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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 3211–3220 of 4002 papers

TitleStatusHype
Automating Idea Unit Segmentation and Alignment for Assessing Reading Comprehension via Summary Protocol Analysis—0
AWE: Asymmetric Word Embedding for Textual Entailment—0
A Word Embedding Approach to Identifying Verb-Noun Idiomatic Combinations—0
A Word Embedding Approach to Predicting the Compositionality of Multiword Expressions—0
A Word-Embedding-based Sense Index for Regular Polysemy Representation—0
AZMAT: Sentence Similarity Using Associative Matrices—0
Massively Multilingual Lexical Specialization of Multilingual Transformers—0
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling—0
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models—0
Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction—0
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