Natural Language Understanding (NLU) Algorithm: Difference between revisions
Jump to navigation
Jump to search
(Created page with "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. * <B>AKA:</B> Text Understanding Method. * <B>Context:</B> ** It can process a Digital Text Item to generate a Semantic Representation. ** It can be evaluated using NLU Task Performance Measures such a...") |
No edit summary |
||
| Line 2: | Line 2: | ||
* <B>AKA:</B> [[Text Understanding Method]]. | * <B>AKA:</B> [[Text Understanding Method]]. | ||
* <B>Context:</B> | * <B>Context:</B> | ||
** | ** [[Algorithm Input|input]]: [[Digital Text Item]]. | ||
** | ** [[Algorithm Output|output]]: [[Semantic Representation]]. | ||
** [[Algorithm Performance Measure|measure]]: [[NLU Task Performance Measure]]s. | |||
** It can range from being a [[Data-Driven Text Understanding Algorithm]] to being a [[Heuristic Text Understanding Algorithm]]. | |||
** It can range from handling [[Shallow NLU Task]]s like [[Text Intent Classification]] to [[Deep NLU Task]]s such as [[Machine Reading Comprehension]]. | ** It can range from handling [[Shallow NLU Task]]s like [[Text Intent Classification]] to [[Deep NLU Task]]s such as [[Machine Reading Comprehension]]. | ||
** It can be developed for [[Language-specific NLU Task]]s (e.g., [[English Understanding Task]]) or [[Language-independent NLU Task]]s. | ** It can be developed for [[Language-specific NLU Task]]s (e.g., [[English Understanding Task]]) or [[Language-independent NLU Task]]s. | ||
** It can support [[General Text Understanding Task]]s or [[Domain-Specific Text Understanding Task]]s. | ** It can support [[General Text Understanding Task]]s or [[Domain-Specific Text Understanding Task]]s. | ||
** It can be | ** It can be implemented in an [[Automated Text Understanding System]] (that can solve an [[NLU task]]). | ||
** It can include preprocessing steps like [[Tokenization]], [[Part-of-Speech Tagging]], or [[Syntactic Parsing]]. | ** 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 support downstream tasks like [[Question Answering]], [[Text Summarization]], or [[Information Extraction]]. | ||
| Line 36: | Line 38: | ||
---- | ---- | ||
__NOTOC__ | __NOTOC__ | ||
[[Category:NLP]] | |||
[[Category:Quality Silver]] | |||
Revision as of 23:41, 19 May 2024
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:
- input: Digital Text Item.
- output: Semantic Representation.
- measure: NLU Task Performance Measures.
- It can range from being a Data-Driven Text Understanding Algorithm to being a Heuristic Text Understanding Algorithm.
- 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 implemented in an Automated Text Understanding System (that can solve an NLU task).
- 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.
- ...
- 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.
- ...
- 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.
- ...
- 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.