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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 2401–2450 of 4002 papers

TitleStatusHype
The Expressive Power of Word Embeddings—0
The Feasibility of Embedding Based Automatic Evaluation for Single Document Summarization—0
The flow of ideas in word embeddings—0
The German Reference Corpus DeReKo: New Developments -- New Opportunities—0
The Golden Rule as a Heuristic to Measure the Fairness of Texts Using Machine Learning—0
The Impact of Word Embeddings on Neural Dependency Parsing—0
The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition—0
The Kyoto University Cross-Lingual Pronoun Translation System—0
The Language of Place: Semantic Value from Geospatial Context—0
The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation—0
The Limits of Word Level Differential Privacy—0
The LMU Munich Unsupervised Machine Translation Systems—0
The LMU Munich Unsupervised Machine Translation System for WMT19—0
The Making of the Royal Society Corpus—0
Theoretical foundations and limits of word embeddings: what types of meaning can they capture?—0
The performance evaluation of Multi-representation in the Deep Learning models for Relation Extraction Task—0
The Role of Context Types and Dimensionality in Learning Word Embeddings—0
The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations—0
The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018—0
The SAME score: Improved cosine based bias score for word embeddings—0
The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations—0
The Sentimental Value of Chinese Sub-Character Components—0
The strange geometry of skip-gram with negative sampling—0
The TALP--UPC Spanish--English WMT Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System—0
The (too Many) Problems of Analogical Reasoning with Word Vectors—0
The UWNLP system at SemEval-2018 Task 7: Neural Relation Extraction Model with Selectively Incorporated Concept Embeddings—0
The Visualization of Change in Word Meaning over Time using Temporal Word Embeddings—0
The Word Analogy Testing Caveat—0
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples—0
Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions—0
THU\_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model—0
THU\_NGN at SemEval-2019 Task 12: Toponym Detection and Disambiguation on Scientific Papers—0
THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN—0
Time-Aware and Corpus-Specific Entity Relatedness—0
Times Are Changing: Investigating the Pace of Language Change in Diachronic Word Embeddings—0
TIME: Text and Image Mutual-Translation Adversarial Networks—0
Tiny Word Embeddings Using Globally Informed Reconstruction—0
TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations—0
TNE: A Latent Model for Representation Learning on Networks—0
Tohoku at SemEval-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance Detection—0
Token Level Identification of Multiword Expressions Using Contextual Information—0
Token-Level Metaphor Detection using Neural Networks—0
Too Many Claims to Fact-Check: Prioritizing Political Claims Based on Check-Worthiness—0
Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context—0
Topic-aware Contextualized Transformers—0
Topic-aware latent models for representation learning on networks—0
Topic Based Sentiment Analysis Using Deep Learning—0
Topic Modeling Using Distributed Word Embeddings—0
Topic Modeling with Contextualized Word Representation Clusters—0
Topic Modeling with Topological Data Analysis—0
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