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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 3201–3225 of 4002 papers

TitleStatusHype
Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia—0
Automatic Generation of Multiple-Choice Questions—0
Automatic Labeling of Problem-Solving Dialogues for Computational Microgenetic Learning Analytics—0
Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings—0
Metaphor Interpretation Using Word Embeddings—0
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings—0
Automatic Term Extraction from Newspaper Corpora: Making the Most of Specificity and Common Features—0
Automatic Transformation of Clinical Narratives into Structured Format—0
Automatic Triage of Mental Health Forum Posts—0
Automatic Word Association Norms (AWAN)—0
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
Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning—0
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese—0
Balancing the composition of word embeddings across heterogenous data sets—0
Batch IS NOT Heavy: Learning Word Representations From All Samples—0
Bayesian Paragraph Vectors—0
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