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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 2601–2650 of 4002 papers

TitleStatusHype
USAAR-WLV: Hypernym Generation with Deep Neural Nets—0
Usability and Accessibility of Bantu Language Dictionaries in the Digital Age: Mobile Access in an Open Environment—0
Use Case: Romanian Language Resources in the LOD Paradigm—0
Use Generalized Representations, But Do Not Forget Surface Features—0
Use of unsupervised word classes for entity recognition: Application to the detection of disorders in clinical reports—0
User recommendation system based on MIND dataset—0
USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings—0
USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection—0
Using Adversarial Debiasing to Remove Bias from Word Embeddings—0
Controllable Speaking Styles Using a Large Language Model—0
Using BERT Embeddings to Model Word Importance in Conversational Transcripts for Deaf and Hard of Hearing Users—0
Using bilingual word-embeddings for multilingual collocation extraction—0
Using Centroids of Word Embeddings and Word Mover's Distance for Biomedical Document Retrieval in Question Answering—0
Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations—0
Using contextual and cross-lingual word embeddings to improve variety in template-based NLG for automated journalism—0
Using Convolution Neural Network with BERT for Stance Detection in Vietnamese—0
How Can BERT Help Lexical Semantics Tasks?—0
Using Embedding Masks for Word Categorization—0
Using Gaze Data to Predict Multiword Expressions—0
Using k-way Co-occurrences for Learning Word Embeddings—0
Using Large Pre-Trained Language Model to Assist FDA in Premarket Medical Device—0
Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation—0
Using meaning instead of words to track topics—0
Using Mined Coreference Chains as a Resource for a Semantic Task—0
Using Neural Word Embeddings in the Analysis of the Clinical Semantic Verbal Fluency Task—0
Using pseudo-senses for improving the extraction of synonyms from word embeddings—0
Using reading behavior to predict grammatical functions—0
Using Sentences as Semantic Representations in Large Scale Zero-Shot Learning—0
Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate—0
Using virtual edges to extract keywords from texts modeled as complex networks—0
Using Word Embedding for Cross-Language Plagiarism Detection—0
UsingWord Embedding for Cross-Language Plagiarism Detection—0
Using Word Embeddings for Automatic Query Expansion—0
Using Word Embeddings for Bilingual Unsupervised WSD—0
Using Word Embeddings for Improving Statistical Machine Translation of Phrasal Verbs—0
Using Word Embeddings for Italian Crime News Categorization—0
UsingWord Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval—0
Using Word Embeddings for Unsupervised Acronym Disambiguation—0
Using Word Embeddings for Visual Data Exploration with Ontodia and Wikidata—0
Using Word Embeddings in Twitter Election Classification—0
Using Word Embeddings to Analyze Protests News—0
Using Word Embeddings to Analyze Teacher Evaluations: An Application to a Filipino Education Non-Profit Organization—0
Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks—0
Using word embeddings to improve the discriminability of co-occurrence text networks—0
Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News—0
Using Word Embeddings to Translate Named Entities—0
Using Word Embeddings to Uncover Discourses—0
UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation—0
Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research—0
Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models—0
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