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A modular view of sentence processing assumes that each factor involved in sentence processing is computed in its own module, which has limited means of communication with the other modules. For example, syntactic analysis creation takes place without input from semantic analysis or context-dependent information, which are processed separately.
It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a ...
The relation holds whenever the truth of one text fragment follows from another text. In the TE framework, the entailing and entailed texts are termed text (t) and hypothesis (h), respectively. The relation is directional because even if "t entails h", the reverse "h entails t" is much less certain. Triphone – sequence of three phonemes ...
A notable example of deep semantic annotation is the Groningen Meaning Bank, developed at the University of Groningen and annotated using Discourse Representation Theory. An example of a shallow semantic treebank is PropBank, which provides annotation of verbal propositions and their arguments, without attempting to represent every word in the ...
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 ).
Text world theory is a cognitive model of language processing which aims to explain how people construct meaning from language. [1] Text world theory and schema theory seek to help people understand how we process language and create mental representations when we read or listen to something. [ 1 ]
The Input Processing theory, put forth by Bill VanPatten in 1993, [1] describes the process of strategies and mechanisms that learners use to link linguistic form with its meaning or function. [2] Input Processing is a theory in second language acquisition that focuses on how learners process linguistic data in spoken or written language.
The XLNet was an autoregressive Transformer designed as an improvement over BERT, with 340M parameters and trained on 33 billion words.It was released on 19 June, 2019, under the Apache 2.0 license. [1]