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#### Final Demo of Group Assignment: POS Tagging with Viterbi Algorithm and Next Word Prediction ####

This demo showcases the implementation of POS tagging using the Viterbi algorithm, including unknown word handling, smoothing techniques, and accuracy evaluations. Additionally, it explores generative vs. discriminative methods, next word prediction algorithms, A* algorithm for POS tagging, and more. Concluding with insights on the importance and applications of Natural Language Processing, this assignment emphasizes the intersection of linguistic, machine learning, AI, and mathematics. ####

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#### Final Demo of Group Assignment: POS Tagging with Viterbi Algorithm and Next Word Prediction ####

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  1. Final Demo of Assignment Group – 8 Maunik Shah HemantAdil Akanksha Patel

  2. POS Tagging using Viterbi Algorithm • Unknown Word handling: • Using Previous tags • Using Smoothing

  3. POS Tagging using Viterbi Algorithm • Unknown Word handling: • Using Previous tags Wn-1|Tn-1 Wn|Tn Emission Table Transition Table

  4. POS Tagging using Viterbi Algorithm • Unknown Word handling: • Using Smoothing • P(Wn|Tn) = #(Wn|Tn) + ℷ #(T) + k ℷ • ℷ = 0.1 • K = |W| ; number of distinct words in corpus • T = tags Wn|Tn

  5. POS Tagging using Viterbi Algorithm • Accuracy by previous tag method (bigram) = 92.3702% • Accuracy by smoothing method (bigram) = 92.29% • Accuracy by smoothing method (trigram) = 92.88%

  6. Generative vs Discriminative Methods of tagging • Discriminative Method • Argmax P(tn | wn, tn-1) tn • P(tn | wn, tn-1) = #<tn,wn, tn-1> #<wn, tn-1> Accuracy = 89.07% • Generative Method Accuracy = 92.29%

  7. Next Word Prediction Algorithm • Only word model P(Wn|Wn-1) Accuracy = 12.49734 • Using POS Tags P(Wn|Tn-1,Wn-1) Accuracy = 13.16058 Perplexity :: For word model => 8703.63611651 For word-tag model => 8211.31994867

  8. POS Tagging using A* Algorithm • g =Σ - log(transition probability*Emission probability) • h = hop count * X • X=min[-log (transition Probability * Emission probability)] • Accuracy of system :: 89.87 %

  9. Parser projection ( Study assignment ) • Presentation has already given by us…

  10. NLTK ( Study assignment ) • Presentation has been prepared...

  11. YAGO • Demo has been prepared in terms of DFS.. Example output :: • Input siring 1 :: Tardeo Input siring 2 :: Egmore Tardeo Mumbai Maharastra India Tamilnadu Chennai Egmore ( Using only yagoGeoData file as knowledge Database )

  12. Conclusion of CS 626 • Natural Language Processing seems like a linguistic subject as first glance but more than that it has a lot of Machine Learning , AI algorithms and Mathematics involved.. Linguistic is base but NLP is more about involving linguistic to mathematics and AI.. • And seeing at the applications of NLP, we have to simply say that NLP is important to almost all automatic user interactive applications and that even involves future web structure.

  13. Take Outs from CS 626 • We now have a whole different approach to linguistic than we had 4 months ago.. • Before we learn this subject we could never think of even including probability with language, the two most distanced and most distasteful topics we had seen before coming here.. • If we tell others that we are learning linguistic subject here in IITB, they clueless guys may just laugh on us but we know that it has more interesting things to learn than we could learn in any other subject, that includes things about linguistics and applications of mathematics in linguistics too..

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