Sentence examples for lexical error from inspiring English sources

He had used the wrong word (lexical error) so the mediator first used an implicit meditational strategy (line 2).

A common lexical error across aphasia types is to substitute a difficult to retrieve verb with a semantically "light" verb containing little semantic information, such as come, go, make, take, get, give, do, have, be, put (Berndt, Haendiges, Mitchum, & Sandson, 1997).

Left hemisphere damaged signers produced significantly more lexical errors than did right hemisphere damaged signers, whereas the reverse pattern held for classifier signs.

Their written work on three academic writing tasks (in-class essays, in-class summaries and at-home summaries) performed twelve weeks apart (during the first and last weeks of the class) was analyzed in terms of its overall accuracy (total number of errors per 300 words), as well as grammatical and lexical errors separately.

For example, the students who received direct error correction made fewer pronoun and lexical errors.

Additionally, the learners preferred WCF on form-focused errors like grammatical and lexical errors.

The English debates were corrected for grammatical and lexical errors beforehand in an attempt to ameliorate (if not eliminate entirely) biases that might arise from imperfect English, while the Japanese speeches were back-translated from the English back into Japanese to help ensure the accuracy and reliability of the translations.

Damage to left BA 47/45 correlated with increased role-reversal errors, but not with increased lexical distractor errors (Fig. 4C; role-reversal errors: r = −0.635, P < 0.05; lexical errors: r = −0.234, P = 0.42).

Since lexical errors can be differentiated from clausal errors in the spoken language of people with aphasia, it is possible to differentiate gestures accompanying lexical errors from those accompanying clausal errors and thus to distinguish between the Lexical Hypothesis and the Interface Hypothesis.

Ascertaining representative rates of different types of errors such as duplications, omissions, phonetic alterations and lexical errors involved abstracting errors manually from a number of real world datasets and extrapolating these to the artificial data.

For example, some speakers will have a clear idea about the meaning they want to express whilst being unable to access the words which represent that meaning, causing them to make lexical errors.