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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 3401–3450 of 4002 papers

TitleStatusHype
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word VectorsCode0
Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914Code0
Beyond Word2Vec: Embedding Words and Phrases in Same Vector SpaceCode0
Collocation Classification with Unsupervised Relation VectorsCode0
Nonparametric Spherical Topic Modeling with Word EmbeddingsCode0
Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense LabelingCode0
Self-Governing Neural Networks for On-Device Short Text ClassificationCode0
Do Word Embeddings Capture Spelling Variation?Code0
GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summariesCode0
Beyond Weight Tying: Learning Joint Input-Output Embeddings for Neural Machine TranslationCode0
Language Models with Pre-Trained (GloVe) Word EmbeddingsCode0
Tracing cultural diachronic semantic shifts in Russian using word embeddings: test sets and baselinesCode0
Language with Vision: a Study on Grounded Word and Sentence EmbeddingsCode0
A Morphology-Based Representation Model for LSTM-Based Dependency Parsing of Agglutinative LanguagesCode0
Collapsed Language Models Promote FairnessCode0
A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference CorpusCode0
\#TagSpace: Semantic Embeddings from HashtagsCode0
DUKweb: Diachronic word representations from the UK Web Archive corpusCode0
Dynamic Bernoulli Embeddings for Language EvolutionCode0
CogniVal: A Framework for Cognitive Word Embedding EvaluationCode0
Dynamic Meta-Embeddings for Improved Sentence RepresentationsCode0
Dynamic Word EmbeddingsCode0
Dynamic Word Embeddings for Evolving Semantic DiscoveryCode0
Self-Taught Convolutional Neural Networks for Short Text ClusteringCode0
E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book SystemsCode0
No Training Required: Exploring Random Encoders for Sentence ClassificationCode0
Tracing the Development of the Virtual Particle Concept Using Semantic Change DetectionCode0
Attentive Mimicking: Better Word Embeddings by Attending to Informative ContextsCode0
Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of TwitterCode0
What Are Large Language Models Mapping to in the Brain? A Case Against Over-Reliance on Brain ScoresCode0
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine TranslationCode0
Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation LearningCode0
Gender-preserving Debiasing for Pre-trained Word EmbeddingsCode0
Layer or Representation Space: What makes BERT-based Evaluation Metrics Robust?Code0
Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and DefinitionsCode0
Tracking Legislators’ Expressed Policy Agendas in Real TimeCode0
NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion ClassificationCode0
Effective Dimensionality Reduction for Word EmbeddingsCode0
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer LearningCode0
Leader: Prefixing a Length for Faster Word Vector SerializationCode0
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing SignalsCode0
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware AttentionCode0
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNsCode0
Unsupervised Multilingual Word EmbeddingsCode0
Tracking Semantic Shifts in German Court Decisions with Diachronic Word EmbeddingsCode0
Acoustic span embeddings for multilingual query-by-example searchCode0
Efficacy of BERT embeddings on predicting disaster from Twitter dataCode0
CMCE at SemEval-2020 Task 1: Clustering on Manifolds of Contextualized Embeddings to Detect Historical Meaning ShiftsCode0
Efficient, Compositional, Order-sensitive n-gram EmbeddingsCode0
A methodology to characterize bias and harmful stereotypes in natural language processing in Latin AmericaCode0
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