Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an inte
ractive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence can be found at https://textessence.github.io.
This piece of research endeavours to highlight the inevitability of the
micro-textonymic transformations throughout the process of translation. The claim that translation necessitates transformation has been ascertained through rendering a few non/
conventional micro-textonymic English collocational patterns into Arabic. However, though some translation theorists comprehend transformations as a remark of inescapable weakness, others maintain its prominence in successfully communicating the TL recipients, to the extent that there is no transation without transformation. Translator's skilfulness and expertise would closely monitor and manage such micro-textonymic transformations, being the decoder of the ST and re-encoder of the TT. Faithfulness in translation has been defined not in relation to extremely possible literalism and adherence to the ST, rather, it stands as a remark of how far do such micro-textonymic transformations help translators communicate the rhetoric of the ST, and guarantee acceptance and readability in the TL language and culture.
