Final Demo of Assignment

1 / 13

# Final Demo of Assignment - PowerPoint PPT Presentation

Final Demo of Assignment. Group – 8 Maunik Shah Hemant Adil Akanksha Patel. POS Tagging using Viterbi Algorithm. Unknown Word handling: Using Previous tags Using Smoothing. POS Tagging using Viterbi Algorithm. Unknown Word handling: Using Previous tags. W n-1 |T n-1. Wn|T n.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about 'Final Demo of Assignment' - oro

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### Final Demo of Assignment

Group – 8

Maunik Shah

Akanksha Patel

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

Wn-1|Tn-1

Wn|Tn

Emission Table

Transition Table

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

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%
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%

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

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 %
Parser projection

( Study assignment )

• Presentation has already given by us…
NLTK

( Study assignment )

• Presentation has been prepared...
YAGO
• Demo has been prepared in terms of DFS.. Example output ::
• Input siring 1 :: Tardeo

Input siring 2 :: Egmore

Tardeo

Mumbai

Maharastra

India

Chennai

Egmore

( Using only yagoGeoData file as knowledge

Database )

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.
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..