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Models讲解了在监督学习框架下的序列标注任务，包括从训练集归纳函数，将新句子映射到标注序列。内容涵盖生成模型与噪声信道模型、隐马尔可夫模型（HMM）标注器，以及标注的基本定义与参数估计方法。文中还介绍Viterbi算法用于最优路径解码，并展示词性标注、命名实体识别及把命名实体提取形式化为标注问题的方法。",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/tagging-problems-and-hidden-markov-models-overview/153917/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/tagging-problems-and-hidden-markov-models-overview/153917.png","ImageObject",300,407,{"name":92,"@type":93},"Jake","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-14","2026-08-28",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",18,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"什么是标注问题（Tagging Problem）？","Question",{"text":113,"@type":114},"标注问题的目标是学习一个函数，把输入句子映射到对应的标签序列。通过训练集归纳该函数后，可对新句子给出标签序列。","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"HMM 标注器在序列标注中扮演什么角色？",{"text":118,"@type":114},"HMM 标注器使用隐状态与转移/发射思想来对序列进行建模，并结合解码算法寻找最优标签序列。内容同时给出基本定义与参数估计。",{"name":120,"@type":111,"acceptedAnswer":121},"Viterbi 算法用于解决什么问题？",{"text":122,"@type":114},"Viterbi算法用于在给定模型条件下高效地找到最优路径，从而得到最可能的标签序列。","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},153917,1787884187,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145},962084928904,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Tagging Problems, and Hidden Markov Models  \nMichael Collins, Columbia University  \nOverview  \nI The Tagging Problem  \nI Generative models, and the noisy-channel model, for supervised learning  \nI Hidden Markov Model (HMM) taggers  \nI Basic de􀀌nitions  \nI Parameter estimation  \nI The Viterbi algorithm  \nPart-of-Speech Tagging  \nINPUT:  \nPro􀀌ts soared at Boeing Co. , easily topping forecasts on Wall Street, as their CEO Alan Mulally announced 􀀌rst quarter results.  \nOUTPUT:  \nPro􀀌ts/N soared/V at/P Boeing/N Co. /N ,/, easily/ADV topping/V forecasts/N on/P Wall/N Street/N ,/, as/P their/POSS CEO/N Alan/N Mulally/N announced/V 􀀌rst/ADJ quarter/N results/N . / .  \nN = Noun  \nV = Verb  \nP = Preposition  \nAdv = Adverb  \nAdj = Adjective: : :  \nNamed Entity Recognition  \nINPUT: Pro􀀌ts soared at Boeing Co. , easily topping forecasts on Wall Street, as their CEO Alan Mulally announced 􀀌rst quarter results.  \nOUTPUT: Pro􀀌ts soared at [Company Boeing Co.], easily topping forecasts on [Location Wall Street], as their CEO [Person Alan Mulally] announced 􀀌rst quarter results.  \nNamed Entity Extraction as Tagging  \nINPUT:  \nPro􀀌ts soared at Boeing Co. , easily topping forecasts on Wall Street, as their CEO Alan Mulally announced 􀀌rst quarter results.  \nOUTPUT:  \nPro􀀌ts/NA soared/NA at/NA Boeing/SC Co. /CC ,/NA easily/NA topping/NA forecasts/NA on/NA Wall/SL Street/CL ,/NA as/NA their/NA CEO/NA Alan/SP Mulally/CP announced/NA 􀀌rst/NA quarter/NA results/NA . /NA  \nNA = No entity  \nSC = Start Company CC = Continue Company SL = Start Location  \nCL = Continue Location  \n: : :  \nOur Goal  \nTraining set:  \n1 Pierre/NNP Vinken/NNP ,/, 61/CD years/NNS old/JJ ,/, will/MD join/VB the/DT board/NN as/IN a/DT nonexecutive/JJ director/NN Nov. /NNP 29/CD . / .  \n2 Mr. /NNP Vinken/NNP is/VBZ chairman/NN of/IN Elsevier/NNP  \nN.V. /NNP ,/, the/DT Dutch/NNP publishing/VBG group/NN . / .  \n3 Rudolph/NNP Agnew/NNP ,/, 55/CD years/NNS old/JJ and/CC chairman/NN of/IN Consolidated/NNP Gold/NNP Fields/NNP PLC/NNP,/, was/VBD named/VBN a/DT nonexecutive/JJ director/NN of/IN this/DT British/JJ industrial/JJ conglomerate/NN . / .  \n: : :  \n38,219 It/PRP is/VBZ also/RB pulling/VBG 20/CD people/NNS out/IN of/IN Puerto/NNP Rico/NNP ,/, who/WP were/VBD helping/VBGHuricane/NNP Hugo/NNP victims/NNS ,/, and/CC sending/VBG them/PRP to/TO San/NNP Francisco/NNP instead/RB . / .  \nI From the training set, induce a function/algorithm that maps new sentences to their tag sequences.  \nTwo Types of Constraints  \nIn􀀍uential/JJ members/NNS of/IN the/DT House/NNP Ways/NNP and/CC Means/NNP Committee/NNP introduced/VBD legislation/NN that/WDT would/MD restrict/VB how/WRB the/DT new/JJ savings-and-loan/NN bailout/NN agency/NN can/MD raise/VB capital/NN . / .  \nI \\Local\": e.g. , can is more likely to be a modal verb MD rather than a noun NN  \nI \\Contextual\": e.g. , a noun is much more likely than a verb to follow a determiner  \nI Sometimes these preferences are in con􀀍ict:  \nThe trash can is in the garage  \nOverview  \nI The Tagging Problem  \nI Generative models, and the noisy-channel model, for supervised learning  \nI Hidden Markov Model (HMM) taggers  \nI Basic de􀀌nitions  \nI Parameter estimation  \nI The Viterbi algorithm  \nSupervised Learning Problems  \nI We have training examples x (i); y (i) for i = 1 : : : m. Each x (i) is an input, each y (i) is a label.  \nI Task is to learn a function f mapping inputs x to labels f (x)  \nSupervised Learning Problems  \nI We have training examples x (i); y (i) for i = 1 : : : m. Each x (i) is an input, each y (i) is a label.  \nI Task is to learn a function f mapping inputs x to labels f (x)  \nI Conditional models:  \nI Learn a distribution p (yj x) from training examples I For any test input x, de􀀌ne f (x) = arg max y p(yj x)","cbCaikCfvszHb4Td","https://ap.wps.com/l/cbCaikCfvszHb4Td","pdf",243013,38,"English","# The Tagging Problem\n## Generative and noisy-channel models for supervised learning\n## Hidden Markov Model (HMM) taggers\n## Basic definitions\n## Parameter estimation\n## The Viterbi algorithm\n# Part-of-Speech Tagging\n## Named Entity Recognition\n## Named Entity Extraction as Tagging\n# Our Goal\n## Training set and inducing a mapping function\n# Two Types of Constraints\n## Influential preferences\n## Contextual preferences\n# Supervised Learning Problems\n## Learning conditional models p(y|x)","[{\"question\":\"什么是标注问题（Tagging Problem）？\",\"answer\":\"标注问题的目标是学习一个函数，把输入句子映射到对应的标签序列。通过训练集归纳该函数后，可对新句子给出标签序列。\"},{\"question\":\"HMM 标注器在序列标注中扮演什么角色？\",\"answer\":\"HMM 标注器使用隐状态与转移/发射思想来对序列进行建模，并结合解码算法寻找最优标签序列。内容同时给出基本定义与参数估计。\"},{\"question\":\"Viterbi 算法用于解决什么问题？\",\"answer\":\"Viterbi算法用于在给定模型条件下高效地找到最优路径，从而得到最可能的标签序列。\"}]","Tagging Problems and Hidden Markov Models - Overview | PDF",96]