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ID:37647674
大小:189.60 KB
页数:7页
时间:2019-05-27
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1、ᅲԧ݇㋏㞾ࡼᢑপ䔺ϛ㖨ˈ߬ᤎˈᴢ⫳˄જᇨⒼᎹϮᄺ䅵ㅫᴎᄺ䰶ˈજᇨⒼ150001˅ᨬ㽕˖ᅲԧ݇㋏ᢑপᰃᙃᢑপ乚ඳЁⱘ䞡㽕ⷨお䇒乬DŽᴀ᭛Փ⫼ϸ⾡Ѣ⡍ᕕ䞣ⱘᴎ఼ᄺдㅫ⊩ˈWinnowᬃᣕ䞣ᴎ˄SVM˅ˈ2004ᑈACE˄AutomaticContentExtraction˅䆘⌟ⱘ䆁㒗᭄Ϟ䖯㸠ᅲԧ݇㋏ᢑপᅲ偠DŽϸ⾡ㅫ⊩䛑䖯㸠䗖ᔧⱘ⡍ᕕ䗝ᢽˈᔧ䗝ᢽ↣ϾᅲԧⱘᎺেϸϾ䆡Ў⡍ᕕᯊˈ䖒ࠄ᳔དⱘᢑপᬜᵰˈWinnowSVMㅫ⊩ⱘࡴᴗᑇഛF-Scoreߚ߿Ў73.08%73.27%DŽৃ㾕Փ⫼Ⳍৠⱘ⡍ᕕ䲚ˈϡৠⱘᄺдㅫ⊩䖯㸠ᅲԧ݇㋏ⱘ䆚߿ᯊˈ᳔㒜ᗻ㛑Ꮒ߿ϡDŽℸ
2、Փ⫼㞾ࡼⱘᮍ⊩䖯㸠ᅲԧ݇㋏ᢑপᯊˈᑨᔧ䲚Ё㊒ᇏᡒདⱘ⡍ᕕDŽ݇䬂䆡˖ᅲԧ݇㋏ᢑপ˗ACE䆘⌟˗⡍ᕕ䗝ᢽ˗Ёߚ㉏ো˖TP391᭛⤂ᷛ䆚ⷕ˖AAutomaticEntityRelationExtractionWanxiangChe,TingLiu,ShengLi(SchoolofComputerScienceandTechnology,HarbinInstituteofTechnology,Harbin150001)Abstract:EntityRelationExtractionisanimportantresearchfieldinInformationExt
3、raction.Twokindsofmachinelearningalgorithms,WinnowandSupportVectorMachine(SVM),wereusedtoextractentityrelationfromthetrainingdataofACE(AutomaticContentExtraction)Evaluation2004automatically.Bothofthealgorithmsneedappropriatefeatureselection.Whentwowordsaroundanentitywereselected,thep
