Deep text mining (Record no. 6564)

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control field 20220107122853.0
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fixed length control field 190710b xxu||||| |||| 00| 0 eng d
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Transcribing agency IIITMK
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Personal name Agilna K C (41718001)
9 (RLIN) 16148
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Title Deep text mining
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Extent MPhil CS 2018-2019
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General note For decades, humans have dreamed of computers that understand natural language<br/>in the form of text or speech. The interaction between human and machine using<br/>natural languages is achieved through Natural language processing (NLP) with the help<br/>of smart assistants. Natural Language Processing and Text Mining or Text Analytics<br/>are Articial Intelligence (AI) technologies that endow users in transforming the key<br/>content in text documents into quantiable and actionable insights.<br/>Text in the documents is a rampant form of communication. The analyzing and<br/>understanding of these text includes multiple tasks which needs to instruct the computer<br/>to understand word-sense disambiguation. It addresses toilsome scaling and language<br/>challenges where traditional NLP techniques are less eective. Deep Text Mining, a<br/>deep learning-based text understanding helps to acquire this task to some extent. Deep<br/>text utilizes several deep neural network architectures like convolutional and recurrent<br/>neural networks and is able to perform word-level and character-level based learning.<br/>Deep learning uses a mathematical concept called word embeddings that preserves the<br/>semantic relationship among words. So, when represented properly, the word embedding<br/>allows capturing the in-depth semantic meaning of words. Word embedding techiques<br/>like GloVe, Word2Vec are euclidean approaches which fails to represent for hierarchical<br/>structures. So a better approach for representation of word embeddings is a requisite<br/>in the eld of text mining. This work attempts to builds a deep learning model with<br/>poincare word embeddings for the task of intent classication.
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Degree type MPhil CS
Name of granting institution 2018-2019
Year degree granted INT
-- Dr Asharaf S
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Topical term or geographic name entry element TEXT ANALYTICS
9 (RLIN) 16149
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Topical term or geographic name entry element CONVOLUTIONAL NEURAL NETWORKS
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Topical term or geographic name entry element DEEP LEARNING
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Topical term or geographic name entry element NATURAL LANGUAGE PROCESSING
9 (RLIN) 16152
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    Dewey Decimal Classification     Non Fiction IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   10/07/2019   R-1568 10/07/2019 10/07/2019 Project Reports