Sentence examples for trained word from inspiring English sources

A repeated-measures ANOVA on word reading accuracy showed a significant main effect of time [ F 1,8) = 6.9, P < 0.05; average trained word accuracy before training = 91.0%; at t3 = 93.3%], but the critical interaction of time with word list was not significant; both trained and untrained word reading accuracy improved after training.

Moreover, this training extended to word pairs containing novel threat-relevant words and was not confined to specifically trained word pairs.

The effect of sub-lexical orthographic frequency on untrained word reading speed was tested by calculating, for each untrained word, the number of times its constituent bigrams and trigrams appeared in the trained word list.

Fifty trials contained words from the trained word list, 50 contained words from the untrained word list and 10 were familiar names (e.g. john, tim, sarah, etc) used as 'catch trials' in an incidental task to maintain the participants' attention throughout the scan.

We trained word-based n-gram language models on the 620 million words FidaPLUS corpus of the Slovene language [22], which consists mainly of articles from newspapers and journals.

It's demeaning to do to an intern, or even a specially trained, word-counting horse-prodigy.

At t3, trained words were read faster than untrained words [ t(7) = 4.1, P < 0.005].

A gain of 1 would indicate that the connection strength was equal for trained and untrained words, whereas a gain significantly greater/lower than 1 would indicate stronger/weaker connectivity for trained words relative to untrained words, respectively.

Compared with word2vec, GloVe requires constructing word word co-occurrence matrix and directly uses it for training word embeddings.

Though word2vec and Glove are different apparently, they essentially use word word co-occurrence to train word embeddings.

By training word vector with deep learning, each word has their corresponding spatial coordinates in high-dimensional space.