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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 18 of 18 1 Automatic extraction of property norm-like data from large text corpora Colin Kelly; A Barry Devereux; B Anna Korhonena In: http://www.cl.cam.ac.uk/%7Ealk23/cog-sci-2014.pdf (2013) BASE Show details 2 Populating a Multilingual Ontology of Proper Names from Open Sources Savary, Agata; Manicki, Leszek; Baron, Malgorzata In: ISSN: 2299-856X ; EISSN: 2299-8470 ; Journal of Language Modelling ; https://hal.archives-ouvertes.fr/hal-01023035 ; Journal of Language Modelling, Institute of Computer Science, Polish Academy of Sciences, Poland, 2013, 1-2, pp.189-225 (2013) BASE Show details 3 Overview of INEX Tweet Contextualization 2013 track Bellot, Patrice; Moriceau, Véronique; Mothe, Josiane... In: Working Notes for CLEF 2013 Conference ; Conference on Multilingual and Multimodal Information Access Evaluation - CLEF 2013 ; https://hal.archives-ouvertes.fr/hal-01140592 ; Conference on Multilingual and Multimodal Information Access Evaluation - CLEF 2013, Sep 2013, Valencia, Spain. pp. 1-4 (2013) BASE Show details 4 Thematically Reinforced Explicit Semantic Analysis Haralambous, Yannis; KLYUEV, Vitaly In: CICLING 2013 : 14th International Conference on Intelligent Text Processing and Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-00959957 ; CICLING 2013 : 14th International Conference on Intelligent Text Processing and Computational Linguistics, Mar 2013, Samos, Greece. pp.79-94 (2013) BASE Show details 5 Moving spaces: spelling alternation in English noun-noun compounds Kuperman, Viktor; Bertram, Raymond In: Language and cognitive processes. - Abingdon : Routledge, Taylor & Francis Group 28 (2013) 7, 939-966 BLLDB Show details 6 W2C – Web to Corpus – tool Majliš, Martin. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2013 BASE Show details 7 The Quality Of Massive Open Online Collaboration ... Ferschke, Oliver. - : Zenodo, 2013 BASE Show details 8 Rich Linguistic Structure from Large-Scale Web Data Yamangil, Elif. - 2013 BASE Show details 9 DutchSemCor ... Vossen, P.T.J.M PIEK. - : Data Archiving and Networked Services (DANS), 2013 BASE Show details 10 From printed to "wikified" encyclopedias: sociological aspects of an incipient cultural revolution Geser, Hans In: 73 (2013) BASE Show details 11 Manypedia: Comparing language points of view of Wikipedia communities Massa, Paolo; Scrinzi, Federico In: First Monday; Volume 18, Number 1 - 7 January 2013 ; 1396-0466 (2013) BASE Show details 12 On the mono- and cross-language detection of text re-use and plagiarism ; Detección de texto reutilizado y plagio monolingüe y translingüe Barrón Cedeño, Alberto. - : Sociedad Española para el Procesamiento del Lenguaje Natural, 2013 BASE Show details 13 Nutzertypen in wikipedia.de : eine Typologie unter dem Aspekt der kohärenzstiftenden hypertextuellen Verlinkungen zwischen den Artikeln Sieber, Claudia. - : Universitätsbibliothek Bielefeld, 2013 BASE Show details 14 Similitud semántica entre conceptos de Wikipedia Mejía Sánchez-Bermejo, Antonio. - 2013 BASE Show details 15 An open-source toolkit for mining Wikipedia Milne, David N.; Witten, Ian H.. - : Elsevier, 2013 BASE Show details 16 Ein digitales Wörterbuch der 200 häufigsten Wörter der Wikipedia in ägyptischer Umgangssprache Siam, Omar. - 2013 BASE Show details 17 Contribució sociolingüística a la Viquipèdia: entrades de conceptes i d'autors Martínez Oronich, Olalla. - : Universitat Oberta de Catalunya, 2013 BASE Show details 18 Multilingual Word Sense Disambiguation Using Wikipedia Dandala, Bharath. - : University of North Texas, 2013 Abstract: Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning. Word sense disambiguation is the task of automatically assigning the most appropriate meaning to a polysemous word within a given context. Generally the problem of resolving ambiguity in literature has revolved around the famous quote “you shall know the meaning of the word by the company it keeps.” In this thesis, we investigate the role of context for resolving ambiguity through three different approaches. Instead of using a predefined monolingual sense inventory such as WordNet, we use a language-independent framework where the word senses and sense-tagged data are derived automatically from Wikipedia. Using Wikipedia as a source of sense-annotations provides the much needed solution for knowledge acquisition bottleneck. In order to evaluate the viability of Wikipedia based sense-annotations, we cast the task of disambiguating polysemous nouns as a monolingual classification task and experimented on lexical samples from four different languages (viz. English, German, Italian and Spanish). The experiments confirm that the Wikipedia based sense annotations are reliable and can be used to construct accurate monolingual sense classifiers. It is a long belief that exploiting multiple languages helps in building accurate word sense disambiguation systems. Subsequently, we developed two approaches that recast the task of disambiguating polysemous nouns as a multilingual classification task. The first approach for multilingual word sense disambiguation attempts to effectively use a machine translation system to leverage two relevant multilingual aspects of the semantics of text. First, the various senses of a target word may be translated into different words, which constitute unique, yet highly salient signal that effectively expand the target word’s feature space. Second, the translated context words themselves embed co-occurrence information that a translation engine gathers from very large parallel corpora. The second approach for multlingual word sense disambiguation attempts to reduce the reliance on the machine translation system during training by using the multilingual knowledge available in Wikipedia through its interlingual links. Finally, the experiments on a lexical sample from four different languages confirm that the multilingual systems perform better than the monolingual system and significantly improve the disambiguation accuracy. Keyword: multilingual; supervised learning; Wikipedia; word sense disambiguation URL: https://digital.library.unt.edu/ark:/67531/metadc500036/ BASE Hide details

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