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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 2851–2875 of 4002 papers

TitleStatusHype
Measuring and Modeling Language Change—0
Measuring a Texts Fairness Dimensions Using Machine Learning Based on Social Psychological Factors—0
Measuring Biases of Word Embeddings: What Similarity Measures and Descriptive Statistics to Use?—0
Measuring Diachronic Evolution of Evaluative Adjectives with Word Embeddings: the Case for English, Norwegian, and Russian—0
Measuring Issue Ownership using Word Embeddings—0
Measuring Similarity by Linguistic Features rather than Frequency—0
Measuring Social Bias in Knowledge Graph Embeddings—0
Measuring Topic Coherence through Optimal Word Buckets—0
Medical Word Embeddings for Spanish: Development and Evaluation—0
Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings—0
Membership Inference on Word Embedding and Beyond—0
Merging Verb Senses of Hindi WordNet using Word Embeddings—0
Meta-Embeddings for Natural Language Inference and Semantic Similarity tasks—0
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German—0
Metaphor Detection in a Poetry Corpus—0
Metaphor Detection Using Contextual Word Embeddings From Transformers—0
Metaphor Detection using Deep Contextualized Word Embeddings—0
Methodical Evaluation of Arabic Word Embeddings—0
Methods for Numeracy-Preserving Word Embeddings—0
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification—0
microNER: A Micro-Service for German Named Entity Recognition based on BiLSTM-CRF—0
MineriaUNAM at SemEval-2020 Task 3: Predicting Contextual WordSimilarity Using a Centroid Based Approach and Word Embeddings—0
Minimally-Supervised Relation Induction from Pre-trained Language Model—0
Minimally-Supervised Relation Induction from Pre-trained Language Model—0
Mining Semantic Relations from Comparable Corpora through Intersections of Word Embeddings—0
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