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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 1361–1370 of 4002 papers

TitleStatusHype
With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation—0
WLV-RIT at HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification in Code-switched YouTube Comments—0
Robust Backed-off Estimation of Out-of-Vocabulary Embeddings—0
ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings—0
From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers—0
Vocabulary Adaptation for Domain Adaptation in Neural Machine TranslationCode0
Alignment-free Cross-lingual Semantic Role Labeling—0
Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated SolutionCode0
Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training—0
The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish—0
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