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 2526–2550 of 4002 papers

TitleStatusHype
Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs—0
Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping—0
Interactive Re-Fitting as a Technique for Improving Word Embeddings—0
Interdependencies of Gender and Race in Contextualized Word Embeddings—0
Interpretable Adversarial Training for Text—0
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts—0
Interpretable embeddings to understand computing careers—0
Interpretable Neural Embeddings with Sparse Self-Representation—0
Interpretable Word Embedding Contextualization—0
Interpretable Word Embeddings via Informative Priors—0
Interpreting Emoji with Emoji—0
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings—0
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models—0
Inter-Sense: An Investigation of Sensory Blending in Fiction—0
Inter-Weighted Alignment Network for Sentence Pair Modeling—0
Intrinsic analysis for dual word embedding space models—0
Intrinsic Bias Metrics Do Not Correlate with Application Bias—0
Intrinsic Evaluations of Word Embeddings: What Can We Do Better?—0
Intrinsic Image Captioning Evaluation—0
Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili—0
Invariance and identifiability issues for word embeddings—0
InvBERT: Reconstructing Text from Contextualized Word Embeddings by inverting the BERT pipeline—0
Investigating and Mitigating Stereotype-aware Unfairness in LLM-based Recommendations—0
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings—0
Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts—0
Show:102550
← PrevPage 102 of 161Next →

No leaderboard results yet.