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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 1226–1250 of 4002 papers

TitleStatusHype
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space—0
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings—0
Automatic classification of speech overlaps: Feature representation and algorithms—0
DAG-based Long Short-Term Memory for Neural Word Segmentation—0
Czech Historical Named Entity Corpus v 1.0—0
CVBed: Structuring CVs usingWord Embeddings—0
Automatically Linking Lexical Resources with Word Sense Embedding Models—0
An Automatic Learning of an Algerian Dialect Lexicon by using Multilingual Word Embeddings—0
Adversarial Representation Learning for Text-to-Image Matching—0
Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic—0
Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts—0
Automatically Inferring Implicit Properties in Similes—0
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks—0
Cultural Cartography with Word Embeddings—0
Automatically Building a Multilingual Lexicon of False Friends With No Supervision—0
An Automated Method to Enrich Consumer Health Vocabularies Using GloVe Word Embeddings and An Auxiliary Lexical Resource—0
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
Cseq2seq: Cyclic Sequence-to-Sequence Learning—0
Automated Trustworthiness Testing for Machine Learning Classifiers—0
An Attentive Fine-Grained Entity Typing Model with Latent Type Representation—0
CroVeWA: Crosslingual Vector-Based Writing Assistance—0
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers—0
Crossword: Estimating Unknown Embeddings using Cross Attention and Alignment Strategies—0
Automated Single-Label Patent Classification using Ensemble Classifiers—0
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