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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 701–725 of 4002 papers

TitleStatusHype
Evolving Large Text Corpora: Four Versions of the Icelandic Gigaword Corpus—0
Enhancing Deep Learning with Embedded Features for Arabic Named Entity RecognitionCode0
Accurate Dependency Parsing and Tagging of Latin—0
Cross-lingual Linking of Automatically Constructed Frames and FrameNet—0
XLNET-GRU Sentiment Regression Model for Cryptocurrency News in English and Malay—0
Query Obfuscation by Semantic Decomposition—0
Count-Based and Predictive Language Models for Exploring DeReKo—0
Casteism in India, but Not Racism - a Study of Bias in Word Embeddings of Indian Languages—0
HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French—0
A Hmong Corpus with Elaborate Expression Annotations—0
Use Case: Romanian Language Resources in the LOD Paradigm—0
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High GermanCode0
Dialects Identification of Armenian Language—0
A General Framework for Detecting Metaphorical Collocations—0
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic—0
Leveraging a Bilingual Dictionary to Learn Wolastoqey Word Representations—0
BERTrade: Using Contextual Embeddings to Parse Old French—0
Sentence Selection Strategies for Distilling Word Embeddings from BERT—0
Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional NetworksCode0
Toward Understanding Bias Correlations for Mitigation in NLP—0
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word AlignmentCode0
Do Deep Learning Models and News Headlines Outperform Conventional Prediction Techniques on Forex Data?—0
Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic—0
What company do words keep? Revisiting the distributional semantics of J.R. Firth & Zellig Harris—0
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