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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 1301–1325 of 4002 papers

TitleStatusHype
Multi-Adversarial Learning for Cross-Lingual Word Embeddings—0
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label GraphsCode1
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?—0
A Self-supervised Representation Learning of Sentence Structure for Authorship AttributionCode0
Legal Document Classification: An Application to Law Area Prediction of Petitions to Public Prosecution Service—0
BRUMS at SemEval-2020 Task 3: Contextualised Embeddings for Predicting the (Graded) Effect of Context in Word SimilarityCode0
Multilingual Offensive Language Identification with Cross-lingual EmbeddingsCode0
gundapusunil at SemEval-2020 Task 9: Syntactic Semantic LSTM Architecture for SENTIment Analysis of Code-MIXed Data—0
comp-syn: Perceptually Grounded Word Embeddings with ColorCode1
MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching—0
VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition ModelingCode0
Analogies minus analogy test: measuring regularities in word embeddingsCode0
Metaphor Interpretation Using Word Embeddings—0
Using Sentences as Semantic Representations in Large Scale Zero-Shot Learning—0
Intrinsic Probing through Dimension SelectionCode0
Compositional Demographic Word EmbeddingsCode1
Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base PairsCode0
Embedding Words in Non-Vector Space with Unsupervised Graph LearningCode1
LEAPME: Learning-based Property Matching with Embeddings—0
PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings—0
On the Effects of Knowledge-Augmented Data in Word Embeddings—0
Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles—0
Syntax Representation in Word Embeddings and Neural Networks -- A Survey—0
Enriching Word Embeddings with Temporal and Spatial InformationCode0
Composing Word Vectors for Japanese Compound Words Using Bilingual Word Embeddings—0
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