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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 3376–3400 of 4002 papers

TitleStatusHype
CogniFNN: A Fuzzy Neural Network Framework for Cognitive Word Embedding Evaluation—0
CogniVal in Action: An Interface for Customizable Cognitive Word Embedding Evaluation—0
CogNLP-Sheffield at CMCL 2021 Shared Task: Blending Cognitively Inspired Features with Transformer-based Language Models for Predicting Eye Tracking Patterns—0
Coherence models in schizophrenia—0
COIN – an Inexpensive and Strong Baseline for Predicting Out of Vocabulary Word Embeddings—0
Co-learning of Word Representations and Morpheme Representations—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media—0
Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings—0
Combining BERT with Static Word Embeddings for Categorizing Social Media—0
Combining Character and Word Embeddings for the Detection of Offensive Language in Arabic—0
Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language—0
Combining Discourse Markers and Cross-lingual Embeddings for Synonym--Antonym Classification—0
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction—0
Combining neural and knowledge-based approaches to Named Entity Recognition in Polish—0
Combining Pretrained High-Resource Embeddings and Subword Representations for Low-Resource Languages—0
Combining Pre-trained Word Embeddings and Linguistic Features for Sequential Metaphor Identification—0
Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels—0
Combining rule-based and embedding-based approaches to normalize textual entities with an ontology—0
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach—0
Combining word embeddings and convolutional neural networks to detect duplicated questions—0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction—0
Coming to its senses: Lessons learned from Approximating Retrofitted BERT representations for Word Sense information—0
Coming to Your Senses: on Controls and Evaluation Sets in Polysemy Research—0
Community Evaluation and Exchange of Word Vectors at wordvectors.org—0
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