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High Dimensional Outputs
For natural language applications, we often want to use words as the fundamental unit of the output. However, because vocabularies can be very large (up to hundreds of thousands of words), this can be computationally expensive. Both the high memory cost and the high computational cost, which occur at training and testing time, make the standard way to approach this type of distribution have many difficulties.
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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
Spelling correction and the noisy channel
Constituency
Text Classification
Information Extraction (IE)
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From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information
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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Semantic Textual Similarity (STS)
Paraphrase Identification (PI)
Machine Comprehension (MC)
Sentence Representation Model Categorizations
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Phonology / Phonetics
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Sentence Pair Modelling
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Training Dataset