8 research outputs found

    Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

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    Computational models for semantic textual similarity

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    164 p.The overarching goal of this thesis is to advance on computational models of meaning and their evaluation. To achieve this goal we define two tasks and develop state-of-the-art systems that tackle both task: Semantic Textual Similarity (STS) and Typed Similarity.STS aims to measure the degree of semantic equivalence between two sentences by assigning graded similarity values that capture the intermediate shades of similarity. We have collected pairs of sentences to construct datasets for STS, a total of 15,436 pairs of sentences, being by far the largest collection of data for STS.We have designed, constructed and evaluated a new approach to combine knowledge-based and corpus-based methods using a cube. This new system for STS is on par with state-of-the-art approaches that make use of Machine Learning (ML) without using any of it, but ML can be used on this system, improving the results.Typed Similarity tries to identify the type of relation that holds between a pair of similar items in a digital library. Providing a reason why items are similar has applications in recommendation, personalization, and search. A range of types of similarity in this collection were identified and a set of 1,500 pairs of items from the collection were annotated using crowdsourcing.Finally, we present systems capable of resolving the Typed Similarity task. The best system resulted in a real-world application to recommend similar items to users in an online digital library

    Computational models for semantic textual similarity

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    164 p.The overarching goal of this thesis is to advance on computational models of meaning and their evaluation. To achieve this goal we define two tasks and develop state-of-the-art systems that tackle both task: Semantic Textual Similarity (STS) and Typed Similarity.STS aims to measure the degree of semantic equivalence between two sentences by assigning graded similarity values that capture the intermediate shades of similarity. We have collected pairs of sentences to construct datasets for STS, a total of 15,436 pairs of sentences, being by far the largest collection of data for STS.We have designed, constructed and evaluated a new approach to combine knowledge-based and corpus-based methods using a cube. This new system for STS is on par with state-of-the-art approaches that make use of Machine Learning (ML) without using any of it, but ML can be used on this system, improving the results.Typed Similarity tries to identify the type of relation that holds between a pair of similar items in a digital library. Providing a reason why items are similar has applications in recommendation, personalization, and search. A range of types of similarity in this collection were identified and a set of 1,500 pairs of items from the collection were annotated using crowdsourcing.Finally, we present systems capable of resolving the Typed Similarity task. The best system resulted in a real-world application to recommend similar items to users in an online digital library

    concepts - methods - visualization

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    While Darwin’s grand view of evolution has undergone many changes and shown up in many facets, there remains one outstanding common feature in its 150-year history: since the very beginning, branching trees have been the dominant scheme for representing evolutionary processes. Only recently, network models have gained ground reflecting contact-induced mixing or hybridization in evolutionary scenarios. In biology, research on prokaryote evolution indicates that lateral gene transfer is a major feature in the evolution of bacteria. In the field of linguistics, the mutual lexical and morphosyntactic borrowing between languages seems to be much more central for language evolution than the family tree model is likely to concede. In the humanities, networks are employed as an alternative to established phylogenetic models, to express the hybridization of cultural phenomena, concepts or the social structure of science. However, an interdisciplinary display of network analyses for evolutionary processes remains lacking. Therefore, this volume includes approaches studying the evolutionary dynamics of science, languages and genomes, all of which were based on methods incorporating network approaches

    A high speed transcription interface for annotating primary linguistic data

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    We present a new transcription mode for the annotation tool ELAN. This mode is designed to speed up the process of creating transcriptions of primary linguistic data (video and/or audio recordings of linguistic behaviour). We survey the basic transcription workflow of some commonly used tools (Transcriber, BlitzScribe, and ELAN) and describe how the new transcription interface improves on these existing implementations. We describe the design of the transcription interface and explore some further possibilities for improvement in the areas of segmentation and computational enrichment of annotations

    Proceedings of the Second Workshop on Annotation of Corpora for Research in the Humanities (ACRH-2). 29 November 2012, Lisbon, Portugal

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    Proceedings of the Second Workshop on Annotation of Corpora for Research in the Humanities (ACRH-2), held in Lisbon, Portugal on 29 November 2012
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