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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 1776–1800 of 4002 papers

TitleStatusHype
Multilingual Culture-Independent Word Analogy Datasets—0
Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context—0
Empirical Autopsy of Deep Video Captioning Frameworks—0
SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word EmbeddingsCode0
Error Analysis for Vietnamese Named Entity Recognition on Deep Neural Network Models—0
Bootstrapping NLU Models with Multi-task Learning—0
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis—0
What do you mean, BERT? Assessing BERT as a Distributional Semantics Model—0
word2ket: Space-efficient Word Embeddings inspired by Quantum EntanglementCode0
Learning Multi-Sense Word Distributions using Approximate Kullback-Leibler Divergence—0
How to Evaluate Word Representations of Informal Domain?Code0
Contextualized End-to-End Neural Entity Linking—0
Towards Understanding Gender Bias in Relation ExtractionCode0
Ruminating Word Representations with Random Noised Masker—0
Neural Graph Embedding Methods for Natural Language ProcessingCode0
Interactive Refinement of Cross-Lingual Word EmbeddingsCode0
Should All Cross-Lingual Embeddings Speak English?Code0
How Can BERT Help Lexical Semantics Tasks?—0
Invariance and identifiability issues for word embeddings—0
A Deep Learning approach for Hindi Named Entity Recognition—0
Incremental Sense Weight Training for the Interpretation of Contextualized Word Embeddings—0
Integrating Dictionary Feature into A Deep Learning Model for Disease Named Entity Recognition—0
Assessing Social and Intersectional Biases in Contextualized Word RepresentationsCode0
Emerging Cross-lingual Structure in Pretrained Language Models—0
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited—0
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