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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 Page: 1 2 3 4 5 Hits 1 – 20 of 83 1 Text+: Language- and text-based Research Data Infrastructure ... Hinrichs, Erhard; Leinen, Peter; Geyken, Alexander. - : Zenodo, 2022 BASE Show details 2 Text+: Language- and text-based Research Data Infrastructure ... Hinrichs, Erhard; Leinen, Peter; Geyken, Alexander. - : Zenodo, 2022 BASE Show details 3 Text+: Language- and text-based Research Data Infrastructure ... Hinrichs, Erhard; Leinen, Peter; Geyken, Alexander. - : Zenodo, 2022 BASE Show details 4 Komponenten-basierte Metadatenschemata und Facetten-basierte Suche ... Barkey Reinhild; Hinrichs Erhard; Hoppermann Christina. - : Zenodo, 2020 BASE Show details 5 Komponenten-basierte Metadatenschemata und Facetten-basierte Suche ... Barkey Reinhild; Hinrichs Erhard; Hoppermann Christina. - : Zenodo, 2020 BASE Show details 6 All that glitters is not gold : a gold standard of adjective-noun collocations for German Hinrichs, Erhard; Strakatova, Yana; Falk, Neele. - 2020 BASE Show details 7 No Word is an Island -- A Transformation Weighting Model for Semantic Composition ... Dima, Corina; de Kok, Daniël; Witte, Neele. - : arXiv, 2019 BASE Show details 8 Composition Models for the Representation and Semantic Interpretation of Nominal Compounds Dima, Gina-Corina. - : Universität Tübingen, 2019 Abstract: The central topic of this thesis are composition models of distributional semantics and their application for representing the semantics of German and English nominal compounds. Composition models are mathematical transformations that, given a compound like Apfelbaum ‘apple tree’, can be applied to the vector representations of Apfel ‘apple’ and Baum ‘tree’ to obtain a vector representation for the compound Apfelbaum ‘apple tree’. The new composed representation is deemed appropriate if it is similar to the representation of Apfelbaum that can be directly learned from large corpora using distributional methods. The thesis is structured into eight chapters. The first four chapters introduce compounds from a linguistic perspective (Chapter 1), present a review of annotation schemes for nominal compounds and introduce a new hybrid annotation scheme (Chapter 2), introduce neural networks and how to represent words via numerical features (Chapter 3) and detail the distributional representation of words (Chapter 4). Existing composition models of distributional semantics are reviewed and evaluated in Chapter 5. Chapter 5 also introduces three new composition models: addmask, wmask and multimatrix, that aim to improve over existing composition models either though a more efficient parametrization (*mask) or by promoting parameter reuse across different, but semantically similar words (multimatrix). The results show that composition models are able to construct meaningful composed representations for 81.8% of the German test compounds, and 78.03% of the English test compounds. In Chapter 6 composed representations are shown to be a useful indicator when investigating non-compositional (lexicalized) compounds. For example, when modeling a compound like Tigerauge, ‘tiger eye’, composition models will produce a composed representation that corresponds to the literal interpretation of the compound - the eye of a tiger. This vector, however, is dissimilar to the distributional vector learned directly from the corpus which captures the lexicalized meaning of semi-precious stone. In Chapter 7 composed representations prove to be the best features for classifying compounds in terms of their semantic relations in setups where simplex words and compounds have representations of the same length. Further analyses show also that some of the modifier information is discarded during the composition process and that extrinsic evaluations tasks such as the semantic classification task are necessary for assessing and improving the quality of the composed representations. Chapter 8 concludes by emphasizing the main contributions of the thesis and sketching directions for future contributions. Keyword: 004; 400; 420; 430; Composition Models; Computational Linguistics; Computerlinguistik; Distributional Semantics; Kompositum; Machine Learning; Maschinelles Lernen; Neural Networks; Nominal Compounds; Semantic Relations; Semantik; Word Representations URL: http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-870984 http://hdl.handle.net/10900/87098 https://doi.org/10.15496/publikation-28485 BASE Hide details 9 Semantic modelling of adjective-noun collocations using FrameNet Strakatova, Yana; Hinrichs, Erhard. - 2019 BASE Show details 10 No Word is an Island—A Transformation Weighting Model for Semantic Composition Dima, Corina; de Kok, Daniël; Witte, Neele... In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 437-451 (2019) (2019) BASE Show details 11 Digitale Forschungsinfrastrukturen für die Sprachwissenschaften Hinrichs, Erhard. - 2018 BASE Show details 12 Connecting Resources: Which Issues Have to be Solved to Integrate CMC Corpora from Heterogeneous Sources and for Different Languages? Beißwenger, Michael; Wigham, Ciara; Etienne, Carole... In: 5th Conference on CMC and Social Media Corpora for the Humanities (cmccorpora17) ; https://hal.archives-ouvertes.fr/hal-01918880 ; 5th Conference on CMC and Social Media Corpora for the Humanities (cmccorpora17), Oct 2017, Bolzano, Italy. pp.52-55 ; https://doi.org/10.5281/zenodo.1040713 (2017) BASE Show details 13 CoNLL 2017 Shared Task System Outputs Zeman, Daniel; Potthast, Martin; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 BASE Show details 14 Connecting Resources: Which Issues Have To Be Solved To Integrate Cmc Corpora From Heterogeneous Sources And For Different Languages? ... Beißwenger, Michael; Wigham, Ciara; Etienne, Carole. - : Zenodo, 2017 BASE Show details 15 Connecting Resources: Which Issues Have To Be Solved To Integrate Cmc Corpora From Heterogeneous Sources And For Different Languages? ... Beißwenger, Michael; Wigham, Ciara; Etienne, Carole. - : Zenodo, 2017 BASE Show details 16 Integrating optical character recognition and machine translation of historical documents Afli, Haithem; Way, Andy In: Afli, Haithem orcid:0000-0002-7449-4707 and Way, Andy orcid:0000-0001-5736-5930 (2016) Integrating optical character recognition and machine translation of historical documents. In: COLING, the 26th International Conference on Computational Linguistics, 13-16 Dec 2016, Osaka, Japan. (2016) BASE Show details 17 Language Technology Resources and Tools for Digital Humanities (LT4DH) : proceedings of the workshop : December 11-16, 2016, Osaka, Japan : LT4DH 2016 Trippel, Thorsten; Hinrichs, Erhard; Hinrichs, Marie. - : Stroudsburg : The COLING 2016 organizing committee, 2016 BASE Show details 18 Coordination Annotation for the Penn Treebank Kübler, Sandra; Maier, Wolfgang; Hinrichs, Erhard. - : Linguistic Data Consortium, 2015. : https://www.ldc.upenn.edu, 2015 BASE Show details 19 Coordination Annotation for the Penn Treebank ... Kübler, Sandra; Maier, Wolfgang; Hinrichs, Erhard. - : Linguistic Data Consortium, 2015 BASE Show details 20 Introduction to the Special Issue [Computational, cognitive, and linguistic approaches to the analysis of compounds and collocations] Henrich, Verena; Hinrichs, Erhard. - : Seoul : Institute for Cognitive Science, Seoul National University, 2015 BASE Show details Page: 1 2 3 4 5

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