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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 651–700 of 4002 papers

TitleStatusHype
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
A Context-Sensitive Word Embedding Approach for The Detection of Troll Tweets—0
Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition—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
Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition—0
When Polysemy Matters: Modeling Semantic Categorization with Word Embeddings—0
Interpreting Emoji with Emoji—0
Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding—0
A Comparative Study on Word Embeddings and Social NLP TasksCode0
TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations—0
Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and DefinitionsCode0
Leveraging Three Types of Embeddings from Masked Language Models in Idiom Token Classification—0
Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language ProcessingCode0
Minimally-Supervised Relation Induction from Pre-trained Language Model—0
Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings—0
Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary?—0
Team Stanford ACMLab at SemEval 2022 Task 4: Textual Analysis of PCL Using Contextual Word Embeddings—0
Subword-based Cross-lingual Transfer of Embeddings from Hindi to Marathi and Nepali—0
LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover’s Distance—0
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting—0
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
BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencodersCode0
TurkishDelightNLP: A Neural Turkish NLP ToolkitCode0
Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings—0
Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation—0
Indigenous Language Revitalization and the Dilemma of Gender Bias—0
An Empirical Study on the Fairness of Pre-trained Word Embeddings—0
How direct is the link between words and images?—0
Using BERT Embeddings to Model Word Importance in Conversational Transcripts for Deaf and Hard of Hearing Users—0
Language with Vision: a Study on Grounded Word and Sentence EmbeddingsCode0
niksss at HinglishEval: Language-agnostic BERT-based Contextual Embeddings with Catboost for Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish TextCode0
JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text—0
TransDrift: Modeling Word-Embedding Drift using Transformer—0
HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence—0
Contextualization and Generalization in Entity and Relation Extraction—0
Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings—0
1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Task—0
Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics—0
Comparing Performance of Different Linguistically-Backed Word Embeddings for Cyberbullying Detection—0
Measuring Gender Bias in Word Embeddings of Gendered Languages Requires Disentangling Grammatical Gender SignalsCode0
Tracking Changes in ESG Representation: Initial Investigations in UK Annual Reports—0
Automating Idea Unit Segmentation and Alignment for Assessing Reading Comprehension via Summary Protocol Analysis—0
HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French—0
Sentence Selection Strategies for Distilling Word Embeddings from BERT—0
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High GermanCode0
Dialects Identification of Armenian Language—0
Query Obfuscation by Semantic Decomposition—0
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