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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 1801–1825 of 4002 papers

TitleStatusHype
Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers—0
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings—0
Clustering of Russian Adjective-Noun Constructions using Word Embeddings—0
Exploiting Morphological Regularities in Distributional Word Representations—0
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data—0
Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions—0
Improved Answer Selection with Pre-Trained Word Embeddings—0
Argument from Old Man’s View: Assessing Social Bias in Argumentation—0
Improved CCG Parsing with Semi-supervised Supertagging—0
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data—0
Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence—0
A Margin-based Loss with Synthetic Negative Samples for Continuous-output Machine Translation—0
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization—0
Clustering is Efficient for Approximate Maximum Inner Product Search—0
Exploiting Class Labels to Boost Performance on Embedding-based Text Classification—0
Improved Word Embeddings with Implicit Structure Information—0
Clustering Comparable Corpora of Russian and Ukrainian Academic Texts: Word Embeddings and Semantic Fingerprints—0
Explaining Word Embeddings via Disentangled Representation—0
Explaining the Trump Gap in Social Distancing Using COVID Discourse—0
Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis—0
Improving Aspect-Level Sentiment Analysis with Aspect Extraction—0
Improving average ranking precision in user searches for biomedical research datasets—0
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning—0
CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way—0
Argumentative Topology: Finding Loop(holes) in Logic—0
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