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《深度自然语言处理》课程教学课件(Natural language processing with deep learning)10 information extraction

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《深度自然语言处理》课程教学课件(Natural language processing with deep learning)10 information extraction
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西安交通大学Natural LanguageProcessingwithDeepLearningXIANHAOTONGUNIVERSITYIntroduction ofInformationExtraction交通大学Chen Li2023

Chen Li 2023 Introduction of Information Extraction Natural Language Processing with Deep Learning

Outline1.Introduction2.Solution Framework3. Deep learning Method5.Classical models6. Tools and data7.Frontier Research

1. Introduction 2. Solution Framework 3. Deep learning Method 5. Classical models 6. Tools and data 7. Frontier Research Outline

Outline1.Introduction2. Solution Framework3. Deep learning Method5.Classical models6. Tools and data7. Frontier Research

1. Introduction 2. Solution Framework 3. Deep learning Method 5. Classical models 6. Tools and data 7. Frontier Research Outline

IntroductionInformationExtractionInformation extraction (IE)systems.Find and understand limited relevantparts oftexts.Gatherinformation from many pieces oftext.Produce a structured representation of relevant information: relations (in the database sense), a.k.a.aknowledgebase麦道大学

• Information extraction (IE) systems • Find and understand limited relevant parts of texts • Gather information from many pieces of text • Produce a structured representation of relevant information: • relations (in the database sense), a.k.a., • a knowledge base l Information Extraction Introduction

IntroductionInformationExtractionInformation extraction (IE)systems.Find and understand limited relevant parts of texts. Gather information from many pieces of text.Produce a structured representation of relevant informationrelations (in the database sense), a.k.a..·a knowledge base交通大学: Goals:Organize information so that it is useful to people12.Put information ina semantically preciseform that allowsfurther inferences to be madeby computeralgorithms

• Information extraction (IE) systems • Find and understand limited relevant parts of texts • Gather information from many pieces of text • Produce a structured representation of relevant information: • relations (in the database sense), a.k.a., • a knowledge base • Goals: 1. Organize information so that it is useful to people 2. Put information in a semantically precise form that allows further inferences to be made by computer algorithms l Information Extraction Introduction

IntroductionInformationExtractionIE systems extract clear, factual information. Roughly: Who did what to whom when?麦通大学

• IE systems extract clear, factual information • Roughly: Who did what to whom when? l Information Extraction Introduction

IntroductionInformationExtractionlE systems extract clear,factual information. Roughly: Who did what to whom when?E.g.,. Gathering earnings, profits, board members, headquartersetc.from companyreports交道大学

• IE systems extract clear, factual information • Roughly: Who did what to whom when? • E.g., • Gathering earnings, profits, board members, headquarters, etc. from company reports l Information Extraction Introduction

IntroductionInformationExtractionlE systems extract clear,factual information. Roughly: Who did what to whom when?E.g.,. Gathering earnings, profits, board members, headquarters,etc.from company reportsThe headquarters of BHP Billiton Limited, and the globalheadquarters of the combinedBHPBillitonGroup,arelocatedinMelbourne,Australiaheadquarters("BHPBilitonLimited",“Melbourne,Australia")

• IE systems extract clear, factual information • Roughly: Who did what to whom when? • E.g., • Gathering earnings, profits, board members, headquarters, etc. from company reports • The headquarters of BHP Billiton Limited, and the global headquarters of the combined BHP Billiton Group, are located in Melbourne, Australia. • headquarters(“BHP Biliton Limited” , “Melbourne, Australia”) l Information Extraction Introduction

IntroductionInformationExtractionIEsystems extract clear,factual information. Roughly: Who did what to whom when?E.g.,. Gathering earnings, profits, board members, headquarters,etc.from company reportsThe headquarters of BHP Billiton Limited, and the globalheadquarters of the combined BHP Billiton Group, arelocatedinMelbourne,Australiaheadquarters("BHPBilitonLimited",“Melbourne,Australia")Learndrug-geneproductinteractionsfrommedical researchliterature

• IE systems extract clear, factual information • Roughly: Who did what to whom when? • E.g., • Gathering earnings, profits, board members, headquarters, etc. from company reports • The headquarters of BHP Billiton Limited, and the global headquarters of the combined BHP Billiton Group, are located in Melbourne, Australia. • headquarters(“BHP Biliton Limited” , “Melbourne, Australia”) • Learn drug-gene product interactions from medical research literature l Information Extraction Introduction

IntroductionInformationExtractionautomatic identification and classification of instances of user-specified types ofentities,relations,andeventsfromtext.Unstructured textStructured sequencesSign of the Zodiac:.AriesTaurusThesecond signoftheZodiacis3GeminiTaurus.Most Common Cause ofStrokes arethe third most common cause ofDeath in America:Heart Diseasedeathin Americatoday.CancerStroke.NostudywouldbecompletewithoutmentioningthelargestrodentintheLargest rodent in theworld,theCapybara.world:CapybaraBeaverPatagonian Cavies3

l Information Extraction automatic identification and classification of instances of user-specified types of entities, relations, and events from text. Unstructured text Structured sequences Introduction

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