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Word Embeddings

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 851–860 of 4002 papers

TitleStatusHype
Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense—0
Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals—0
Content-Aware Speaker Embeddings for Speaker Diarisation—0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
context2vec: Learning Generic Context Embedding with Bidirectional LSTM—0
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't.—0
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content—0
Context-Aware Neural Machine Translation Decoding—0
Context-Dependent Sense Embedding—0
A novel methodology on distributed representations of proteins using their interacting ligands—0
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