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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 1021–1030 of 4002 papers

TitleStatusHype
SINAI at SemEval-2021 Task 5: Combining Embeddings in a BiLSTM-CRF model for Toxic Spans Detection—0
CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way—0
“Are you calling for the vaporizer you ordered?” Combining Search and Prediction to Identify Orders in Contact Centers—0
TUDA-CCL at SemEval-2021 Task 1: Using Gradient-boosted Regression Tree Ensembles Trained on a Heterogeneous Feature Set for Predicting Lexical Complexity—0
GX at SemEval-2021 Task 2: BERT with Lemma Information for MCL-WiC TaskCode0
Applying Occam’s Razor to Transformer-Based Dependency Parsing: What Works, What Doesn’t, and What is Really Necessary—0
RS\_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction—0
Modeling Text using the Continuous Space Topic Model with Pre-Trained Word Embeddings—0
Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages—0
Tracking Semantic Change in Cognate Sets for English and Romance Languages—0
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