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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 3281–3290 of 4002 papers

TitleStatusHype
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content—0
Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models—0
BLCU\_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention—0
Blind signal decomposition of various word embeddings based on join and individual variance explained—0
Blinov: Distributed Representations of Words for Aspect-Based Sentiment Analysis at SemEval 2014—0
BLISS in Non-Isometric Embedding Spaces—0
Boosting Named Entity Recognition with Neural Character Embeddings—0
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora—0
Bootstrapping Multilingual AMR with Contextual Word Alignments—0
Bootstrapping NLU Models with Multi-task Learning—0
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