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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 1271–1280 of 4002 papers

TitleStatusHype
Efficient Data Selection for Bilingual Terminology Extraction from Comparable Corpora—0
"A Passage to India": Pre-trained Word Embeddings for Indian Languages—0
Efficient Extraction of Pseudo-Parallel Sentences from Raw Monolingual Data Using Word Embeddings—0
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space—0
Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering—0
Determining Code Words in Euphemistic Hate Speech Using Word Embedding Networks—0
Bridging the Modality Gap: Enhancing Channel Prediction with Semantically Aligned LLMs and Knowledge Distillation—0
Evaluating Word Embeddings in Extremely Under-Resourced Languages: A Case Study in Bribri—0
EICA Team at SemEval-2018 Task 2: Semantic and Metadata-based Features for Multilingual Emoji Prediction—0
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context—0
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