Part of Speech Tagging of Indian languages using

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Part of Speech Tagging of Indian languages using
Hidden Markov Model Ph. D. Seminar
Report by Manish Shrivastava Roll no.
03405002 Under the guidance of Dr. Pushpak
Bhattacharyya
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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

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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.

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Example of POS tagging
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Motivation

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

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Existing Taggers

• Techniques used for foreign languages
• Rule Based Tagging
• Stochastic Tagging

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Overview of PoS tagging
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Existing Taggers

• Rule Based Taggers
• Brill tagger
• Stochastic Taggers
• CLAWS tagger
• Tree tagger

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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

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Example of Free word structure
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Part of Speech tagging of Indian Languages

• To make efficient taggers
• Get morphological information
• Use heuristics to use morphological information
  

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Morphological Perspective

• Three kind of word morphologies
• Verb
• Noun
• Adjectives

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Morphological Perspective

• Noun Morphology
• Depicting possesion
• laD,ka Possesion laD,ko ka
• Depicting number
• laD,ka plural laD,ko

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Morphological Perspective

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

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Morphological Advantage

• POS tag heuristic
• Noun
• laD,kaoM Suffix -- oM aoM
  
• sahoilayaaoM Suffix -- iyoN yaaoM
• Verb
• pZ,UMgaa Suffix -- UMgA Mgaa
• pZ,ta Suffix -- wA ta

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Morphological Advantages

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

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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

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Hidden Markov Model
Example of a Hidden Markov Model
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Hidden Markov Model

• The Parameters
• ?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

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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.