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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 576–600 of 4002 papers

TitleStatusHype
Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge—0
Multimodal Hate Speech Detection from Bengali Memes and TextsCode0
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition ModelingCode0
Word Embeddings Are Capable of Capturing Rhythmic Similarity of WordsCode0
Towards Better Chinese-centric Neural Machine Translation for Low-resource LanguagesCode1
Automatic Extraction of Nested Entities in Clinical Referrals in SpanishCode0
"This is my unicorn, Fluffy": Personalizing frozen vision-language representationsCode1
A Part-of-Speech Tagger for YiddishCode0
A bilingual approach to specialised adjectives through word embeddings in the karstology domain—0
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings—0
Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to ModelingCode0
Semantic properties of English nominal pluralization: Insights from word embeddings—0
An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese CodexCode0
Comparing in context: Improving cosine similarity measures with a metric tensor—0
The SAME score: Improved cosine based bias score for word embeddings—0
Isomorphic Cross-lingual Embeddings for Low-Resource Languages—0
Probabilistic Embeddings with Laplacian Graph Priors—0
Gender and Racial Stereotype Detection in Legal Opinion Word Embeddings—0
Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset—0
Building Robust Spoken Language Understanding by Cross Attention between Phoneme Sequence and ASR Hypothesis—0
Compression of Generative Pre-trained Language Models via Quantization—0
VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot LearningCode1
From meaning to perception -- exploring the space between word and odor perception embeddings—0
Improving Word Translation via Two-Stage Contrastive LearningCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little CostCode1
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