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What is Word Embedding

Advanced Applications of NLP and Deep Learning in Social Media Data
Document dense vocabulary representation in which similar words have a similar encoding mapping into low d-dimensional distributed real-valued vector. Capture the context of words in a document, semantic and syntactic similarity, relation with other words, etc. The technique often merged into the field of deep learning.
Published in Chapter:
Clustering Methods and Tools to Handle High-Dimensional Social Media Text Data
Marcellus Amadeus (Alana AI Research, Brazil) and William Alberto Cruz Castañeda (Alana AI Research, Brazil)
DOI: 10.4018/978-1-6684-6909-5.ch003
Abstract
Social media data has changed the way big data is used. The amount of data available offers more natural insights that make it possible to find relations and social interactions. Natural language processing (NLP) is an essential tool for such a task. NLP promises to scale traditional methods that allow the automation of tasks for social media datasets. A social media text dataset with a large number of attributes is referred to as a high-dimensional text dataset. One of the challenges of high-dimensional text datasets for NLP text clustering is that not all the measured variables are important for understanding the underlying phenomena of interest, and dimension reduction needs to be performed. Nonetheless, for text clustering, the existing literature is remarkably segmented, and the well-known methods do not address the problems of the high dimensionality of text data. Thus, different methods were found and classified in four areas. Also, it described metrics and technical tools as well as future directions.
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More Results
Information Retrieval Systems in Healthcare: Understanding Medical Data Through Text Analysis
A technique in natural language processing that represents words as vectors in a high-dimensional space. This captures semantic relationships and contextual meanings to enhance language understanding and machine learning applications.
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Metaphors in Business Applications: Modelling Subjectivity Through Emotions for Metaphor Comprehension
A learned numerical representation for a word where words having similar meaning have a similar representation.
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Classification of Product Backlog Items in Agile Software Development Using Machine Learning
Word embedding is a phrase used in natural language processing (NLP) to describe the representation of words for text analysis, often in the form of a real-valued vector that encodes the meaning of the word, such that words that are near in the vector space are considered to be similar in meaning. Word embeddings may be generated by employing language modelling and feature learning approaches in which words or phrases from the lexicon are mapped to real-number vectors.
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