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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 301–325 of 4002 papers

TitleStatusHype
A Domain Adaptation Regularization for Denoising Autoencoders—0
Adjusting Word Embeddings with Semantic Intensity Orders—0
Analyzing autoencoder-based acoustic word embeddings—0
A Comparative Study of Neural Network Models for Sentence Classification—0
4chan & 8chan embeddings—0
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings—0
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets—0
Analyzing Acoustic Word Embeddings from Pre-trained Self-supervised Speech Models—0
A Distribution-based Model to Learn Bilingual Word Embeddings—0
A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions—0
Analysis of Word Embeddings Using Fuzzy Clustering—0
Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction—0
A Deterministic Algorithm for Bridging Anaphora Resolution—0
A Simple and Efficient Probabilistic Language model for Code-Mixed Text—0
Analysis of Italian Word Embeddings—0
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning—0
Analysis of Inferences in Chinese for Opinion Mining—0
Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings—0
A Deep Representation Empowered Distant Supervision Paradigm for Clinical Information Extraction—0
A Short Survey of Pre-trained Language Models for Conversational AI-A NewAge in NLP—0
A Simple Disaster-Related Knowledge Base for Intelligent Agents—0
A comparative analysis of embedding models for patent similarity—0
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't.—0
A Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task—0
A Deep Neural Framework for Contextual Affect Detection—0
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