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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 1251–1260 of 4002 papers

TitleStatusHype
An Artificial Language Evaluation of Distributional Semantic Models—0
Cross-lingual Word Sense Disambiguation using mBERT Embeddings with Syntactic Dependencies—0
Automated Scoring of Clinical Expressive Language Evaluation Tasks—0
Cross-Lingual Word Representations: Induction and Evaluation—0
Cross-lingual Word Embeddings in Hyperbolic Space—0
Automated Preamble Detection in Dictated Medical Reports—0
Anaphora Resolution in Dialogue Systems for South Asian Languages—0
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER—0
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings—0
Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings—0
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