Natural Language Processing (NLP) - Introduction
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Natural Language Processing (NLP) refers to the ability of computers to understand and interpret human language, employing machine learning algorithms for analysis. It has various applications such as voice assistants and text analysis tools, while enabling functionalities like sentiment analysis and text searching. However, NLP faces challenges in understanding abstract and nuanced language, such as sarcasm, highlighting its limitations.
Natural Language Processing (NLP) - Introduction
- 1. Natural Language Processing (NLP) Presented by Aritra Mukherjee Techno International Batanagar Computer Science & Engineering | 3rd Year
- 2. Introduction
- 3. Hey Google “is it going to rain today?” “do I need an umbrella today?”
- 4. What is NLP? Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken. NLP is a component of artificial intelligence.
- 5. Programming Languages C++, Java, Python … English, Hindi, Bengali … Natural Languages
- 6. Computers traditionally require humans to "speak" to them in a programming language that is precise, unambiguous and highly structured.
- 7. Human speech, is not precise - it is often ambiguous and the linguistic structure can depend on many complex variables, like regional dialects and social context.
- 8. How does NLP work?
- 9. Current approaches to NLP are based on machine learning algorithms, a subset of AI that examines and uses patterns in data to improve a program's understanding.
- 10. Techniques used in NLP
- 11. 1. Syntax In NLP, syntactic analysis is used to assess how the natural language aligns with the grammatical rules.
- 12. 2. Semantics Semantics refers to the meaning that is conveyed by text and involves computer algorithms to interpret the same.
- 13. “CSK was on fire last Sunday, they totally destroyed KKR” • To a computer, this may mean CSK was literally on fire. • CSK literally destroyed KKR and it doesn’t exist anymore!
- 14. Applications of NLP (some of the many)
- 15. 1. Voice Based Google Translate Google Assistant Siri
- 16. 2. Text Based SwiftKey Keyboard Microsoft Word Grammarly
- 17. Benefits of NLP
- 18. 1. Sentiment Analysis Enables data scientists to assess comments on social media to see how their business's brand is performing.
- 19. 2. Searching Text NLP allows analysts to sift through massive troves of text to find relevant information. Enterprise Search | Text Summarization | Filtering Sensitive Keywords
- 20. Limitations of NLP (understanding abstract rules)
- 21. 1. High Level Rules Sometimes, words can be high- level and abstract; for example, when someone uses a sarcastic remark to pass information.
- 22. 2. Low Level Rules Some of the rules can be low- levelled; for example, using the character “s” to signify the plurality of items.
- 24. 1. Generation of a natural language resource using a parallel corpus 2. SystemT: Declarative Text Understanding for Enterprise Alan Akbik, Laura Chiticariu, Marina Danilevsky, Yunyao Li, Huaiyu Zhu Laura Chiticariu, Marina Danilevsky, Yunyao Li, Frederick Reiss, Huaiyu Zhu
- 25. References
- 26. 1. Wikipedia - https://en.wikipedia.org/wiki/Natural_language_processing#Syntax 2. Search Business Analytics - https://searchbusinessanalytics.techtarget.com/definition/natural-language- processing-NLP 3. Becominghuman.ai - https://becominghuman.ai/a-simple- introduction-to-natural-language-processing-ea66a1747b32
- 27. Thank You