《深度自然语言处理》课程教学课件（Natural language processing with deep learning）01 About the course

西安交通大学Natural Language Processingwith DeepLearningXIANHAOTONGUNIVERSITYAbout the course交通大学ChenLicli@xjtu.edu.cn2023

Chen Li cli@xjtu.edu.cn 2023 About the course Natural Language Processing with Deep Learning

AboutthelecturerChenLi（李辰）cli@xjtu.edu.cnAprofessorinthe School ofComputer Scienceand TechnologyAresearcherinArtificial Intelligence,especiallyAlinBiomedicine交道大学

About the lecturer • cli@xjtu.edu.cn • A professor in the School of Computer Science and Technology • A researcher in Artificial Intelligence, especially AI in Biomedicine Chen Li (李辰)

ResourcesSlidesandOther Materialshttp://ispace.xjtu.edu.cn/course/7223ReferencesSPEECHANDPATTERN RECOGNITIONDEEP LEARNINGLANGUAGEPROCESSINGAND MACHINE LEARNINGndAaronCCHRISTOPHERM.BISHOPFOUNDATIONSOFSTATISTICALNATURALLANGUAGEPROCESSINGCHRISTOPAERDMANNINGANDHINRICH SCHUTZEDANIELJURAFSKY&JAMESHMARTINThe related papers will be at the end of each chapter

Resources • http://ispace.xjtu.edu.cn/course/7223 Slides and Other Materials References • The related papers will be at the end of each chapter

Logic oftheCourseWhat is NLP, why NLP and HowNLP?General ButEssential(1th week)FundamentalTasksofNLPDistributedHypothesis andW2V(2hweek)Language ModelComputational Linguistics【(2-3mweek)Information ExtractionCoreference Resolution (4th week)NLUSentimentAnalysis(4th week)Sumarization(5in week)AdvancedTasksQuestion&AnsweringI (5thweek)NLGMachineTranslation(6th week)AIGC(6th week)DeepLearningProgamFramework(7th week)Applications(7th week)Application-biomedical text mining(8tn week)Projectdemonstration

Logic of the Course u What is NLP, why NLP and How NLP? u Fundamental Tasks of NLP u Distributed Hypothesis and W2V u Language Model u Information Extraction u Coreference Resolution u Sentiment Analysis u Sumarization u Question & Answering u Machine Translation u AIGC u Deep Learning Progam Framework u Application-biomedical text mining u Project demonstration Advanced Tasks Applications General But Essential Computational Linguistics （1th week） （7th week） （7th week） （8th week） （2th week） （2-3th week） （6th week） （4th week） （4th week） （5th week） （5th week） （6th week） NLU NLG

Logic of theCourseWhat is NLP,why NLP andHowNLP?General But Essential(1thweek)Statistical characteristics of NLpFundamental Tasks ofNLPDistributedHypothesisand W2V(2hweek)Computational Linguistics-3mweek)Language ModInformation ExtractioCoreference ReThat's a lot!SentimentAnalAdvanced TasksSumarization5hweeLGQuestion&Answering(6hweek)(6m,week)Machine TranslationDeepLearningProgamFramework(7hweek)Applications(7h week)Application-biomedical text mining(8tweek)Project demonstration

Logic of the Course u What is NLP, why NLP and How NLP? u Statistical characteristics of NLP u Fundamental Tasks of NLP u Distributed Hypothesis and W2V u Language Model u Information Extraction u Coreference Resolution u Sentiment Analysis u Sumarization u Question & Answering u Machine Translation u Deep Learning Progam Framework u Application-biomedical text mining u Project demonstration Advanced Tasks Applications General But Essential Computational Linguistics （1th week） （7th week） （7th week） （8th week） （2th week） （2-3th week） （6th week） （4th week） （4th week） （5th week） （5th week） （6th week） NLU NLG That’s a lot!

Assessments2assignmentsDetails will be available at the classes;Eachgroupcanhave1-3people;Grades of all assignments will be counted in.交通大学

Assessments • Details will be available at the classes; • Each group can have 1-3 people; • Grades of all assignments will be counted in. 2 assignments

Assessments2assignmentsDetails will be available at the classes;Each group can have 1-3 people;Grades of all assignments will be counted in.1final projectFinishing independentlyPresentations will beatthelast session.交通大学

Assessments • Details will be available at the classes; • Each group can have 1-3 people; • Grades of all assignments will be counted in. 2 assignments • Finishing independently; • Presentations will be at the last session. 1 final project

Assessments2assignmentsDetails will be available at the classes; Each group can have 1-3 people;Grades of all assignments will be counted in.1final projectFinishing independently,Presentations will be atthe last session.Grading policy交通大学2 assignments: 25% x 2 = 50%;Final project: 40%;Attendance:10%Late days will be counted against the grading.i.e. A perfect but late submission won't get a full score

Assessments • Details will be available at the classes; • Each group can have 1-3 people; • Grades of all assignments will be counted in. 2 assignments • Finishing independently; • Presentations will be at the last session. 1 final project • 2 assignments: 25% x 2 = 50%; • Final project: 40%; • Attendance: 10%. • Late days will be counted against the grading. i.e. A perfect but late submission won’t get a full score. Grading policy

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