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Parsing
Parsing, syntax analysis, or syntactic analysis is the process of analyzing a string of symbols, either in natural language, computer languages or data structures, conforming to the rules of a formal grammar.
Within computational linguistics the term is used to refer to the formal analysis by a computer of a sentence or other string of words into its constituents, resulting in a parse tree showing their syntactic relation to each other, which may also contain semantic and other information (p-values). Some parsing algorithms may generate a parse forest or list of parse trees for a syntactically ambiguous input.
Simply speaking, parsing in NLP is the process of determining the syntactic structure of a text by analyzing its constituent words based on an underlying grammar (of the language).
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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
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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
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Low-Resource Scenario in Natural Language Processing
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Training Dataset