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Deep linguistic processing is a natural language processing framework which draws on theoretical and descriptive linguistics. It models language predominantly by way of theoretical syntactic/semantic theory (e.g. CCG , HPSG , LFG , TAG , the Prague School ).
Deep structure and surface structure (also D-structure and S-structure although those abbreviated forms are sometimes used with distinct meanings) are concepts used in linguistics, specifically in the study of syntax in the Chomskyan tradition of transformational generative grammar. The deep structure of a linguistic expression is a theoretical ...
Grammar induction (or grammatical inference) [1] is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite state machine or automaton of some kind) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects.
In transformational grammar, each sentence in a language has two levels of representation: a deep structure and a surface structure. [3] The deep structure represents the core semantic relations of a sentence and is mapped onto the surface structure, which follows the phonological form of the sentence very closely, via transformations.
The AI programs first adapted to simulate both natural and artificial grammar learning used the following basic structure: Given A set of grammatical sentences from some language. Find A procedure for recognizing and/or generating all grammatical sentences in that language. An early model for AI grammar learning is Wolff's SNPR System.
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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 ...
During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1] In machine translation, the seq2seq model, as it was proposed in 2014, [24] would encode an input text into a fixed-length vector, which would then be decoded into an output text. If the input text is long, the fixed-length vector ...