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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 1951–1975 of 4002 papers

TitleStatusHype
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings—0
Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts—0
BERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation—0
Investigating Gender Bias in BERT—0
Investigating Graph Structure Information for Entity Alignment with Dangling Cases—0
Investigating Language Universal and Specific Properties in Word Embeddings—0
Investigating neural architectures for short answer scoring—0
Investigating Sub-Word Embedding Strategies for the Morphologically Rich and Free Phrase-Order Hungarian—0
Automatic classification of speech overlaps: Feature representation and algorithms—0
Investigating the Effectiveness of Representations Based on Pretrained Transformer-based Language Models in Active Learning for Labelling Text Datasets—0
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings—0
Investigating the Stability of Concrete Nouns in Word Embeddings—0
A Neural Virtual Anchor Synthesizer based on Seq2Seq and GAN Models—0
IRISA at SMM4H 2018: Neural Network and Bagging for Tweet Classification—0
Data-Driven Mitigation of Adversarial Text Perturbation—0
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics—0
A Neural Model for Compositional Word Embeddings and Sentence Processing—0
Dependency Parsing for Urdu: Resources, Conversions and Learning—0
Isomorphic Cross-lingual Embeddings for Low-Resource Languages—0
Dependency Link Embeddings: Continuous Representations of Syntactic Substructures—0
BERT-based Ensembles for Modeling Disclosure and Support in Conversational Social Media Text—0
Is there Gender bias and stereotype in Portuguese Word Embeddings?—0
Is ``Universal Syntax'' Universally Useful for Learning Distributed Word Representations?—0
ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings—0
Dependency-Based Word Embeddings—0
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