The premise of symbolic NLP is well-summarized by John Searle's Chinese room experiment: Given a collection of rules (e.g., a Chinese phrasebook, with questions and matching answers), the computer emulates natural language understanding (or other NLP tasks) by applying those rules to the data it confronts.
Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old rule-based approach.
As of 2020, three trends among the topics of the long-standing series of CoNLL Shared Tasks can be observed:[46] Most higher-level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language.
More broadly speaking, the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP (see trends among CoNLL shared tasks above).
Cognition refers to "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses.
[49] Especially during the age of symbolic NLP, the area of computational linguistics maintained strong ties with cognitive studies.
[57] Likewise, ideas of cognitive NLP are inherent to neural models multimodal NLP (although rarely made explicit)[58] and developments in artificial intelligence, specifically tools and technologies using large language model approaches[59] and new directions in artificial general intelligence based on the free energy principle[60] by British neuroscientist and theoretician at University College London Karl J. Friston.