"language corpora" is a correct and usable phrase in written English.
It refers to a collection of language data that has been compiled for analysis. For example, "Computers are often used to search through large language corpora to identify linguistic trends."
Semantics derived automatically from language corpora contain human-like biases.
The entire framework is based on information theory and utilizes a Markovian language model derived from linguistic analysis of language corpora for English, French, and German.
Currently, the majority of existing semantic annotation tools identify and tag partial core semantic information in language data, but they tend to be applicable only for modern language corpora.
Rather than trying to sanitize or eliminate them, we should treat biases as part of the language and establish an explicit way in machine learning of determining what we consider acceptable and unacceptable". The paper, "Semantics derived automatically from language corpora contain human-like biases," was published April 14 in Science.
But it also aims at going beyond our individual practice to set out the sources and grounds of the VinoLingua scientific framework and to examine the ideas about language and learning on which our approach is based: the importance of working with real language corpora and the use of standardised expressions and off-the-peg chunks of varying degrees of complexity for LSP teaching and learning.
They also used natural language corpora and ontology such as WordNet (Miller, 1995; Mitkov et al., 2006).
The data come from The Helsinki Cross-Sectional Child Language Corpus.
The words and phrases in a language corpus are encoded as DNA molecules to build a language model of the corpus.
I'll also be reviewing the iPad app for UCL's iGE (interactive Grammar of English), which draws on the survey's language corpus.
The numerical results, obtained on the Sleepy Language Corpus (SLC) by using Support Vector Machines SVMM) classifier, demonstrate a 10% improvement on average on unweighted recall rates compared to the benchmarking results.
Areas of Interest: Evolution, Acquisition and Processing of Language; Corpus Analyses; Computational Models of Language; Chunking, Learning and Memory; Statistical Learning; Cognitive Science of Language; Neuroimaging of Language; Genetics of Language.
