In machine learning, semantic analysis of a corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. A metalanguage based on predicate logic can analyze the speech of humans. Another strategy to understand the semantics of a text is symbol grounding. If language is grounded, it is equal to recognizing a machine readable meaning. For the restricted domain of spatial analysis, a computer based language understanding system was demonstrated.
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| - Semantic analysis (machine learning) (en)
- 語意分析 (zh)
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| - 語意分析(semantic analysis)技術是指將一長串的文字或內容,從其中分析出該個段落的摘要以及大意,甚至更進一步,將整篇文章的文意整理出來。此項技術可以應用在解讀影片、音訊等檔案,使得搜尋引擎能夠搜尋到文字以外的物件,方便使用者省去大量時間觀看影片、聆聽音訊,同時也可以幫助使用者提前了解影片與音訊的內容。 語意分析技術在早期基於奇異值分解(Singular Value Decomposition, SVD)、非負矩陣拆解法(Non-negative matrix factorization,NMF)等方式,近年來則有用各種型態的類神經網絡(Neural Network, NN)來完成語意分析的目的。 (zh)
- In machine learning, semantic analysis of a corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. A metalanguage based on predicate logic can analyze the speech of humans. Another strategy to understand the semantics of a text is symbol grounding. If language is grounded, it is equal to recognizing a machine readable meaning. For the restricted domain of spatial analysis, a computer based language understanding system was demonstrated. (en)
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| - In machine learning, semantic analysis of a corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. A metalanguage based on predicate logic can analyze the speech of humans. Another strategy to understand the semantics of a text is symbol grounding. If language is grounded, it is equal to recognizing a machine readable meaning. For the restricted domain of spatial analysis, a computer based language understanding system was demonstrated. Latent semantic analysis (sometimes latent semantic indexing), is a class of techniques where documents are represented as vectors in term space. A prominent example is PLSI. Latent Dirichlet allocation involves attributing document terms to topics. n-grams and hidden Markov models work by representing the term stream as a Markov chain where each term is derived from the few terms before it. (en)
- 語意分析(semantic analysis)技術是指將一長串的文字或內容,從其中分析出該個段落的摘要以及大意,甚至更進一步,將整篇文章的文意整理出來。此項技術可以應用在解讀影片、音訊等檔案,使得搜尋引擎能夠搜尋到文字以外的物件,方便使用者省去大量時間觀看影片、聆聽音訊,同時也可以幫助使用者提前了解影片與音訊的內容。 語意分析技術在早期基於奇異值分解(Singular Value Decomposition, SVD)、非負矩陣拆解法(Non-negative matrix factorization,NMF)等方式,近年來則有用各種型態的類神經網絡(Neural Network, NN)來完成語意分析的目的。 (zh)
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