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Part-of-Speech Tagging and Chunking with Maximum Entropy Model. Sandipan Dandapat Department of Computer Science & Engineering Indian Institute of Technology Kharagpur. Goal. Lexical Analysis Part-Of-Speech (POS) Tagging : Assigning part-of-speech to each word. e.g. Noun, Verb...

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part of speech tagging and chunking with maximum entropy model

Part-of-Speech Tagging and Chunking with Maximum Entropy Model

Sandipan Dandapat

Department of Computer Science & Engineering

Indian Institute of Technology Kharagpur

slide2
Goal
  • Lexical Analysis
    • Part-Of-Speech (POS) Tagging : Assigning part-of-speech to each word. e.g. Noun, Verb...
  • Syntactic Analysis
    • Chunking: Identify and label phrases as verb phrase and noun phrase etc.
machine learning to resolve pos tagging and chunking
Machine Learning to Resolve POS Tagging and Chunking
  • HMM
    • Supervised (DeRose,88; Mcteer,91; Brants,2000; etc.)
    • Semi-supervised (Cutting,92; Merialdo,94; Kupiec,92; etc.)
  • Maximum Entropy (Ratnaparkhi,96; etc.)
  • TB(ED)L (Brill,92,94,95; etc.)
  • Decision Tree (Black,92; Marquez,97; etc.)
our approach
Our Approach
  • Maximum Entropy based
  • Diverse and overlapping features
  • Language Independence
  • Reasonably good accuracy
  • Data intensive
  • Absence of sequence information
pos tagging schema
POS Tagging Schema

Language

Model

Raw

text

Disambiguation

Algorithm

Tagged

text

Possible POS

Class Restriction

POS tagging

pos tagging our approach
POS Tagging: Our Approach

ME Model

ME Model: Current state depends on history (features)

Raw

text

Disambiguation

Algorithm

Tagged

text

Possible POS

Class Restriction

POS tagging

pos tagging our approach1
POS Tagging: Our Approach

ME Model

ME Model: Current state depends on history (features)

Raw

text

Disambiguation

Algorithm

Tagged

text

Possible POS

Class Restriction

POS tagging

pos tagging our approach2
POS Tagging: Our Approach

{T} : Set of all tags

TMA(wi) : Set of tags computed by Morphological Analyzer

ME Model

ti  {T}

or

ti  TMA(wi)

Raw

text

Disambiguation

Algorithm

Tagged

text

POS tagging

pos tagging our approach3
POS Tagging: Our Approach

{T} : Set of all tags

TMA(wi) : Set of tags computed by Morphological Analyzer

ME Model

ti  {T}

or

ti  TMA(wi)

Raw

text

Beam Search

Tagged

text

POS tagging

disambiguation algorithm
Disambiguation Algorithm

Text:

Tags:

Where, ti{T} , wi{T} = Set of tags

disambiguation algorithm1
Disambiguation Algorithm

Text:

Tags:

Where, ti TMA(wi), wi{T} = Set of tags

what are features
What are Features?
  • Feature function
    • Binary function of the history and target

Example,

pos tagging features

i-3 W1 T1

i-2

i-1

i

i+1

i+2

i+3

T2

T3

W1

W2

W3

W4

W4

T4

T5

T6

T7

T4

POS Tagging Features

pos word POS_Tag

Feature Set

Estimated Tag

  • 40 different experiments were conducted taking several combination from set ‘F’
pos tagging features1

i-3 W1 T1

T2

W2

i-2

i-1

i

i+1

i+2

i+3

T3

T3

W3

W3

W4

T4

T5

T6

T7

W6

W7

POS Tagging Features

pos word POS_Tag

Feature Set

Estimated Tag

chunking features
Chunking Features

pos word POS_Tag Chunk_Tag

i-3 W1 T1 C1

W2

W3

i-2

i-1

i

i+1

i+2

i+3

C2

C3

T2

T3

T4

T5

T6

Feature Set

W4

Estimated Tag

C4

C5

C6

C7

W5

W6

W7

T7

experiments pos tagging
Experiments: POS tagging
  • Baseline Model
  • Maximum Entropy Model
    • ME (Bengali, Hindi and Telugu)
    • ME + IMA ( Bengali)
    • ME + CMA (Bengali)
  • Data Used
tagset and corpus ambiguity
Tagset and Corpus Ambiguity
  • Tagset consists of 27 grammatical classes
  • Corpus Ambiguity
    • Mean number of possible tags for each word
    • Measured in the training tagged data

(Dermatas et al 1995)

pos tagging results on development set1
POS Tagging Results on Development Set

Known Words

Unknown Words

Overall Accuracy

chunking results
Chunking Results
  • Two different measures
    • Per word basis
    • Per chunk basis  Correctly identified groups along with correctly labeled groups
assessment of error types
Assessment of Error Types

Bengali

Hindi

Telugu

results on test set
Results on Test Set
  • Bengali data has been tagged using ME+IMA model
  • Hindi and Telugu data has been tagged with simple ME model
  • Chunk Accuracy has been measured per word basis
conclusion and future scope
Conclusion and Future Scope
  • Morphological restriction on tags gives an efficient tagging model even when small labeled text is available
  • The performance of Hindi and Telugu can be improved using the morphological analyzer of the languages
  • Linguistic prefix and suffix information can be adopted
  • More features can be explored for chunking