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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 1401–1450 of 4002 papers

TitleStatusHype
Latte-Mix: Measuring Sentence Semantic Similarity with Latent Categorical Mixtures—0
LT3 at SemEval-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of Hinglish Social Media Text—0
What makes multilingual BERT multilingual?—0
Image Captioning with Visual Object Representations Grounded in the Textual Modality—0
Generating Fact Checking Summaries for Web ClaimsCode0
Multi-Adversarial Learning for Cross-Lingual Word Embeddings—0
A Self-supervised Representation Learning of Sentence Structure for Authorship AttributionCode0
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?—0
BRUMS at SemEval-2020 Task 3: Contextualised Embeddings for Predicting the (Graded) Effect of Context in Word SimilarityCode0
Legal Document Classification: An Application to Law Area Prediction of Petitions to Public Prosecution Service—0
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
Analogies minus analogy test: measuring regularities in word embeddingsCode0
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
Metaphor Interpretation Using Word Embeddings—0
Using Sentences as Semantic Representations in Large Scale Zero-Shot Learning—0
Intrinsic Probing through Dimension SelectionCode0
Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base PairsCode0
On the Effects of Knowledge-Augmented Data in Word Embeddings—0
PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings—0
LEAPME: Learning-based Property Matching with Embeddings—0
Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles—0
Enriching Word Embeddings with Temporal and Spatial InformationCode0
Syntax Representation in Word Embeddings and Neural Networks -- A Survey—0
Neural Machine Translation from Historical Japanese to Contemporary Japanese Using Diachronically Domain-Adapted Word Embeddings—0
Composing Word Vectors for Japanese Compound Words Using Bilingual Word Embeddings—0
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks—0
Interactive Re-Fitting as a Technique for Improving Word Embeddings—0
Multiple Word Embeddings for Increased Diversity of RepresentationCode0
Development of Word Embeddings for Uzbek Language—0
Leader: Prefixing a Length for Faster Word Vector SerializationCode0
Metaphor Detection using Deep Contextualized Word Embeddings—0
CogniFNN: A Fuzzy Neural Network Framework for Cognitive Word Embedding Evaluation—0
Visual-Semantic Embedding Model Informed by Structured Knowledge—0
Exploring the Linear Subspace Hypothesis in Gender Bias MitigationCode0
Word class flexibility: A deep contextualized approachCode0
An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment AnalysisCode0
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA—0
More Embeddings, Better Sequence Labelers?—0
Unsupervised Summarization by Jointly Extracting Sentences and Keywords—0
Lessons Learned from Applying off-the-shelf BERT: There is no Silver Bullet—0
ReviewViz: Assisting Developers Perform Empirical Study on Energy Consumption Related Reviews for Mobile ApplicationsCode0
Coreference Resolution System for Indonesian Text with Mention Pair Method and Singleton Exclusion using Convolutional Neural NetworkCode0
Investigating Gender Bias in BERT—0
UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts using Transformers and Multi-Task Learning—0
Bio-inspired Structure Identification in Language Embeddings—0
Japanese Word Readability Assessment using Word Embeddings—0
VinAI at ChEMU 2020: An accurate system for named entity recognition in chemical reactions from patents—0
Discovering Bilingual Lexicons in Polyglot Word Embeddings—0
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