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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 1826–1850 of 4002 papers

TitleStatusHype
CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way—0
Argumentative Topology: Finding Loop(holes) in Logic—0
A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic—0
Improving Disfluency Detection by Self-Training a Self-Attentive Model—0
Improving Distributional Similarity with Lessons Learned from Word Embeddings—0
Adaptive Compression of Word Embeddings—0
Improving Entity Linking by Modeling Latent Entity Type Information—0
A Challenge Set and Methods for Noun-Verb Ambiguity—0
Improving evaluation and optimization of MT systems against MEANT—0
Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings—0
Improving Interpretability of Word Embeddings by Generating Definition and Usage—0
Explainable Identification of Hate Speech towards Islam using Graph Neural Networks—0
Explainability of Text Processing and Retrieval Methods: A Critical Survey—0
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations—0
Experiments on a Guarani Corpus of News and Social Media—0
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables—0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
Improving neural tagging with lexical information—0
Improving Opinion-Target Extraction with Character-Level Word Embeddings—0
Improving Optimization for Models With Continuous Symmetry Breaking—0
Experimental Evaluation of Deep Learning models for Marathi Text Classification—0
Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity—0
Expanding the Text Classification Toolbox with Cross-Lingual Embeddings—0
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces—0
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