SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1001–1025 of 4002 papers

TitleStatusHype
Deriving Disinformation Insights from Geolocalized Twitter CalloutsCode0
Evolution of emotion semanticsCode0
Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings—0
UMUTeam at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Linguistic Features and Word EmbeddingsCode0
MXX@FinSim3 - An LSTM–based approach with custom word embeddings for hypernym detection in financial texts—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
Paradigm Clustering with Weighted Edit Distance—0
Measure and Evaluation of Semantic Divergence across Two Languages—0
Implicit Phenomena in Short-answer Scoring Data—0
Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains—0
Using Word Embeddings to Analyze Teacher Evaluations: An Application to a Filipino Education Non-Profit Organization—0
RAW-C: Relatedness of Ambiguous Words in Context (A New Lexical Resource for English)—0
RS\_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction—0
FKIE_itf_2021 at CASE 2021 Task 1: Using Small Densely Fully Connected Neural Nets for Event Detection and Clustering—0
SINAI at SemEval-2021 Task 5: Combining Embeddings in a BiLSTM-CRF model for Toxic Spans Detection—0
Exploring Input Representation Granularity for Generating Questions Satisfying Question-Answer Congruence—0
Lifelong Learning of Topics and Domain-Specific Word EmbeddingsCode0
GlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word Embeddings—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
GX at SemEval-2021 Task 2: BERT with Lemma Information for MCL-WiC TaskCode0
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding—0
Linguistic change and historical periodization of Old Literary Finnish—0
Applying Occam’s Razor to Transformer-Based Dependency Parsing: What Works, What Doesn’t, and What is Really Necessary—0
Modeling Text using the Continuous Space Topic Model with Pre-Trained Word Embeddings—0
Show:102550
← PrevPage 41 of 161Next →

No leaderboard results yet.