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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 1601–1625 of 4002 papers

TitleStatusHype
Frequency-based Distortions in Contextualized Word Embeddings—0
Comparing Performance of Different Linguistically-Backed Word Embeddings for Cyberbullying Detection—0
From cart to truck: meaning shift through words in English in the last two centuries—0
From communities to interpretable network and word embedding: an unified approach—0
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation—0
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings—0
Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine—0
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?—0
From meaning to perception -- exploring the space between word and odor perception embeddings—0
Comparing the Performance of Feature Representations for the Categorization of the Easy-to-Read Variety vs Standard Language—0
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings—0
From Raw Text to Universal Dependencies - Look, No Tags!—0
Comparison between Voting Classifier and Deep Learning methods for Arabic Dialect Identification—0
Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media—0
Comparison of Paragram and GloVe Results for Similarity Benchmarks—0
Affordance Extraction and Inference based on Semantic Role Labeling—0
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models—0
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers—0
From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers—0
Comparison of Short-Text Sentiment Analysis Methods for Croatian—0
Detecting and Mitigating Indirect Stereotypes in Word Embeddings—0
Fully Delexicalized Contexts for Syntax-Based Word Embeddings—0
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts—0
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter—0
Des repr\'esentations continues de mots pour l'analyse d'opinions en arabe: une \'etude qualitative (Word embeddings for Arabic sentiment analysis : a qualitative study)—0
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