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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 801–825 of 4002 papers

TitleStatusHype
AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach—0
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation—0
Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine—0
Comparing the Performance of Feature Representations for the Categorization of the Easy-to-Read Variety vs Standard Language—0
Comparing Word Representations for Implicit Discourse Relation Classification—0
Comparison between Voting Classifier and Deep Learning methods for Arabic Dialect Identification—0
Comparison of Paragram and GloVe Results for Similarity Benchmarks—0
Comparison of Representations of Named Entities for Document Classification—0
Comparison of Short-Text Sentiment Analysis Methods for Croatian—0
A House United: Bridging the Script and Lexical Barrier between Hindi and Urdu—0
Compiling a Highly Accurate Bilingual Lexicon by Combining Different Approaches—0
Complementary Strategies for Low Resourced Morphological Modeling—0
Complex networks based word embeddings—0
Complex Ontology Matching with Large Language Model Embeddings—0
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations—0
Component-Enhanced Chinese Character Embeddings—0
Composing Knowledge Graph Embeddings via Word Embeddings—0
Composing Noun Phrase Vector Representations—0
Composing Word Vectors for Japanese Compound Words Using Bilingual Word Embeddings—0
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA—0
A Hmong Corpus with Elaborate Expression Annotations—0
Compositional Fusion of Signals in Data Embedding—0
Compositional Morpheme Embeddings with Affixes as Functions and Stems as Arguments—0
Compound Embedding Features for Semi-supervised Learning—0
An RNN-based Binary Classifier for the Story Cloze Test—0
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