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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Los verbos sintagmáticos del espaňol. Descripción y propuesta de introducción en el aula de ELE. / Phrasal verbs in Spanish

MORENO DIEZ, Pilar January 2017 (has links)
The present work focuses on the analysis of the Spanish particle-verbs (Verbos Sintagmáticos) oriented towards the teaching of Spanish as a foreign language. The most problematic aspects about this category have been observed in terms of their use and behaviour in the semantic, syntactic and phonological levels of the language. For this, we have based on the studies on Italian VS and English phrasal verbs (more developed theoretically). The purpose of this study has been to offer a clear basis that will serve for future research ?both theoretical and practice?, to elaborate a proposal of definition based on real data extracted from different corpus of Spanish, and to suggest a basic sample of didactic material that facilitates the introduction in the classrooms of Spanish as a second language content related to this type of verbs.
2

Exploring the Compositionality of German Particle Verbs

Rawein, Carina January 2018 (has links)
In this thesis we explore the compositionality of particle verbs using distributional similarity and pre-trained word embeddings. We investigate the compositionality of 100 pairs of particle verbs with their base verbs. The ranking of our findings are compared to a ranking of human ratings on compositionality. In our distributional approach we use features such as context window size, content words, and only use particle verbs with one word sense. We then compare the distributional approach to a ranking done with pre-trained word embeddings. While none of the results are statistically significant, it is shown that word embeddings are not automatically superior to the more traditional distributional approach.

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