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 841–850 of 4002 papers

TitleStatusHype
Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation—0
Corporate IT-support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach—0
Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models—0
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop—0
Connecting Supervised and Unsupervised Sentence Embeddings—0
Considerations for the Interpretation of Bias Measures of Word Embeddings—0
Consistency and Variation in Kernel Neural Ranking Model—0
Consistent Structural Relation Learning for Zero-Shot Segmentation—0
Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings—0
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content—0
Show:102550
← PrevPage 85 of 401Next →

No leaderboard results yet.