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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 1401–1425 of 4002 papers

TitleStatusHype
A Survey On Neural Word Embeddings—0
Context-Aware Neural Machine Translation Decoding—0
context2vec: Learning Generic Context Embedding with Bidirectional LSTM—0
A Survey on Automatically-Constructed WordNets and their Evaluation: Lexical and Word Embedding-based Approaches—0
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't.—0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
Content-Aware Speaker Embeddings for Speaker Diarisation—0
A Survey Of Cross-lingual Word Embedding Models—0
Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals—0
Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense—0
A survey of cross-lingual features for zero-shot cross-lingual semantic parsing—0
Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
Consistent Structural Relation Learning for Zero-Shot Segmentation—0
Consistency and Variation in Kernel Neural Ranking Model—0
A supervised approach to taxonomy extraction using word embeddings—0
Analogies Explained: Towards Understanding Word Embeddings—0
A Deep Neural Framework for Contextual Affect Detection—0
A comparative analysis of embedding models for patent similarity—0
Considerations for the Interpretation of Bias Measures of Word Embeddings—0
Connecting Supervised and Unsupervised Sentence Embeddings—0
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop—0
Conditional Random Fields for Metaphor Detection—0
ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition.—0
Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation—0
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