Introduction·InformationExtraction（NER+RE)NamedEntity:thewordorphrasethatrepresentsaspecificreal-worldobjectNamed entity recognition(NER):thetask of identifying all thementions(occurrences)ofaparticularNEtypeinthegivendocumentsDetection:Mr.Smith eats bitterballen[Mr.Smith] :ENTITYClassification:Mr.Smitheatsbitterballen[Mr.Smith] :PERSONRelation:usually denotes a well-defined (having a specific meaning)relationshipbetweentwoormoreNEsRelationshipExtraction (RE):identifymentions of the relations of interestin eachsentenceofthegivendocumentsEvent: involves the interaction of the trigger (the principal word defining the event)andmultiplearguments.ThethiefbrokethedoorwithahammerCAUSE HARMVerb:breakAgent: the thiefPatient:the doorInstrument:ahammer
● Named Entity： the word or phrase that represents a specific real-world object. ● Named entity recognition (NER): the task of identifying all the mentions (occurrences) of a particular NE type in the given documents. Detection: Mr. Smith eats bitterballen [Mr. Smith] : ENTITY Classification: Mr. Smith eats bitterballen [Mr. Smith] : PERSON ● Relation: usually denotes a well-defined (having a specific meaning) relationship between two or more NEs. ● Relationship Extraction (RE): identify mentions of the relations of interest in each sentence of the given documents. ● Event: involves the interaction of the trigger (the principal word defining the event) and multiple arguments. The thief broke the door with a hammer CAUSE_HARM Verb: break Agent: the thief Patient: the door Instrument: a hammer l Information Extraction （NER+ RE） Introduction
IntroductionNamedentityrecognitionA very important sub-task:find andclassify names intext, forexample:. The decision by the independent MP Andrew Wilkie to withdrawhis supportfor the minority Labor government sounded dramaticbut it should not further threaten its stability. When, after the2010 election, Wilkie, Rob Oakeshott, Tony Windsor and theGreens agreed to support Labor, they gave just two guarantees:confidenceandsupply
• A very important sub-task: find and classify names in text, for example: • The decision by the independent MP Andrew Wilkie to withdraw his support for the minority Labor government sounded dramatic but it should not further threaten its stability. When, after the 2010 election, Wilkie, Rob Oakeshott, Tony Windsor and the Greens agreed to support Labor, they gave just two guarantees: confidence and supply. l Named entity recognition Introduction
IntroductionNamedentityrecognitionA very important sub-task: find and classify names intext, for example:. The decision by the independent MP Andrew Wilkie to withdrawhis supportfor the minority Labor government sounded dramaticbut it should not further threaten its stability. When, after the2010 election, Wilkie, Rob Oakeshott, Tony Windsor and theGreens agreed to support Labor, they gave just two guarantees:confidence and supplyPerson Date Location Organization
Person Date Location Organization • A very important sub-task: find and classify names in text, for example: • The decision by the independent MP Andrew Wilkie to withdraw his support for the minority Labor government sounded dramatic but it should not further threaten its stability. When, after the 2010 election, Wilkie, Rob Oakeshott, Tony Windsor and the Greens agreed to support Labor, they gave just two guarantees: confidence and supply. l Named entity recognition Introduction
IntroductionNamedentityrecognitionThe uses:.Namedentitiescanbeindexed,linkedoff,etc.. Sentiment can be attributed to companies or products:A lot of lErelations are associations between named entities: For question answering, answers are often named entities.交通大学
• The uses: • Named entities can be indexed, linked off, etc. • Sentiment can be attributed to companies or products • A lot of IE relations are associations between named entities • For question answering, answers are often named entities. l Named entity recognition Introduction
IntroductionNamed entity recognitionThe uses:.Named entities canbeindexed,linkedoff,etc. Sentiment can be attributed to companies or products:A lotof lE relations are associations between named entities: For question answering, answers are often named entities.Concretely:: Many web pages tag various entities, with links to bio or topic pages,etc.Reuters'OpenCalais,Evri,AlchemyAPl,Yahoo'sTermExtraction,Apple/Google/Microsoft/...smartrecognizersfordocumentcontent
• The uses: • Named entities can be indexed, linked off, etc. • Sentiment can be attributed to companies or products • A lot of IE relations are associations between named entities • For question answering, answers are often named entities. • Concretely: • Many web pages tag various entities, with links to bio or topic pages, etc. • Reuters’ OpenCalais, Evri, AlchemyAPI, Yahoo’s Term Extraction, . • Apple/Google/Microsoft/. smart recognizers for document content l Named entity recognition Introduction