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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 351–375 of 4002 papers

TitleStatusHype
A Deeper Look into Dependency-Based Word Embeddings—0
A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
A Multi-Domain Framework for Textual Similarity. A Case Study on Question-to-Question and Question-Answering Similarity Tasks—0
A Multidimensional Lexicon for Interpersonal Stancetaking—0
A Deep Content-Based Model for Persian Rumor Verification—0
Amrita\_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension—0
Amplifying the Range of News Stories with Creativity: Methods and their Evaluation, in Portuguese—0
Addressing the Challenges of Cross-Lingual Hate Speech Detection—0
A Classification-Based Approach to Cognate Detection Combining Orthographic and Semantic Similarity Information—0
A mostly unlexicalized model for recognizing textual entailment—0
A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition—0
Addressing Noise in Multidialectal Word Embeddings—0
Addressing Low-Resource Scenarios with Character-aware Embeddings—0
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages—0
A Chinese Writing Correction System for Learning Chinese as a Foreign Language—0
Decoupled Vocabulary Learning Enables Zero-Shot Translation from Unseen Languages—0
Text Classification Components for Detecting Descriptions and Names of CAD models—0
ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks—0
A Twitter Corpus and Benchmark Resources for German Sentiment Analysis—0
Automated Discovery of Mathematical Definitions in Text with Deep Neural Networks—0
A Model of Zero-Shot Learning of Spoken Language Understanding—0
Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification—0
Addressing Biases in the Texts using an End-to-End Pipeline Approach—0
A Mixture Model for Learning Multi-Sense Word Embeddings—0
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