Natural Language Understanding (NLU) Algorithm
A Natural Language Understanding (NLU) Algorithm is an NLP algorithm designed to perform Natural Language Understanding (NLU) Tasks, which involves automated text processing to derive semantic representations from digital text items.
- AKA: Text Understanding Method.
- Context:
- It can process a Digital Text Item to generate a Semantic Representation.
- It can be evaluated using NLU Task Performance Measures such as Time, Recall, Precision, and Comprehension Level.
- It can range from handling Shallow NLU Tasks like Text Intent Classification to Deep NLU Tasks such as Machine Reading Comprehension.
- It can be developed for Language-specific NLU Tasks (e.g., English Understanding Task) or Language-independent NLU Tasks.
- It can support General Text Understanding Tasks or Domain-Specific Text Understanding Tasks.
- It can be part of an Automated Text Understanding System that integrates multiple NLU algorithms.
- It can include preprocessing steps like Tokenization, Part-of-Speech Tagging, or Syntactic Parsing.
- It can support downstream tasks like Question Answering, Text Summarization, or Information Extraction.
- It can utilize prompting techniques for optimizing LLMs for specific NLU tasks.
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- Example(s):
- an Text Intent Classification Algorithm using LLMs and few-shot prompting.
- an Named Entity Recognition Algorithm employing CoT prompting and schema.org definitions.
- an Machine Reading Comprehension Algorithm that incorporates field definitions in prompts.
- an Document-level Sentiment Analysis Algorithm using fine-tuned LLMs.
- an Legal Text Classification Algorithm using LLMs with few-shot and CoT prompting.
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- Counter-Example(s):
- NLG Algorithm, which focuses on generating text rather than understanding it.
- Human-Performed Reading, which is not an automated process.
- Optical Character Recognition, which deals with text recognition rather than understanding.
- Automated Speech Comprehension, which focuses on spoken language.
- Mathematical Formula Understanding Task, which is not focused on natural language.
- Scene Understanding Task, which deals with visual data rather than text.
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- See: Automated Text Understanding (NLU) Task, Shallow NLU Task, Deep NLU Task, Language-specific NLU Task, Language-independent NLU Task, Text Intent Classification, Named Entity Recognition, Machine Reading Comprehension, Document-level Sentiment Analysis, Legal Text Classification.