Selectively generating word vector and paragraph vector representations of fields for machine learning

Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence...

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Hauptverfasser:	Ganapathy, Chitrabharathi, Thakur, Aniruddha Madhusudan, Govindarajan, Kannan, Jayaraman, Baskar, Ramanna, Shiva Shankar
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS
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creator	Ganapathy, Chitrabharathi Thakur, Aniruddha Madhusudan Govindarajan, Kannan Jayaraman, Baskar Ramanna, Shiva Shankar
description	Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.
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subjects	CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS
title	Selectively generating word vector and paragraph vector representations of fields for machine learning
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