Editing Cognitive linguistics (section)

== Computational approaches ==

===Cognitive perspective on natural language processing===
Cognitive linguistics offers a scientific [[first principle]] direction for quantifying states-of-mind through [[natural language processing]]. <ref>{{cite journal |title=Semantic measures: Using natural language processing to measure, differentiate, and describe psychological constructs |last=Kjell |journal=Psychological Methods |volume=24 |issue=1 |pages=92–115 |year=2019 |doi=10.1037/met0000191 |pmid=29963879 |s2cid=49642731 |url=https://www.researchgate.net/publication/326141366}}</ref> As mentioned earlier Cognitive Linguistics, approaches grammar with a nontraditional view.  Traditionally [[grammar]] has been defined as a set of structural rules governing the composition of clauses, phrases and words in a natural language.  From the perspective of Cognitive Linguistics, grammar is seen as the rules of arrangement of language which best serve communication of the experience of the human organism through its [[Cognitive skill|cognitive skills]]  which include perception, attention, motor skills, and visual and spatial processing.<ref name="Croft&Cruse_2004" />  Such rules are derived from observing the conventionalized pairings of meaning to understand sub-context in the evolution of language patterns. <ref name="Dahl_2001" /> The cognitive approach to identifying sub-context by observing what comes before and after each linguistic construct provides a grounding of meaning in terms of sensorimotoric embodied experience. <ref name="Ibarretxe-Antuñano_2002" /> When taken together, these two perspectives form the basis of defining approaches in [[computational linguistics]]  with strategies to work through the [[symbol grounding problem]] which posits that, for a computer, a word is merely a symbol, which is a symbol for another symbol and so on in an unending chain without grounding in human experience. <ref>Vogt, Paul. "[http://ilk.uvt.nl/~pvogt/publications/acsChapter.pdf Language evolution and robotics: issues on symbol grounding and language acquisition]." Artificial cognition systems. IGI Global, 2007. 176–209.</ref>  The broad set of tools and methods of [[computational linguistics]] are available as [[natural language processing]] or NLP.  Cognitive linguistics adds a new set of capabilities to NLP.  These cognitive NLP methods enable software to analyze sub-context in terms of internal embodied experience. <ref name="Ibarretxe-Antuñano_2002" />

===Methods===
The goal of [[natural language processing]] (NLP) is to enable a computer to "understand" the contents of text and documents, including the contextual nuances of the language within them. 
The perspective of traditional [https://en.wikipedia.org/w/index.php?title=Chomskyan_linguistics&redirect=no Traditional Chomskyan Linguistics] offers NLP three approaches or methods to identify and quantify the literal contents, the who, what, where and when in text – in linguistic terms, the semantic meaning or [[semantics]] of the text. The perspective of cognitive linguistics offers NLP a direction to identify and quantify the contextual nuances, the why and how in text – in linguistics terms, the implied pragmatic meaning or [[pragmatics]] of text.

The three NLP approaches to understanding literal semantics in text based on traditional linguistics are symbolic NLP, statistical NLP, and neural NLP. The first method, [[Natural language processing#Symbolic NLP (1950s – early 1990s)|symbolic NLP]] (1950s – early 1990s) is based on first principles and rules of traditional linguistics.  The second method, [[Natural language processing#Statistical NLP (1990s–2010s)|statistical NLP]] (1990s–2010s), builds upon the first method with a layer of human curated & machine-assisted corpora for multiple contexts.  The third approach [[Natural language processing#Neural_NLP_(present)|neural NLP]] (2010 onwards), builds upon the earlier methods by leveraging advances in [[deep learning|deep neural network]]-style methods to automate tabulation of corpora & parse models for multiple contexts in shorter periods of time. <ref name="goldberg:nnlp17">{{cite journal |last=Goldberg |first=Yoav |year=2016 |arxiv=1807.10854 |title=A Primer on Neural Network Models for Natural Language Processing |journal=Journal of Artificial Intelligence Research |volume=57 |pages=345–420 |doi=10.1613/jair.4992 |s2cid=8273530 }}</ref><ref name="goodfellow:book16">{{cite book |first1=Ian |last1=Goodfellow |first2=Yoshua |last2=Bengio |first3=Aaron |last3=Courville |url=http://www.deeplearningbook.org/ |title=Deep Learning |publisher=MIT Press |year=2016 }}</ref> All three methods are used to power NLP techniques like [[stemming]] and [[lemmatisation]] in order to obtain statistically relevant listing of the who, what, where & when in text through [[named-entity recognition]] and [[Topic model]] programs. The same methods have been applied with NLP techniques like a [[bag-of-words model]] to obtain statistical measures of emotional context through [[sentiment analysis]] programs. The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments.{{cn|date=November 2023}} Because evaluation of sentiment analysis is becoming more and more specialty based, each implementation needs a separate training model and specialized human verification raising [[Inter-rater reliability]] issues. However, the accuracy is considered generally acceptable for use in evaluating emotional context at a statistical or group level. <ref>[[Jussi Karlgren|Karlgren, Jussi]], [[Magnus Sahlgren]], Fredrik Olsson, Fredrik Espinoza, and Ola Hamfors. "Usefulness of sentiment analysis." In European Conference on Information Retrieval, pp. 426-435. Springer Berlin Heidelberg, 2012.</ref><ref> [[Jussi Karlgren|Karlgren, Jussi]]. "[http://www.diva-portal.org/smash/get/diva2:1042636/FULLTEXT01.pdf Affect, appeal, and sentiment as factors influencing interaction with multimedia information]." In Proceedings of Theseus/Image CLEF workshop on visual information retrieval evaluation, pp. 8-11. 2009.</ref>

A developmental trajectory of NLP to understand contextual pragmatics in text involving emulating intelligent behavior and apparent comprehension of natural language is [[Natural language processing#General_tendencies_and_(possible)_future_directions|cognitive NLP]]. This method is a rules based approach which involves assigning meaning to a word, phrase, sentence or piece of text based on the information presented before and after the piece of text being analyzed.