para cubrir los predicados nominales (inglés, español) y predicados en otros idiomas (castellano, catalán y vasco). Como resultado, la Matriz de predicados proporciona un léxico multilingüe que permite el análisis semántico interoperable en múltiples idiomas

    Comparing the production of a formula with the development of L2 competence

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    This pilot study investigates the production of a formula with the development of L2 competence over proficiency levels of a spoken learner corpus. The results show that the formula in beginner production data is likely being recalled holistically from learners’ phonological memory rather than generated online, identifiable by virtue of its fluent production in absence of any other surface structure evidence of the formula’s syntactic properties. As learners’ L2 competence increases, the formula becomes sensitive to modifications which show structural conformity at each proficiency level. The transparency between the formula’s modification and learners’ corresponding L2 surface structure realisations suggest that it is the independent development of L2 competence which integrates the formula into compositional language, and ultimately drives the SLA process forward

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

    Wiktionary Popularity from a Citation Analysis Point of View

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    Wiktionary is a collaborative web-based project to produce a free-content multilingual dictionary of terms in all natural languages and in a number of artificial languages. This study aims to provide an overview of the citation rate of Wiktionary. The primary source of data utilized in this study was the Scopus database. A REFERENCE search was conducted for indexed citations in the Scopus citation index, to find citations to Wiktionary in June 2023. Bibliometrix was used to design the keyword co-occurrence network of author-supplied keywords of documents citing Wiktionary. This study determines to what extent the Wiktionary is used and cited by papers indexed in Scopus. The total number of citations to Wiktionary from 2006 was 1,766 of which the highest number of citations is 161 in the year 2017 and the lowest number of citations is five in the year 2006. Wiktionary is highly cited by the subject areas of computer science, social sciences, and arts and humanities. The analysis of the language distribution of citations to Wiktionary indicates that the authors of citing papers used Wiktionary in different languages. However, the English language was the most dominant language of citing documents with 1,642 citations (i.e., 93%). Wiktionary was cited 1,766 times in Scopus by different languages (especially English, German, and French) in different countries (especially the U.S. with 335 citations, Germany with 295 citations, and France with 122 citations) mainly by the subject areas of computer science, social sciences, and arts and humanities. The significance of Wiktionary from a citation analysis point of view goes well beyond open access and enhanced opportunities for citation in linguistics, natural language processing systems, computational linguistics, semantics, and ontology

    Making Presentation Math Computable

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    This Open-Access-book addresses the issue of translating mathematical expressions from LaTeX to the syntax of Computer Algebra Systems (CAS). Over the past decades, especially in the domain of Sciences, Technology, Engineering, and Mathematics (STEM), LaTeX has become the de-facto standard to typeset mathematical formulae in publications. Since scientists are generally required to publish their work, LaTeX has become an integral part of today's publishing workflow. On the other hand, modern research increasingly relies on CAS to simplify, manipulate, compute, and visualize mathematics. However, existing LaTeX import functions in CAS are limited to simple arithmetic expressions and are, therefore, insufficient for most use cases. Consequently, the workflow of experimenting and publishing in the Sciences often includes time-consuming and error-prone manual conversions between presentational LaTeX and computational CAS formats. To address the lack of a reliable and comprehensive translation tool between LaTeX and CAS, this thesis makes the following three contributions. First, it provides an approach to semantically enhance LaTeX expressions with sufficient semantic information for translations into CAS syntaxes. Second, it demonstrates the first context-aware LaTeX to CAS translation framework LaCASt. Third, the thesis provides a novel approach to evaluate the performance for LaTeX to CAS translations on large-scaled datasets with an automatic verification of equations in digital mathematical libraries. This is an open access book

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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    Geographic information extraction from texts

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    A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

