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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 601–625 of 4002 papers

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An Open-World Extension to Knowledge Graph Completion ModelsCode0
DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali LanguageCode0
AnlamVer: Semantic Model Evaluation Dataset for Turkish - Word Similarity and RelatednessCode0
Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal GuidanceCode0
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding SpacesCode0
Data-driven models and computational tools for neurolinguistics: a language technology perspectiveCode0
Bilingual Lexicon Induction through Unsupervised Machine TranslationCode0
BioSentVec: creating sentence embeddings for biomedical textsCode0
BI-RADS BERT & Using Section Segmentation to Understand Radiology ReportsCode0
Crossmodal ASR Error Correction with Discrete Speech UnitsCode0
Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptationCode0
DefSent+: Improving sentence embeddings of language models by projecting definition sentences into a quasi-isotropic or isotropic vector space of unlimited dictionary entriesCode0
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word EmbeddingsCode0
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition ModelingCode0
Contextually Propagated Term Weights for Document RepresentationCode0
Deriving Disinformation Insights from Geolocalized Twitter CalloutsCode0
CS-Embed at SemEval-2020 Task 9: The effectiveness of code-switched word embeddings for sentiment analysisCode0
Detecting Anxiety through RedditCode0
BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencodersCode0
A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical AttentionCode0
Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched DataCode0
An Unsupervised Neural Attention Model for Aspect ExtractionCode0
Aggressive Language Identification Using Word Embeddings and Sentiment FeaturesCode0
Dict2vec : Learning Word Embeddings using Lexical DictionariesCode0
Bilingual Learning of Multi-sense Embeddings with Discrete AutoencodersCode0
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