Natural Language Processing and its ApplicationDr., Samir Rustamov,Assistant Professor, School of IT & Engineering, ADA University

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WhatisNaturalLanguageProcessing(NLP)?

• NaturalLanguageProcessing(NLP)isafieldofartificialintelligencethatenablescomputersinteractwithhumaninnaturallanguage.

Ultimategoal:Naturalhuman-to-computercommunication

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Computer Human

HumanJudge

• HumanJudge askstele-typedquestionstoComputer andHuman.• Computer’sjobistoactlikeahuman.• Human’s jobistoconvinceJudgethatheisnotmachine.• Computer isjudged“intelligent”ifitcanfoolthejudge• Judgmentofintelligenceislinkedtoappropriateanswerstoquestionsfromthesystem.

The Turing Test(Can Machine think? A. M. Turing, 1950)

NaturalLanguageProcessingMarkettoReach$22.3Billionby2025

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FormsofNaturalLanguage

• Theinput/outputofaNLPsystemcanbe:• writtentext• speech

• Toprocesswrittentext,weneed:• lexical,syntactic,semanticknowledgeaboutthelanguage• discourseinformation,realworldknowledge

• Toprocessspokenlanguage,weneedeverythingrequiredtoprocesswrittentext,plusthechallengesofspeechrecognitionandspeechsynthesis.

ComponentsofNLP

NaturalLanguageUnderstandingMappingthegiveninputinthenaturallanguageintoausefulrepresentation.

NaturalLanguageGenerationProducingoutputinthenaturallanguage

fromsomeinternalrepresentation.

NLUnderstanding ismuchharderthanNLGeneration.But,stillbothofthemarehard.

WhyNLUnderstandingishard?

• Naturallanguageisextremelyrichinformandstructure,andveryambiguous.• Oneinputcanmeanmanydifferentthings.Ambiguitycanbeatdifferentlevels.• Lexical(wordlevel)ambiguity-- differentmeaningsofwords• Syntacticambiguity-- differentwaystoparsethesentence• Interpretingpartialinformation-- howtointerpretpronouns

• Manyinputcanmeanthesamething.• Interactionamongcomponentsoftheinputisnotclear.

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Exampleofambiguity

• Someinterpretationsof: Adamı gördüm.1. Isawtheman.2. IsawAdam3. Isawmyisland.4. Ivisitedmyisland.5. IsawmyADA6. IvisitedmyADA7. Ibribedtheman.

• SemanticAmbiguity:• gör tosee• gör tovisit• gör tobribe

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ResolveAmbiguities

• lexicaldisambiguation -- Resolutionofpart-of-speechandword-senseambiguitiesaretwoimportantkindsoflexicaldisambiguation.• syntacticambiguity -- canbeaddressedbyprobabilisticparsing.

WhatisPoS tagging?Whyisitimportant?

“gül” - ?

Eachwordhasapart-of-speechtagtodescribeitscategory.POSTaggerstrytofindPOStagsforthewords.

Whyitmatters?Applicationsu Machinetranslation– “Daşıdaşı”

Phonetics&Phonology (Speechsound)

Morphology, LexiconWords&theirforms(Words&theirforms)

Syntax,Parsing (Structureofsentences)

Semantics(Meaningofsentences)

Pragmatics (Meaning incontext&forapurpose )

Discourse(Connectedsentenceprocessinginalargerbodyoftext)

LanguageProcessing

Applicationsforspellingcorrection

Websearch

PhonesWordprocessing

Spellingcorrection

- Cinseddi dunyanınyedimocusəsindənbirdir.- Niye?- Cunki duzəldikleti enuzunomurlu sheydi.f( )=

-Çinsəddidünyanınyeddimöcüzəsindənbiridir.- Niyə?- Çünkidüzəltdiklətiənuzunömürlüşeydir.

WhenthespaceorganizationNASAfirststartedsendingupastronaunts theydiscoveredballpointpenswouldnotworkinzerogravity.Tosolvetheproblem,NASAscientistsspenttenyearsand12billion todevelopapenthatwouldwriteinzerogravity,upsidedown,underwater,onalltypesofsurface,andattemperaturesrangingfrombelowfreezing to300C.Russiansusedapencil.Originaltext

NASAkosmik təşkilatı ilkdəfə astronavtların göndərilməsinə başladıqları zaman,ballpointqələmləri sıfır çəkisi ilə işləməyəcəkdi.Problemi həll etmək üçün,NASAalimləri sıfır ağırlıq,baş aşağı,su altında,bütün səthlərdə və aşağıdadondurmadan 300dərəcə qədər dəyişən temperaturda yazacaqbir qələmhazırlamaq üçün onil və 12milyard dollar sərf etmişdir.Ruslar bir qələmistifadə edirdi.Google

Kosmos təşkilatı NASAilkastronaunts qaldıraraq başlayanda onlar kəşf diyircəkliqələmlər çəkisizlik şəraitində işləməyəcək.Problemin həlli,NASAalimləriçəkisizlik yazacaqqələm inkişaf etdirmək,onil və 12milyard xərcləyib,tərsinə,su altında,bütün səthinin növləri,300c-aşaxta tutmuş və temperaturda .karandaş istifadə olunan Ruslar.Dilmanc

Machinetranslation

Whyismachinetranslationhard?

• Requiresbothunderstandingthe“from”languageandgeneratingthe“to”language.

• Howcanweteachacomputera“secondlanguage”whenitdoesn’tevenreallyhaveafirstlanguage?

• Canwedomachinetranslationwithoutsolvingnaturallanguageunderstanding andnaturallanguagegeneration first?

TextClassification

•Assigning subjectcategories,topics,orgenres•Spamdetection•Authorship identification•Age/gender identification• LanguageIdentification•Sentimentanalysis•…

Informationretrieval

• Informationretrieval istheactivityofobtaininginformationresourcesrelevanttoaninformationneedfromacollectionofinformationresources.

TextSummarization• Goal:produceanabridgedversionofatextthatcontainsinformationthatisimportantorrelevanttoauser.

Dialoguesystems

• A dialog system or conversational agent (CA)is acomputer systemintended to conversewith ahuman,with acoherent structure.

QuestionAnswering:

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Deep Learning Algorithms NLP Usage

NeuralNetwork– NN(feed)

•Part-of-speechTagging•Tokenization•NamedEntityRecognition•IntentExtraction

RecurrentNeuralNetworks-(RNN)•MachineTranslation•QuestionAnsweringSystem•ImageCaptioning

RecursiveNeuralNetworks

•Parsingsentences•SentimentAnalysis•Paraphrasedetection•RelationClassification•Objectdetection

ConvolutionalNeuralNetwork-(CNN)

•Sentence/Textclassification•Relationextractionandclassification•Spamdetection•Categorizationofsearchqueries•Semanticrelationextraction

References:

• 1.OverviewofArtificialIntelligenceandNaturalLanguageProcessing.NAVDEEPSINGHGILL.https://www.upwork.com/hiring/for-clients/artificial-intelligence-and-natural-language-processing-in-big-data/

2.NaturalLanguageProcessingMarkettoReach$22.3Billionby2025https://www.tractica.com/newsroom/press-releases/natural-language-processing-market-to-reach-22-3-billion-by-2025/3.DanJurafsky.NaturalLanguageProcessingLectures.4.BİL711NaturalLanguageProcessing.Prof.Dr.İlyas Çiçekli.