4、erformanceofthebothalgorithmsgotthepeak.TheaverageweightedF-ScoreofWinnowandSVMalgorithmswere73.08%and73.27%respectively.Wecanconcludethatwhenthesamefeaturesetisused,theperformanceofdifferentmachinelearningalgorithmsgetlittledifference.Soweshouldpaymoreattentiontofindbetterfeatureswh
5、enweusetheautomaticlearningmethodstoextracttheentityrelation.Keywords:entityrelationextraction;ACEevaluation;featureselection;䞥乍Ⳃ˖ᴀ᭛ফࠄᆊ㞾✊⾥ᄺ䞥˄60435020˅䌘ࡽDŽ㗙ㅔҟ˖䔺ϛ㖨˄1980-˅ˈ⬋ˈ咥啭∳Ҏˈमⷨお⫳ˈЏⓨⷨお乚ඳЎ㞾✊䇁㿔໘⧚ˈᙃẔ㋶ϔǃᓩ㿔䱣ⴔ䅵ㅫᴎⱘ᱂ঞҹঞѦ㘨㔥ⱘ䖙⣯থሩˈ䞣ⱘᙃҹ⬉ᄤ᭛ᴀⱘᔶᓣߎ⦄ҎӀ䴶ࠡDŽЎњᑨᇍᙃ⟚⚌ᏺᴹⱘᣥˈ䖿ߛ䳔㽕ϔѯ㞾ࡼ࣪ⱘᎹᐂࡽҎӀ⍋䞣ᙃ⑤Ё䖙䗳ᡒࠄ
6、ⳳℷ䳔㽕ⱘᙃDŽᙃᢑপ˄InformationExtraction˅ⷨおℷᰃ䖭⾡㚠᱃ϟѻ⫳ⱘDŽᙃᢑপⱘЏ㽕Ⳃⱘᰃᇚ᮴㒧ᵘⱘ᭛ᴀ䕀࣪Ў㒧ᵘ࣪ञ㒧ᵘ࣪ⱘᙃˈᑊҹ᭄ᑧⱘᔶᓣᄬټˈկ⫼᠋ᶹ䆶ҹঞ䖯ϔℹߚᵤ߽⫼DŽᙃᢑপ㋏㒳ⱘЏ㽕ࡳ㛑ᰃҢ᭛ᴀЁᢑপߎ⡍ᅮⱘџᅲᙃˈ៥Ӏ⿄ПЎᅲԧ˄Entity˅DŽ՟བ˖ᯊ䯈˄TIME˅ǃ㒘㒛ᴎᵘ˄ORG˅ǃҎ⠽˄PER˅ҹঞ℺఼˄WEAPON˅ㄝㄝDŽ✊㗠ˈ᭄ⱘᑨ⫼ЁˈϡԚ㽕䆚߿᭛ᴀЁⱘᅲԧˈ䖬㽕⹂ᅮ䖭ѯᅲԧП䯈ⱘ݇㋏ˈ៥Ӏ⿄݊Ўᅲԧ݇㋏ᢑপDŽϢᅲԧᢑপ㉏Ԑˈᅲԧ݇㋏ⱘ㉏ൟгᰃ乘ܜᅮНⱘˈ՟བ˖ഄ⧚ԡ㕂݇㋏˄PHYS˅ǃ䲛Է݇㋏˄EM
7、P-ORG˅ㄝㄝDŽ؛䆒᭛ᴀЁᦤࠄ¯Ă㕢ϗ㠄䯳ৌҸĂāˈ݊Ё¯㕢ϗ㠄䯳ৌҸā¯㕢ϗ㠄䯳āߚ߿ЎҎ⠽˄PER˅㒘㒛˄ORG˅ᅲԧDŽ㗠ᅗӀজᵘ៤њϔ⾡䲛Է݇㋏˄EMP-ORG˅ˈे¯㕢ϗ㠄䯳ৌҸāফ䲛Ѣ¯㕢ϗ㠄䯳āDŽ䗮䖛ҹϞҟ㒡ˈ៥Ӏথ⦄བᵰ䇈ᙃᢑপⱘЏ㽕ࡳ㛑ᰃ㞾ࡼᇚ᭛ᴀ䕀࣪Ў᭄㸼Ḑˈᅲԧᢑপ⹂ᅮњ㸼ḐЁϾܗ㋴ⱘ䆱ˈᅲԧ݇㋏ᢑপ߭ᰃ⹂ᅮ䖭ѯܗ㋴㸼ḐЁⱘⳌᇍԡ㕂DŽৃ㾕ˈᅲԧ݇㋏ᢑপᰃᙃᢑপЁⱘ䞡㽕⦃㡖DŽ䖥ᑈᴹˈ䱣ⴔᅲԧᢑপⷨお䗤ℹᅲ⫼࣪ˈᅲԧ݇㋏ᢑপⱘⷨおг䍞ᴹ䍞ফࠄҎӀⱘ䞡㾚DŽ݊Ё㕢䰆催㑻ⷨお䅵ߦྨਬӮ˄DARPAˈtheDefens
8、eAdvancedRes
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