Sentence examples similar to semantic word vectors from inspiring English sources

Word vectors with such semantic relationships could be used to improve many existing NLP applications, such as machine translation, information retrieval, and question response systems.

The authors find that word vectors which are trained on massive amounts of data show subtle semantic relationships between words, such as a city and the country it belongs to – for example, Paris belongs to France and Berlin belongs to Germany.

Each of the approaches to calculating care episode similarity, with the models described in section 'Semantic models', are tested both with and without such metathesaurus-based re-weighting of word vectors.

The fact that RI-ICD and W2V-ICD did not benefit from such re-weighting of word vectors can be explained through how these models are trained, and that the "statistical correct" semantic meanings of words, especially in relation to the ICD-10 codes, is already induced through the training phase.

The word vectors of a word are combined by various forms of the vector word eventually.

Second, we induce word vector class models from word vectors as word representations.

The activation task of interest involved semantic word matching.

Reading aloud and picture naming > Semantic word and picture matching.

Semantic word and picture matching > Reading aloud and picture naming.

Category-specific brain activation distinguishing between semantic word types has imposed challenges on theories of semantic representations and processes.

Both the intact pair and the broken pair condition allowed for semantic word priming.