Editing Natural language processing (section)

=== Higher-level NLP applications ===
; [[Automatic summarization]] (text summarization): Produce a readable summary of a chunk of text.  Often used to provide summaries of the text of a known type, such as research papers, articles in the financial section of a newspaper.
;{{visible anchor|Grammatical error correction}}
:Grammatical error detection and correction involves a great band-width of problems on all levels of linguistic analysis (phonology/orthography, morphology, syntax, semantics, pragmatics). Grammatical error correction is impactful since it affects hundreds of millions of people that use or acquire English as a second language. It has thus been subject to a number of shared tasks since 2011.<ref>{{Cite web|last=Administration|title=Centre for Language Technology (CLT)|url=https://www.mq.edu.au/research/research-centres-groups-and-facilities/innovative-technologies/centres/centre-for-language-technology-clt|access-date=2021-01-11|website=Macquarie University|language=en-au}}</ref><ref>{{Cite web|title=Shared Task: Grammatical Error Correction|url=https://www.comp.nus.edu.sg/~nlp/conll13st.html|access-date=2021-01-11|website=www.comp.nus.edu.sg}}</ref><ref>{{Cite web|title=Shared Task: Grammatical Error Correction|url=https://www.comp.nus.edu.sg/~nlp/conll14st.html|access-date=2021-01-11|website=www.comp.nus.edu.sg}}</ref> As far as orthography, morphology, syntax and certain aspects of semantics are concerned, and due to the development of powerful neural language models such as [[GPT-2]], this can now (2019) be considered a largely solved problem and is being marketed in various commercial applications.
;[[Logic translation]]
:Translate a text from a natural language into formal logic.
; [[Machine translation]] (MT)
:Automatically translate text from one human language to another.  This is one of the most difficult problems, and is a member of a class of problems colloquially termed "[[AI-complete]]", i.e. requiring all of the different types of knowledge that humans possess (grammar, semantics, facts about the real world, etc.) to solve properly.
; [[Natural-language understanding]] (NLU): Convert chunks of text into more formal representations such as [[first-order logic]] structures that are easier for [[computer]] programs to manipulate. Natural language understanding involves the identification of the intended semantic from the multiple possible semantics which can be derived from a natural language expression which usually takes the form of organized notations of natural language concepts. Introduction and creation of language metamodel and ontology are efficient however empirical solutions. An explicit formalization of natural language semantics without confusions with implicit assumptions such as [[closed-world assumption]] (CWA) vs. [[open-world assumption]], or subjective Yes/No vs. objective True/False is expected for the construction of a basis of semantics formalization.<ref>{{cite journal|last1=Duan|first1=Yucong|last2=Cruz|first2=Christophe|year=2011|title=Formalizing Semantic of Natural Language through Conceptualization from Existence|url=http://www.ijimt.org/abstract/100-E00187.htm|journal=International Journal of Innovation, Management and Technology|volume=2|issue=1|pages=37–42|archive-url=https://web.archive.org/web/20111009135952/http://www.ijimt.org/abstract/100-E00187.htm|archive-date=2011-10-09}}</ref>
; [[Natural language generation|Natural-language generation]]<nowiki> (NLG):</nowiki>
:Convert information from computer databases or semantic intents into readable human language.
; Book generation
:Not an NLP task proper but an extension of natural language generation and other NLP tasks is the creation of full-fledged books. The first machine-generated book was created by a rule-based system in 1984 (Racter, ''The policeman's beard is half-constructed'').<ref>{{Cite web|title=U B U W E B :: Racter|url=http://www.ubu.com/historical/racter/index.html|access-date=2020-08-17|website=www.ubu.com}}</ref> The first published work by a neural network was published in 2018, ''[[1 the Road]]'', marketed as a novel, contains sixty million words. Both these systems are basically elaborate but non-sensical (semantics-free) [[language model]]s. The first machine-generated science book was published in 2019 (Beta Writer, ''Lithium-Ion Batteries'', Springer, Cham).<ref>{{Cite book|last=Writer|first=Beta|date=2019|title=Lithium-Ion Batteries|language=en-gb|doi=10.1007/978-3-030-16800-1|isbn=978-3-030-16799-8|s2cid=155818532}}</ref> Unlike ''Racter'' and ''1 the Road'', this is grounded on factual knowledge and based on text summarization.
; [[Document AI]]
:A Document AI platform sits on top of the NLP technology enabling users with no prior experience of artificial intelligence, machine learning or NLP to quickly train a computer to extract the specific data they need from different document types. NLP-powered Document AI enables non-technical teams to quickly access information hidden in documents, for example, lawyers, business analysts and accountants.<ref>{{Cite web|title=Document Understanding AI on Google Cloud (Cloud Next '19) – YouTube|url=https://www.youtube.com/watch?v=7dtl650D0y0| archive-url=https://ghostarchive.org/varchive/youtube/20211030/7dtl650D0y0| archive-date=2021-10-30|access-date=2021-01-11|website=www.youtube.com| date=11 April 2019 }}{{cbignore}}</ref>
; [[Dialogue system|Dialogue management]]
:Computer systems intended to converse with a human.
; [[Question answering]]: Given a human-language question, determine its answer. Typical questions have a specific right answer (such as "What is the capital of Canada?"), but sometimes open-ended questions are also considered (such as "What is the meaning of life?").
; [[Text-to-image generation]]: Given a description of an image, generate an image that matches the description.<ref>{{Cite web |last=Robertson |first=Adi |date=2022-04-06 |title=OpenAI's DALL-E AI image generator can now edit pictures, too |url=https://www.theverge.com/2022/4/6/23012123/openai-clip-dalle-2-ai-text-to-image-generator-testing |access-date=2022-06-07 |website=The Verge |language=en}}</ref>
; Text-to-scene generation: Given a description of a scene, generate a [[3D model]] of the scene.<ref>{{Cite web |title=The Stanford Natural Language Processing Group |url=https://nlp.stanford.edu/projects/text2scene.shtml |access-date=2022-06-07 |website=nlp.stanford.edu}}</ref><ref>{{Cite book |last1=Coyne |first1=Bob |last2=Sproat |first2=Richard |title=Proceedings of the 28th annual conference on Computer graphics and interactive techniques |chapter=WordsEye |date=2001-08-01 |chapter-url=https://doi.org/10.1145/383259.383316 |series=SIGGRAPH '01 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=487–496 |doi=10.1145/383259.383316 |isbn=978-1-58113-374-5|s2cid=3842372 }}</ref>
; [[Text-to-video model|Text-to-video]]: Given a description of a video, generate a video that matches the description.<ref>{{Cite web |date=2022-11-02 |title=Google announces AI advances in text-to-video, language translation, more |url=https://venturebeat.com/ai/google-announces-ai-advances-in-text-to-video-language-translation-more/ |access-date=2022-11-09 |website=VentureBeat |language=en-US}}</ref><ref>{{Cite web |last=Vincent |first=James |date=2022-09-29 |title=Meta's new text-to-video AI generator is like DALL-E for video |url=https://www.theverge.com/2022/9/29/23378210/meta-text-to-video-ai-generation-make-a-video-model-dall-e |access-date=2022-11-09 |website=The Verge |language=en-US}}</ref>