Sentence examples for residual language from inspiring English sources

Tomblin et al. (1992) tested for residual language difficulties by placing heavy demands on participants' language performance.

This is the first study that has directly correlated activity in a domain general system, specifically the salience network, with residual language performance in post-stroke aphasia.

In language therapy, it is possible to introduce so-called constraints that force the patient to use those residual language skills, therefore pushing the individual to their verbal and communicative limits.

These data highlight the issue of general applicability of a treatment approach using suppression of the non-stroke hemisphere; for a subset of these patients the right IFG was also essential for residual language function.

In other words, it is important to constrain verbal communication so that the patient takes advantage even of those residual language skills he or she would normally not risk applying in everyday language interaction due to fear of failure.

Sentence repetition tasks are widely used clinically and have been considered especially useful because they appear to be sensitive to residual language processing weaknesses which may not be detected on other expressive and receptive language tasks (Conti-Ramsden et al., 2001; Stothard, Snowling, Bishop, Chipchase & Kaplan, 1998).

Residual languages are important and natural components of regular languages and several grammatical inference algorithms naturally rely on this notion.

An RFSA A is a NonDeterministic Automaton whose states corresponds to residual languages of the language LA it recognizes.

Finally, we design a new learning algorithm, DeLeTe2, based on the search for the inclusion relations between the residual languages of the target language.

The inclusion relations between residual languages of LA can be naturally materialized on A. We prove that the class of RFSAs is not polynomially characterizable.

In order to identify a given target language L, classical inference algorithms try to identify words which define identical residual languages of L. Here, we study whether it could be interesting to perform a tighter analysis by identifying inclusion relations between the residual languages of L. We consider the class of Residual Finite State Automata (RFSAs).