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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 851–875 of 4002 papers

TitleStatusHype
Using Word Embeddings to Analyze Teacher Evaluations: An Application to a Filipino Education Non-Profit Organization—0
Paradigm Clustering with Weighted Edit Distance—0
Exploring Input Representation Granularity for Generating Questions Satisfying Question-Answer Congruence—0
Linguistic change and historical periodization of Old Literary Finnish—0
Tracking Semantic Change in Cognate Sets for English and Romance Languages—0
Multilingual Dependency Parsing for Low-Resource African Languages: Case Studies on Bambara, Wolof, and Yoruba—0
“Are you calling for the vaporizer you ordered?” Combining Search and Prediction to Identify Orders in Contact Centers—0
Applying Occam’s Razor to Transformer-Based Dependency Parsing: What Works, What Doesn’t, and What is Really Necessary—0
SINAI at SemEval-2021 Task 5: Combining Embeddings in a BiLSTM-CRF model for Toxic Spans Detection—0
RAW-C: Relatedness of Ambiguous Words in Context (A New Lexical Resource for English)—0
Rethinking Stealthiness of Backdoor Attack against NLP ModelsCode1
Measure and Evaluation of Semantic Divergence across Two Languages—0
Modeling Text using the Continuous Space Topic Model with Pre-Trained Word Embeddings—0
Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring—0
UMUTeam at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Linguistic Features and Word EmbeddingsCode0
GX at SemEval-2021 Task 2: BERT with Lemma Information for MCL-WiC TaskCode0
Learning Embeddings for Rare Words Leveraging Internet Search Engine and Spatial Location Relationships—0
Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains—0
PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP)—0
JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction—0
CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way—0
Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages—0
RS\_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction—0
GlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word Embeddings—0
Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings—0
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