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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 2941–2950 of 4002 papers

TitleStatusHype
Multilingual segmentation based on neural networks and pre-trained word embeddings—0
Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data—0
Multilingual Seq2seq Training with Similarity Loss for Cross-Lingual Document Classification—0
Multilingual Training of Crosslingual Word Embeddings—0
Multilingual Visual Sentiment Concept Matching—0
Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages—0
Multilingual Word Embeddings using Multigraphs—0
Multilingual Wordnet sense Ranking using nearest context—0
Multimedia Lab @ ACL WNUT NER Shared Task: Named Entity Recognition for Twitter Microposts using Distributed Word Representations—0
Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations—0
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