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2017
Taking our discussion further along from Chapter 4, on machine learning basics, we will discuss here the next topic: natural language processing, or the way machines and computers process and understand natural (human) languages.
Srikanth Machiraju, Ritesh Modi
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Taking our discussion further along from Chapter 4, on machine learning basics, we will discuss here the next topic: natural language processing, or the way machines and computers process and understand natural (human) languages.
Srikanth Machiraju, Ritesh Modi
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Journal of the American Society for Information Science, 1984
AbstractNatural language processing has two primary roles to play in the storage and retrieval of large bodies of information: providing a friendly, easily‐learned interface to information retrieval systems, and automatically structuring texts so that their information can be more easily processed and retrieved.
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AbstractNatural language processing has two primary roles to play in the storage and retrieval of large bodies of information: providing a friendly, easily‐learned interface to information retrieval systems, and automatically structuring texts so that their information can be more easily processed and retrieved.
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Advances in natural language processing
Science, 2015Natural language processing employs computational techniques for the purpose of learning, understanding, and producing human language content. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, speech recognition ...
Julia Hirschberg, Christopher D. Manning
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2019
In the previous chapters, we took a look at different machine learning algorithms and then we worked on how to work with data for those algorithms. Those data sets were largely tabular, and the individual values were either numerical or categorical.
Gopinath Rebala +2 more
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In the previous chapters, we took a look at different machine learning algorithms and then we worked on how to work with data for those algorithms. Those data sets were largely tabular, and the individual values were either numerical or categorical.
Gopinath Rebala +2 more
openaire +4 more sources
Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005
Summary form only given. Natural language processing (NLP) is a major area of artificial intelligence research, which in its turn serves as a field of application and interaction of a number of other traditional AI areas. Until recently, the focus in AI applications in NLP was on knowledge representation, logical reasoning, and constraint satisfaction -
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Summary form only given. Natural language processing (NLP) is a major area of artificial intelligence research, which in its turn serves as a field of application and interaction of a number of other traditional AI areas. Until recently, the focus in AI applications in NLP was on knowledge representation, logical reasoning, and constraint satisfaction -
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1976
Despite the apparent lack of effect, people frequently talk to their machines. To replace such fruitless monologs with productive dialogs is probably the most important and most ambitious goal of artificial intelligence. Since nearly all of man’s intellectual activities involve language, a full mechanical language processing capability would seem to ...
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Despite the apparent lack of effect, people frequently talk to their machines. To replace such fruitless monologs with productive dialogs is probably the most important and most ambitious goal of artificial intelligence. Since nearly all of man’s intellectual activities involve language, a full mechanical language processing capability would seem to ...
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A unified architecture for natural language processing: deep neural networks with multitask learning
International Conference on Machine Learning, 2008R. Collobert, J. Weston
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Foundations of Statistical Natural Language Processing
Information retrieval (Boston), 2001P. Kantor
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2007
In most natural language processing applications, Description Logics have been used to encode in a knowledge base some syntactic, semantic, and pragmatic elements needed to drive the semantic interpretation and the natural language generation processes.
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In most natural language processing applications, Description Logics have been used to encode in a knowledge base some syntactic, semantic, and pragmatic elements needed to drive the semantic interpretation and the natural language generation processes.
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