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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 1376–1400 of 4002 papers

TitleStatusHype
A Deterministic Algorithm for Bridging Anaphora Resolution—0
Contextualized Spoken Word Representations from Convolutional Autoencoders—0
Contextualized moral inference—0
Contextualized Embeddings for Enriching Linguistic Analyses on Politeness—0
Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing—0
A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations—0
Contextualized context2vec—0
Contextualization and Generalization in Entity and Relation Extraction—0
Analysis of Italian Word Embeddings—0
Contextual Embeddings: When Are They Worth It?—0
Contextual Document Embeddings—0
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings—0
Contextual Aware Joint Probability Model Towards Question Answering System—0
Contextual and Position-Aware Factorization Machines for Sentiment Classification—0
A Syllable-based Technique for Word Embeddings of Korean Words—0
Analysis of Inferences in Chinese for Opinion Mining—0
Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation—0
Context Sensitive Neural Lemmatization with Lematus—0
Context-Sensitive Malicious Spelling Error Correction—0
A Survey on Word Meta-Embedding Learning—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 Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions—0
ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification—0
Context-Dependent Sense Embedding—0
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