    On the semantic information in zero-shot action recognition

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    Orientador: Dr. David MenottiCoorientador: Dr. Hélio PedriniTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 14/04/2023Inclui referências: p. 117-132Área de concentração: Ciência da ComputaçãoResumo: Os avanços da última década em modelos de aprendizagem profunda aliados à alta disponibilidade de exemplos em plataformas como o YouTube foram responsáveis por notáveis progressos no problema de Reconhecimento de Ações Humanas (RAH) em vídeos. Esses avanços trouxeram o desafio da inclusão de novas classes aos modelos existentes, pois incluí-las é uma tarefa que demanda tempo e recursos computacionais. Além disso, novas classes de ações são frequentemente criadas pelo uso de novos objetos ou novas formas de interação entre humanos. Esse cenário é o que motiva o problema Zero-Shot Action Recognition (ZSAR), definido como classificar instâncias pertencentes a classes não disponíveis na fase de treinamento dos modelos. Métodos ZSAR objetivam aprender funções de projeção que relacionem as representações dos vídeos com as representações semânticas dos rótulos das classes conhecidas. Trata-se, portanto, de um problema de representação multi-modal. Nesta tese, investigamos o problema do semantic gap em ZSAR, ou seja, as propriedades dos espaços vetoriais das representações dos vídeos e dos rótulos não são coincidentes e, muitas vezes, as funções de projeção aprendidas são insuficientes para corrigir distorções. Nós defendemos que o semantic gap deriva do que chamamos semantic lack, ou falta de semântica, que ocorre em ambos os lados do problema (i.e., vídeos e rótulos) e não é suficientemente investigada na literatura. Apresentamos três abordagens ao problema investigando diferentes informações semânticas e formas de representação para vídeos e rótulos. Mostramos que uma forma eficiente de representar vídeos é transformando-os em sentenças descritivas utilizando métodos de video captioning. Essa abordagem permite descrever cenários, objetos e interações espaciais e temporais entre humanos. Nós mostramos que sua adoção gera modelos de alta eficácia comparados à literatura. Também propusemos incluir informações descritivas sobre os objetos presentes nas cenas a partir do uso de métodos treinados em reconhecimento de objetos. Mostramos que a representação dos rótulos de classes apresenta melhores resultados com o uso de sentenças extraídas de textos descritivos coletados da Internet. Ao usar apenas textos, nós nos valemos de modelos de redes neurais profundas pré-treinados na tarefa de paráfrase para codificar a informação e realizar a classificação ZSAR com reduzido semantic gap. Finalmente, mostramos como condicionar a representação dos quadros de um vídeo à sua correspondente descrição texual, produzindo um modelo capaz de representar em um espaço vetorial conjunto tanto vídeos quanto textos. As abordagens apresentadas nesta tese mostraram efetiva redução do semantic gap a partir das contribuições tanto em acréscimo de informação quanto em formas de codificação.Abstract: The advancements of the last decade in deep learning models and the high availability of examples on platforms such as YouTube were responsible for notable progress in the problem of Human Action Recognition (HAR) in videos. These advancements brought the challenge of adding new classes to existing models, since including them takes time and computational resources. In addition, new classes of actions are frequently created, either by using new objects or new forms of interaction between humans. This scenario motivates the Zero-Shot Action Recognition (ZSAR) problem, defined as classifying instances belonging to classes not available for the model training phase. ZSAR methods aim to learn projection functions associating video representations with semantic label representations of known classes. Therefore, it is a multi-modal representation problem. In this thesis, we investigate the semantic gap problem in ZSAR. The properties of vector spaces are not coincident, and, often, the projection functions learned are insufficient to correct distortions. We argue that the semantic gap derives from what we call semantic lack, which occurs on both sides of the problem (i.e., videos and labels) and is not sufficiently investigated in the literature. We present three approaches to the problem, investigating different information and representation strategies for videos and labels. We show an efficient way to represent videos by transforming them into descriptive sentences using video captioning methods. This approach enables us to produce high-performance models compared to the literature. We also proposed including descriptive information about objects present in the scenes using object recognition methods. We showed that the representation of class labels presents better results using sentences extracted from descriptive texts collected on the Internet. Using only texts, we employ deep neural network models pre-trained in the paraphrasing task to encode the information and perform the ZSAR classification with a reduced semantic gap. Finally, we show how conditioning the representation of video frames to their corresponding textual description produces a model capable of representing both videos and texts in a joint vector space. The approaches presented in this thesis showed an effective reduction of the semantic gap based on contributions in addition to information and representation ways
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