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Deep Learning Algorithms in Data Augmentation
Deep learning algorithms are preferred over deep neural networks for purposes of data augmentation due to their strength and lower bias-error, but exhibit higher variance in different subsets of training data. This can be solved by regularizing the algorithm so that it prefers simple solutions, or providing it with more training data - usually through data augmentation.
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Deep Learning
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Natural language processing in ACM Computing Classification
NLP references
Models used in NLP
Text normalization
Part-of-speech Tagging
Sentiment Analysis
Topic Model
Parsing
High Dimensional Outputs
Historical Perspective: Natural Language Processing
Machine Reading and Comprehension
Minimum Edit Distance
Variation Factors of Input Texts
Period Disambiguation
Features Design for NLP Classification Problems
Vector Semantics and Embeddings
Words and Vectors
English Word Classes
Logical Representations of Sentence Meaning
First-Order Logic
Information Extraction
Word Senses
Semantic Roles: Labeling
Semantic Roles ( Thematic Roles )
Question Answering
Information Retrieval
Dialogue Systems
Properties of Human Conversation
Prompt Tuning
Types of NLP Model Paradigms
Types of Training Objectives of Pre-trained LM
Major Tuning Strategy Types
Articulatory Phonetics
Phonetics
Word embedding
A Survey of Data Augmentation Approaches for NLP
Data Augmentation in NLP
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Constituency
Text Classification
Information Extraction (IE)
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Machine Translation (MT)
Temporal Reasoning
Knowledge Graph
Dynamic Neural Network in Natural Language Processing
Label Preservation
Deep Learning Algorithms in Data Augmentation
Applications of Data Augmentation
Coreference Resolution
Explainable AI for Natural Language Processing
Corpora
Racism in NLP
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Low-Resource Scenario in Natural Language Processing
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios
Low-Resource NLP
Continual Learning
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Object Naming in Language and Vision
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Text Style Transfer (TST)
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Number representation in NLP
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Paraphrase Identification (PI)
Machine Comprehension (MC)
Sentence Representation Model Categorizations
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Phonology / Phonetics
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering
Sentence Pair Modelling
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Training Dataset