525 research outputs found

    METRICC: Harnessing Comparable Corpora for Multilingual Lexicon Development

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    International audienceResearch on comparable corpora has grown in recent years bringing about the possibility of developing multilingual lexicons through the exploitation of comparable corpora to create corpus-driven multilingual dictionaries. To date, this issue has not been widely addressed. This paper focuses on the use of the mechanism of collocational networks proposed by Williams (1998) for exploiting comparable corpora. The paper first provides a description of the METRICC project, which is aimed at the automatically creation of comparable corpora and describes one of the crawlers developed for comparable corpora building, and then discusses the power of collocational networks for multilingual corpus-driven dictionary development

    Semi-Supervised Multiple Disambiguation

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    Determining the true entity behind an ambiguousword is an NP-Hard problem known as Disambiguation. Previoussolutions often disambiguate a single ambiguous mention acrossmultiple documents. They assume each document contains onlya single ambiguous word and a rich set of unambiguous contextwords. However, nowadays we require fast disambiguation ofshort texts (like news feeds, reviews or Tweets) with few contextwords and multiple ambiguous words. In this research we focuson Multiple Disambiguation (MD) in contrast to Single Disambiguation(SD). Our solution is inspired by a recent algorithm developed for SD. The algorithm categorizes documents by first,transferring them into a graph and then, clustering the graphbased on its topological structure. We changed the graph-baseddocument-modeling of the algorithm, to account for MD. Also,we added a new parameter that controls the resolution of theclustering. Then, we used a supervised sampling approach formerging the clusters when appropriate. Our algorithm, comparedwith the original model, achieved 10% higher quality in termsof F1-Score using only 4% sampling from the dataset.QC 20160407</p

    Investigating Cue Selection and Placement in Tutorial Discourse

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    Our goal is to identify the features that predict cue selectlob and placement in order to devise strategies for automatic text generation

    HILDA: A Discourse Parser Using Support Vector Machine Classification

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    Discourse structures have a central role in several computational tasks, such as question-answering or dialogue generation. In particular, the framework of the Rhetorical Structure Theory (RST) offers a sound formalism for hierarchical text organization. In this article, we present HILDA, an implemented discourse parser based on RST and Support Vector Machine (SVM) classification. SVM classifiers are trained and applied to discourse segmentation and relation labeling. By combining labeling with a greedy bottom-up tree building approach, we are able to create accurate discourse trees in linear time complexity. Importantly, our parser can parse entire texts, whereas the publicly available parser SPADE (Soricut and Marcu 2003) is limited to sentence level analysis. HILDA outperforms other discourse parsers for tree structure construction and discourse relation labeling. For the discourse parsing task, our system reaches 78.3% of the performance level of human annotators. Compared to a state-of-the-art rule-based discourse parser, our system achieves a performance increase of 11.6%

    Automatic differentiation in machine learning: a survey

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    Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply "autodiff", is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs. AD is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, and engineering design optimization. Until very recently, the fields of machine learning and AD have largely been unaware of each other and, in some cases, have independently discovered each other's results. Despite its relevance, general-purpose AD has been missing from the machine learning toolbox, a situation slowly changing with its ongoing adoption under the names "dynamic computational graphs" and "differentiable programming". We survey the intersection of AD and machine learning, cover applications where AD has direct relevance, and address the main implementation techniques. By precisely defining the main differentiation techniques and their interrelationships, we aim to bring clarity to the usage of the terms "autodiff", "automatic differentiation", and "symbolic differentiation" as these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure
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