Suchergebnisse

This paper describes our contribution to the Shared Task on Translation Inference across Dictionaries (TIAD-2019). In our approach, we construct a multi-lingual word embedding space by projecting new languages in the feature space of a language for which a pretrained embedding model exists. We use the similarity of the word embeddings to predict candidate translations. Even if our projection methodology is rather simplistic, our system outperforms the other participating systems with respect to the F1 measure for the language pairs which we predicted. ; The research described in this paper was primarily conducted in the project 'Linked Open Dictionaries' (LiODi, 2015-2020), funded by the German Ministry for Education and Research (BMBF) as an Independent Research Group on eHumanities. The conversion of FreeDict dictionaries into TIAD-TSV data that we used for estimating the prediction threshold was performed in the context of the Research and Innovation Action "Pret-a-LLOD. Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors" funded in the European Union's Horizon 2020 research and innovation programme under grant agreement No 825182.

Speaker attitude detection is important for processing opinionated text. Survey data as such provide a valuable source of information and research for different scientific disciplines. They are also of interest to practitioners such as policymakers, politicians, government bodies, educators, journalists, and all other stakeholders with occupations related to people and society. Survey data provide evidence about particular language phenomena and public attitudes to provide a broader picture about the clusters of social attitudes. In this regard, attitudinal discourse markers play a central role in the sense that they are pointers to the speaker's attitudes.