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 1521–1530 of 4002 papers

TitleStatusHype
A Classification-Based Approach to Cognate Detection Combining Orthographic and Semantic Similarity Information—0
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach—0
A Simple Fully Connected Network for Composing Word Embeddings from Characters—0
A Simple Disaster-Related Knowledge Base for Intelligent Agents—0
A mostly unlexicalized model for recognizing textual entailment—0
Combining rule-based and embedding-based approaches to normalize textual entities with an ontology—0
Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels—0
Combining Pre-trained Word Embeddings and Linguistic Features for Sequential Metaphor Identification—0
A Simple and Efficient Probabilistic Language model for Code-Mixed Text—0
A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition—0
Show:102550
← PrevPage 153 of 401Next →

No leaderboard results yet.