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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 3241–3250 of 4002 papers

TitleStatusHype
Beyond Adjacency Pairs: Hierarchical Clustering of Long Sequences for Human-Machine Dialogues—0
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context—0
Beyond Context: A New Perspective for Word Embeddings—0
Beyond Offline Mapping: Learning Cross Lingual Word Embeddings through Context Anchoring—0
Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring—0
Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases—0
BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media—0
Gender bias in (non)-contextual clinical word embeddings for stereotypical medical categories—0
Bias in word embeddings—0
Bidirectional Long Short-Term Memory Networks for Relation Classification—0
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