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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 1601–1650 of 4002 papers

TitleStatusHype
Frequency-based Distortions in Contextualized Word Embeddings—0
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media—0
From communities to interpretable network and word embedding: an unified approach—0
A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment—0
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings—0
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts—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
Extracting Temporal and Causal Relations between Events—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
Extracting Tags from Large Raw Texts Using End-to-End Memory Networks—0
Extracting Social Networks from Literary Text with Word Embedding Tools—0
A Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task—0
A Model of Zero-Shot Learning of Spoken Language Understanding—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
Extracting Possessions from Social Media: Images Complement Language—0
Extracting domain-specific terms using contextual word embeddings—0
Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning—0
Extending Text Informativeness Measures to Passage Interestingness Evaluation (Language Model vs. Word Embedding)—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
Co-learning of Word Representations and Morpheme Representations—0
Fusing Vector Space Models for Domain-Specific Applications—0
Fusion approaches for emotion recognition from speech using acoustic and text-based features—0
ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation—0
A semi-supervised model for Persian rumor verification based on content information—0
Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications—0
Extending and Improving Wordnet via Unsupervised Word Embeddings—0
COIN – an Inexpensive and Strong Baseline for Predicting Out of Vocabulary Word Embeddings—0
Expressivity-aware Music Performance Retrieval using Mid-level Perceptual Features and Emotion Word Embeddings—0
Expressing Objects just like Words: Recurrent Visual Embedding for Image-Text Matching—0
Coherence models in schizophrenia—0
Gender Bias Hidden Behind Chinese Word Embeddings: The Case of Chinese Adjectives—0
Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification—0
Addressing Biases in the Texts using an End-to-End Pipeline Approach—0
Exponential Family Word Embeddings: An Iterative Approach for Learning Word Vectors—0
Gender Prediction for Chinese Social Media Data—0
Gender Roles from Word Embeddings in a Century of Children’s Books—0
Exponential Family Embeddings—0
CogNLP-Sheffield at CMCL 2021 Shared Task: Blending Cognitively Inspired Features with Transformer-based Language Models for Predicting Eye Tracking Patterns—0
Exploring Word Sense Disambiguation Abilities of Neural Machine Translation Systems (Non-archival Extended Abstract)—0
Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization—0
CogniVal in Action: An Interface for Customizable Cognitive Word Embedding Evaluation—0
Generating Varied Training Corpora in Runyankore Using a Combined Semantic and Syntactic, Pattern-Grammar-based Approach—0
Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors—0
Exploring Word Embedding for Drug Name Recognition—0
Exploring Wasserstein Distance across Concept Embeddings for Ontology Matching—0
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