Q. What does "shallow" NLP primarily focus on in the context of information retrieval and extraction?

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Q. For what purposes has deep NLP mainly been used in the past?

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Q. In the context of computer linguistics, what is emphasized by calling the IBM Watson system "DeepQA"?

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Q. What is a key component in Deep NLP systems for knowledge extraction or analysis?

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Q. How does the slot grammar used in the IBM Watson system enhance parsing for knowledge extraction?

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Q. What is the primary transformation that deep parsing leads to in the context of knowledge extraction?

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Q. What kind of engine is used in the DeepQA system for further processing the knowledge implied in the analyzed sentences?

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Q. What does the term "machine readers" refer to in the context of Deep NLP with a focus on knowledge extraction?

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Q. What is one of the key challenges for knowledge extraction through machine reading?

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Q. Why is entity or predicate disambiguation needed in machine reading?

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Q. What is relation extraction used for in machine reading?

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Q. What is one of the criteria for distinguishing between document scope and corpus scope in knowledge discovery analyses on textual data?

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Q. In the context of the modeling level of analysis, what is the primary distinction between information extraction and knowledge extraction?

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Q. What modeling focus is distinguished for information retrieval?

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Q. How does document classification help in the context of electronic patent filing?

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Q. In knowledge discovery procedures, what is the term "multilabeling" typically associated with?

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Q. In practice, what are the two machine learning method approaches often combined for in an exploratory phase?

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Q. What is the primary purpose of document-based information extraction?

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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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