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A unique combination of features defines a phoneme. Examples of phonemic or distinctive features are: [+/- voice], [+/- ATR] (binary features) and [ CORONAL] (a unary feature; also a place feature). Surface representations can be expressed as the result of rules acting on the features of the underlying representation. These rules are formulated ...
Data-driven learning (DDL) is an approach to foreign language learning. Whereas most language learning is guided by teachers and textbooks, data-driven learning treats language as data and students as researchers undertaking guided discovery tasks. Underpinning this pedagogical approach is the data - information - knowledge paradigm (see DIKW ...
In geolinguistics, areal features are elements shared by languages or dialects in a geographic area, [1] particularly when such features are not descended from a common ancestor or proto-language. An areal feature is contrasted with genetic relationship determined similarity within the same language family .
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. [1] Choosing informative, discriminating, and independent features is crucial to produce effective algorithms for pattern recognition, classification, and regression tasks.
Linguistic typology (or language typology) is a field of linguistics that studies and classifies languages according to their structural features to allow their comparison. Its aim is to describe and explain the structural diversity and the common properties of the world's languages. [ 1 ]
Corpus-assisted discourse studies (abbr.: CADS) is related historically and methodologically to the discipline of corpus linguistics.The principal endeavor of corpus-assisted discourse studies is the investigation, and comparison of features of particular discourse types, integrating into the analysis the techniques and tools developed within corpus linguistics.
The most likely language is the one with the model that is most similar to the model from the text needing to be identified. This approach can be problematic when the input text is in a language for which there is no model. In that case, the method may return another, "most similar" language as its result.
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.