Natural Language Processing – Lexical Analysis, POS Tagging, N-grams, and NER – Lecture Summary
This document covers key concepts from Chapter 2 of Natural Language Processing, including lexical analysis, tokenization, normalization, and regular expressions. It also explains language models (unigram, bigram, trigram), smoothing techniques, POS tagging approaches, morphology, and named entity recognition. The notes include definitions, examples, formulas, and practical applications, making it a comprehensive summary for exam preparation. Content is clearly structured and aligned with foundational NLP topics.
Geschreven voor
- Instelling
- Symbiosis Institute Of Technology
- Vak
- NLP (TEE7022)
Documentinformatie
- Geüpload op
- 28 april 2026
- Aantal pagina's
- 10
- Geschreven in
- 2025/2026
- Type
- College aantekeningen
- Docent(en)
- G. suryaarayana reddy
- Bevat
- Alle colleges
Onderwerpen
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lexical analysis
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tokenization
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normalization
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regular expressions
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language models
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unigram
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bigram
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trigram
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n gram models
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probability in nlp
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smoothing techniques
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laplace smoothing
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good turing method
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back o