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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

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Showing 2901–2925 of 4002 papers

TitleStatusHype
A novel methodology on distributed representations of proteins using their interacting ligands—0
Early Detection of Social Media Hoaxes at Scale—0
Unsupervised Open Relation ExtractionCode0
A Survey of Word Embeddings Evaluation MethodsCode0
A Resource-Light Method for Cross-Lingual Semantic Textual SimilarityCode0
Contextual and Position-Aware Factorization Machines for Sentiment Classification—0
WEAC: Word embeddings for anomaly classification from event logs—0
Biomedical Question Answering via Weighted Neural Network Passage Retrieval—0
ReferenceNet: a semantic-pragmatic network for capturing reference relations.—0
Evaluating the Stability of Embedding-based Word Similarities—0
Wordnet-based Evaluation of Large Distributional Models for Polish—0
Recognition of Hyponymy and Meronymy Relations in Word Embeddings for Polish—0
Multilingual Wordnet sense Ranking using nearest context—0
Lifelong Word Embedding via Meta-Learning—0
Learning Covariate-Specific Embeddings with Tensor Decompositions—0
Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases—0
Sound Analogies with Phoneme Embeddings—0
On the Use of Word Embeddings Alone to Represent Natural Language Sequences—0
Initial Experiments in Data-Driven Morphological Analysis for Finnish—0
An Iterative Approach for Unsupervised Most Frequent Sense Detection using WordNet and Word Embeddings—0
Learning Representations Specialized in Spatial Knowledge: Leveraging Language and VisionCode0
A Simple Fully Connected Network for Composing Word Embeddings from Characters—0
Unsupervised Learning of Entailment-Vector Word Embeddings—0
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora—0
One-shot and few-shot learning of word embeddings—0
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