1. Dr. Pushpak Bhattacharyya
Part of Speech Tagging of Indian languages using
Hidden Markov Model
Ph. D. Seminar Report by
Manish Shrivastava
Roll no
Under the guidance of
Dr. Pushpak Bhattacharyya

2. Presentation Outline Part of Speech Tagging Motivation
Existing Taggers
Need for Part of Speech Taggers for Indian languages
Part of Speech Tagging of Indian languages
The Morphological Perspective
Morphological Advantages
Hidden Markov Model
Conclusions
Future work

3. Part of Speech Tagging Is the task of assigning POS tags to words
Selecting among more than one tags that apply
Can be used for further NLP tasks
Information extraction, Question Answering etc.

4. Example of POS tagging

5. Motivation Lack of significant tools for Indian languages
Dependence of other NLP activities on PoS tagging
Failure of existing techniques on Indian Languages

6. Existing Taggers Techniques used for foreign languages
Rule Based Tagging
Stochastic Tagging

7. Overview of PoS tagging

8. Existing Taggers Rule Based Taggers Stochastic Taggers Brill tagger
CLAWS tagger
Tree tagger

9. Need for a new Taggers for Hindi
The existing taggers fail on Indian languages
The grammatical structure differs
Free word structure of Hindi
Stochastic taggers cannot give good performance
Morphological Information not taken into account

10. Example of Free word structure

11. Part of Speech tagging of Indian Languages
To make efficient taggers
Get morphological information
Use heuristics to use morphological information

12. Morphological Perspective
Three kind of word morphologies
Verb
Noun
Adjectives

13. Morphological Perspective
Noun Morphology
Depicting possesion
laD,ka Possesion laD,ko ka
Depicting number
laD,ka plural laD,ko

14. Morphological Perspective
Verb Morphology
Tense
Kola laD,ko Kola rho hO.
Kola laDko Kolato qao .
Kola laD,ko Kolanaa caahto hOM.

15. Morphological Advantage
POS tag heuristic
Noun
laD,kaoM Suffix -- oM “ aoM “
sahoilayaaoM Suffix -- iyoN “ [yaaoM “
Verb
pZ,U^Mgaa Suffix -- UMgA “ }^Mgaa “
pZ,ta Suffix -- wA “ ta “

16. Morphological Advantages
Morphological strength of Hindi helps in efficient tagging
The morphological information can be used for further tasks

17. The Tool : Hidden Markov Model
Why HMM
Underlying events generate surface probabilities
The models can be trained using Expectation Maximization algorithm.
Easy to port to other languages

18. Example of a Hidden Markov Model

19. Hidden Markov Model The Parameters Estimation
i = initial state probabilities
aij = state transition probability
bij = probability of recognizing kth symbol in transition from i to j
Estimation
Initial estimation done with training data
Re-estimation done using Baum-Welch Re-estimation

20. Conclusions
The Part of Speech taggers for Hindi should morphological information
To make efficient taggers we must allow use of heuristics
Hidden Markov Models can be used for portable taggers.