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 391–400 of 4002 papers

TitleStatusHype
A Simple Language Model based on PMI Matrix Approximations—0
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings—0
A Simple Word Embedding Model for Lexical Substitution—0
Ask the GRU: Multi-Task Learning for Deep Text Recommendations—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
Article citation study: Context enhanced citation sentiment detection—0
Aspect-based Opinion Summarization with Convolutional Neural Networks—0
Aspect-Based Sentiment Analysis Using Bitmask Bidirectional Long Short Term Memory Networks—0
A multilabel approach to morphosyntactic probing—0
A Minimalist Approach to Shallow Discourse Parsing and Implicit Relation Recognition—0
Show:102550
← PrevPage 40 of 401Next →

No leaderboard results yet.