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 1221–1230 of 4002 papers

TitleStatusHype
Automatic Creation of Correspondence Table of Meaning Tags from Two Dictionaries in One Language Using Bilingual Word Embedding—0
Data Filtering using Cross-Lingual Word Embeddings—0
Automatic Community Creation for Abstractive Spoken Conversations Summarization—0
Anchor-based Bilingual Word Embeddings for Low-Resource Languages—0
Data-Driven Mitigation of Adversarial Text Perturbation—0
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space—0
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings—0
Automatic classification of speech overlaps: Feature representation and algorithms—0
DAG-based Long Short-Term Memory for Neural Word Segmentation—0
Czech Historical Named Entity Corpus v 1.0—0
Show:102550
← PrevPage 123 of 401Next →

No leaderboard results yet.