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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 2521–2530 of 4002 papers

TitleStatusHype
Dissecting Contextual Word Embeddings: Architecture and Representation—0
Improving Cross-Lingual Word Embeddings by Meeting in the MiddleCode0
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric ApproachCode0
Churn Intent Detection in Multilingual Chatbot Conversations and Social MediaCode0
Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition—0
Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs—0
Reducing Gender Bias in Abusive Language Detection—0
The Influence of Down-Sampling Strategies on SVD Word Embedding Stability—0
SeVeN: Augmenting Word Embeddings with Unsupervised Relation VectorsCode0
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach—0
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