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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 2661–2670 of 4002 papers

TitleStatusHype
Diachronic word embeddings and semantic shifts: a survey—0
Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data—0
Probabilistic FastText for Multi-Sense Word EmbeddingsCode1
Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient MobilityCode0
Finding Convincing Arguments Using Scalable Bayesian Preference LearningCode0
The Limitations of Cross-language Word Embeddings EvaluationCode0
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?—0
Closed Form Word Embedding Alignment—0
Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction—0
Bidirectional Retrieval Made Simple—0
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