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 1781–1790 of 4002 papers

TitleStatusHype
Identifying and interpreting non-aligned human conceptual representations using language modeling—0
Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings—0
Identifying attack and support argumentative relations using deep learning—0
Identifying Cognates in English-Dutch and French-Dutch by means of Orthographic Information and Cross-lingual Word Embeddings—0
Clustering of Russian Adjective-Noun Constructions using Word Embeddings—0
Identity-sensitive Word Embedding through Heterogeneous Networks—0
Exploiting Morphological Regularities in Distributional Word Representations—0
Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes—0
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data—0
Argument from Old Man’s View: Assessing Social Bias in Argumentation—0
Show:102550
← PrevPage 179 of 401Next →

No leaderboard results yet.