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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 451–475 of 4002 papers

TitleStatusHype
Gender bias in (non)-contextual clinical word embeddings for stereotypical medical categories—0
Massively Multilingual Lexical Specialization of Multilingual Transformers—0
Knowing Where and What: Unified Word Block Pretraining for Document UnderstandingCode0
SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation—0
Exploring Wasserstein Distance across Concept Embeddings for Ontology Matching—0
Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition—0
A Context-Sensitive Word Embedding Approach for The Detection of Troll Tweets—0
A methodology to characterize bias and harmful stereotypes in natural language processing in Latin AmericaCode0
Myers-Briggs personality classification from social media text using pre-trained language models—0
TurkishDelightNLP: A Neural Turkish NLP ToolkitCode0
A Comparative Study on Word Embeddings and Social NLP TasksCode0
Minimally-Supervised Relation Induction from Pre-trained Language Model—0
Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary?—0
Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language ProcessingCode0
Subword-based Cross-lingual Transfer of Embeddings from Hindi to Marathi and Nepali—0
Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings—0
Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition—0
Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings—0
BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencodersCode0
Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding—0
Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and DefinitionsCode0
Indigenous Language Revitalization and the Dilemma of Gender Bias—0
TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations—0
LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse DictionaryCode1
Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource LanguageCode0
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