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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 2151–2200 of 4002 papers

TitleStatusHype
Self-Supervised learning with cross-modal transformers for emotion recognition—0
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction—0
Semantic Annotation Aggregation with Conditional Crowdsourcing Models and Word Embeddings—0
Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning—0
Semantic-aware transformation of short texts using word embeddings: An application in the Food Computing domain—0
Semantic Change and Semantic Stability: Variation is Key—0
Semantic Change in the Language of UK Parliamentary Debates—0
Semantic Clustering and Convolutional Neural Network for Short Text Categorization—0
Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification—0
Semantic Frame Embeddings for Detecting Relations between Software Requirements—0
Semantic Frame Identification with Distributed Word Representations—0
Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering—0
Semantic Frame Induction with Deep Metric Learning—0
Semantic Frame Labeling with Target-based Neural Model—0
Semantic Guided Level-Category Hybrid Prediction Network for Hierarchical Image Classification—0
Semantic Information Extraction for Improved Word Embeddings—0
Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoders—0
Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings—0
Semantic properties of English nominal pluralization: Insights from word embeddings—0
Semantic Relatedness and Taxonomic Word Embeddings—0
Semantic Relatedness for Keyword Disambiguation: Exploiting Different Embeddings—0
Semantic Representation and Inference for NLP—0
Semantic Representations for Domain Adaptation: A Case Study on the Tree Kernel-based Method for Relation Extraction—0
Semantics and Homothetic Clustering of Hafez Poetry—0
Semantics-Driven Recognition of Collocations Using Word Embeddings—0
Semantic Similarity of Arabic Sentences with Word Embeddings—0
Semantic Term "Blurring" and Stochastic "Barcoding" for Improved Unsupervised Text Classification—0
Semantic Word Clusters Using Signed Spectral Clustering—0
SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity—0
Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings—0
Semi-automated extraction of research topics and trends from NCI funding in radiological sciences from 2000-2020—0
Semi-automatic WordNet Linking using Word Embeddings—0
SEMIE: SEMantically Infused Embeddings with Enhanced Interpretability for Domain-specific Small Corpus—0
Semi-Supervised Bootstrapping of Relationship Extractors with Distributional Semantics—0
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding—0
Semi-supervised Dependency Parsing using Bilexical Contextual Features from Auto-Parsed Data—0
Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings—0
Semi-supervised Learning with Multi-Domain Sentiment Word Embeddings—0
Meta-Embedding as Auxiliary Task Regularization—0
SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages—0
SenSALDO: Creating a Sentiment Lexicon for Swedish—0
Sense Embeddings are also Biased -- Evaluating Social Biases in Static and Contextualised Sense Embeddings—0
Sense Embeddings are also Biased – Evaluating Social Biases in Static and Contextualised Sense Embeddings—0
SenseFitting: Sense Level Semantic Specialization of Word Embeddings for Word Sense Disambiguation—0
SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis—0
SensEmbed: Learning Sense Embeddings for Word and Relational Similarity—0
Sense representations for Portuguese: experiments with sense embeddings and deep neural language models—0
Sentence Alignment Methods for Improving Text Simplification Systems—0
Sentence Complexity in Context—0
Sentence Compression by Deletion with LSTMs—0
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