Q. How is document classification based on information extraction used in opinion or sentiment mining?

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Q. What is the main distinction between knowledge extraction and information extraction?

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Q. What is the purpose of unsupervised methods for information extraction on the corpus level?

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Q. In multidocument analysis, how is document similarity typically measured?

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Q. How are entities and their relationships treated in the modeling level of information extraction?

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Q. What is the purpose of ontology-based text annotation technology in information extraction?

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Q. What does the vector space model consider a document as?

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Q. What is the purpose of combining the TF and IDF components in the term-document matrix?

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Q. What is the expectation of fij based on a uniform distribution of ti occurrences among all documents in the corpus referred to as?

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Q. What is the difference between the use of IDF and ETF weights in literary science and related approaches in the humanities?

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Q. What is the primary benefit of latent semantic analysis (LSA) over frequency-based document profiles?

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Q. What is the primary purpose of individual document classification in document topic analysis?

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Q. What technique is used to decompose the term by documents C matrix in latent semantic analysis (LSA)?

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Q. What is the purpose of the latent factors weight matrix Λ in LSA?

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Q. In LSA, what is the primary purpose of dimensionality reduction?

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Q. What characteristic do both matrices U and VT have in LSA?

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Q. What is the outcome of LSA analysis typically based on?

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Q. In the context of LSA, what is the benefit of using a subset of singular values for dimensionality reduction?

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Q. What does the "folding in" method allow LSA/LSI to do?

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Q. What is a key limitation of techniques like latent semantic analysis (LSA) as compared to generative approaches like LDA?

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