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In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear approximately equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog word

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rdfs:label	Topic model (it) Topic model (fr) 토픽 모델 (ko) Topic model (en) Тематическое моделирование (ru) Тематичне моделювання (uk) 主题模型 (zh)
rdfs:comment	En apprentissage automatique et en traitement automatique du langage naturel, un topic model (modèle thématique ou « modèle de sujet ») est un modèle probabiliste permettant de déterminer des sujets ou thèmes abstraits dans un document. (fr) 主题模型（Topic Model）在机器学习和自然语言处理等领域是用来在一系列文档中发现抽象主题的一种统计模型。直观来讲，如果一篇文章有一个中心思想，那么一些特定词语会更频繁的出现。比方说，如果一篇文章是在讲狗的，那“狗”和“骨头”等词出现的频率会高些。如果一篇文章是在讲猫的，那“猫”和“鱼”等词出现的频率会高些。而有些词例如“这个”、“和”大概在两篇文章中出现的频率会大致相等。但真实的情况是，一篇文章通常包含多种主题，而且每个主题所占比例各不相同。因此，如果一篇文章10%和猫有关，90%和狗有关，那么和狗相关的关键字出现的次数大概会是和猫相关的关键字出现次数的9倍。一个主题模型试图用数学框架来体现文档的这种特点。主题模型自动分析每个文档，统计文档内的词语，根据统计的信息来断定当前文档含有哪些主题，以及每个主题所占的比例各为多少。主题模型最初是运用于自然语言处理相关方向，但目前以及延伸至例如生物信息学的其它领域。 (zh) In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear approximately equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog word (en) 기계 학습 및 자연언어 처리 분야에서 토픽 모델(Topic model)이란 문서 집합의 추상적인 "주제"를 발견하기 위한 통계적 모델 중 하나로, 텍스트 본문의 숨겨진 의미구조를 발견하기 위해 사용되는 텍스트 마이닝 기법 중 하나이다. 특정 주제에 관한 문헌에서는 그 주제에 관한 단어가 다른 단어들에 비해 더 자주 등장할 것이다. 예를 들어 개에 대한 문서에서는 "개"와 "뼈다귀"라는 단어가 더 자주 등장하는 반면, 고양이에 대한 문서에서는 "고양이"와 "야옹"이 더 자주 등장할 것이고, "그", "~이다"와 같은 단어는 양쪽 모두에서 자주 등장할 것이다. 이렇게 함께 자주 등장하는 단어들은 대게 유사한 의미를 지니게 되는데 이를 잠재적인 "주제"로 정의할 수 있다. 즉, "개"와 "뼈다귀"를 하나의 주제로 묶고, "고양이"와 "야옹"을 또 다른 주제로 묶는 모형을 구상할 수 있는데 바로 이것이 토픽 모델의 개략적인 개념이다. 실제로 문헌 내에 어떤 주제가 들어있고, 주제 간의 비중이 어떤지는 문헌 집합 내의 단어 통계를 수학적으로 분석함으로써 알아 낼 수 있다. (ko) Nell'apprendimento automatico e nell'elaborazione del linguaggio naturale, un topic model è un tipo di modello statistico per scoprire gli "argomenti" (topic) astratti che si verificano in una raccolta di documenti. Questi vengono frequentemente utilizzati per la scoperta di strutture semantiche nascoste in un testo o in una raccolta di testi. Intuitivamente, dato che un documento riguarda un argomento particolare, ci si aspetterebbe che nel documento compaiano parole particolari più o meno frequentemente: "cane" e "osso" appariranno più spesso nei documenti sui cani, "gatto" e "miagolio" appariranno nei documenti sui gatti e "il" e "è" appariranno approssimativamente allo stesso modo in entrambi. Un documento in genere riguarda più argomenti in proporzioni diverse; quindi, in un documento (it) Тематическое моделирование — способ построения модели коллекции текстовых документов, которая определяет, к каким темам относится каждый из документов. Тематическая модель (англ. topic model) коллекции текстовых документов определяет, к каким темам относится каждый документ и какие слова (термины) образуют каждую тему. Переход из пространства терминов в пространство найденных тематик помогает разрешать синонимию и полисемию терминов, а также эффективнее решать такие задачи, как тематический поиск, классификация, суммаризация и аннотация коллекций документов и новостных потоков. (ru) Тематичне моделювання — спосіб побудови моделі колекції текстових документів, яка визначає, до яких тем належить кожен з документів. Тематична модель (англ. topic model) колекції текстових документів визначає, до яких тем належить кожен документ, і які слова (терміни) утворюють кожну тему. Перехід з простору термінів в простір знайдених тематик допомагає вирішувати синонімію і полісемію термінів, а також ефективніше вирішувати такі завдання як тематичний пошук, класифікація, сумаризація і анотація колекцій документів і новинних потоків. (uk)
dcterms:subject	Latent variable models Statistical natural language processing Corpus linguistics
Wikipage page ID	28934119 (xsd:integer)
Wikipage revision ID	1112640170 (xsd:integer)
Link from a Wikipage to another Wikipage	Method of moments (statistics) Natural language processing Non-negative matrix factorization Pennsylvania Gazette David Blei Gensim Andrew Ng Mallet (software project) Singular value decomposition Statistical model Statistics Latent variable models Computer vision Probabilistic latent semantic indexing Statistical natural language processing Latent Dirichlet allocation Latent semantic analysis American Civil War PNAS Dirichlet distribution Richmond Times-Dispatch Hierarchical Dirichlet process Corpus linguistics Bioinformatics Michael I. Jordan Stochastic block model Statistical classification Explicit semantic analysis Unsupervised learning Pachinko allocation dbr:File:Topic_model_scheme.webm
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sameAs	Topic model Topic model Topic model Topic model Topic model Topic model Topic model Topic model

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