A comparison of the results of the language-independent QbE STD system with those of the language-dependent text-based STD system presented in this paper shows that it is clear that there is still ample room for improvement to approximate the performance of a language-independent QbE STD system to that of a language-dependent text-based STD system.
This ability is comparable to the performance of a language-trained bonobo (Pan paniscus) and children in a similar situation (Savage-Rumbaugh et al. 1993) and is evidence for the memory of What and Where in a domestic dog.
The performance of language learners at different language proficiency levels has been depicted in Fig. 1.
Every performance of a play in whatever language is itself a form of translation – an attempt to interpret and reanimate sparse and often enigmatic texts.
We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic patients over time.
First, we look at Parts of Speech (POS) tags and lemmas, two sources of word-level linguistic information that are known to make a contribution to the performance of conventional language models.
Sight & Sound magazine recently gave a stark analysis of the performance of foreign language films at cinemas in the UK.
The geometrical structure of the SVM vector space is completely determined by the kernel, so the selection of the kernel has a crucial impact on the performance of the language recognition systems.
This paper describes a preprocessing module for improving the performance of a Spanish into Spanish Sign Language (Lengua de Signos Española: LSE) translation system when dealing with sparse training data.
Then, the mean score and standard deviation of the performance of language learners in each level of language proficiency (low intermediate, intermediate, high intermediate) and in each instruction group ('focus on form' group and 'focus on forms' group) on each test (pre-test, post-test, follow-up test) was calculated.
The percentage of out of vocabulary (OOV) words affects the performance of the language model, since the recognition system cannot output them.
