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Hindi POS tagging and chunking : An MEMM
approach
Aniket Dalal Kumar Nagaraj Uma Sawant Sandeep Shelke
Under the guidance of Prof. P. Bhattacharyya
Goal
Lexical AnalysisPart-Of-Speech (POS) Tagging : Assigning part-of-speech to each word. e.g. Noun, Verb...
Syntactic AnalysisChunking : Identify and label phrases as verb phrase, noun phrase etc.
Language : Hindi Approach : MEMM
Outline
Maximum Entropy Markov Model (MEMM)Principle
Mathematical formulation
System overview Parameter estimation and classification
POS tagging features
Chunking features
Results and error analysis
Future work
Conclusion
Maximum Entropy Markov Model
Maximum entropy principle The least biased model which considers all known
information is the one which maximizes entropy.
Entropy
Maximum Entropy Markov Model
Mathematical formulation...
The distribution with the maximum entropy is equivalent to
\
System overview
Parameter estimation and classification
GIS (Generalized Iterative Scaling)
finds the model parameters that define the maximum
entropy classifier for a given feature set and training
corpus
Beam Search
heuristic search algorithm, optimization of best-first
search
unfolds the first m most promising nodes at each depth
What are features?
Feature function : Indicator function which captures useful facts of the
modelling task
For example,
POS tagging features
Context-based POS tag of previous word
Current word
Word-dependentSuffixes
Digits
Special characters
English words
POS tagging features
Dictionary-basedPossible tags for the word, according to the dictionary
Corpus-drivenOccurrence of a word and its tag(s) according to the training data
Chunking features
Context based features Word itself (conditionally)
POS tag
Chunk label of previous word
Current POS tag based featureTag class
Experimental Setup
26 POS tags6 chunk labels75 - 25 split of training and test dataResult averaged over 10 data sets
Results
POS tagging accuracy Best : 89.346 %
Average : 88.4 %
Chunk labelling accuracy (per word basis)
Best : 87.399 %
Average : 86.45 %
Accuracy across runs
Error Analysis : POS tagging
Good performance for :VAUX, VFM, VNN
Postpositions
Need to improve :Compound tags
Proper nouns
Error Analysis : Chunking
Good performance for :Noun phrase
Need to improve :Verb phrase
Future Work
Morphological Features
Enriching dictionary
Hybrid models
References
1. Adwait Ratnaparakhi. 1996. A maximum entropy model for part-of-speech tagging. In Erich Brill and Kenneth Church, editors, Proceedings of the Conference on Empirical Methods in NLP, pages 133-142. ACL. Somerset, New Jersey.
2. Adwait Ratnaparakhi. 1997. A simple introduction to maximum entropy models for natural language processing. Technical report 97-08, Institute for Research in Cognitive Science, University of Pennsylvania.
References
3. Adam L. Berger , Vincent J. Della Pietra , Stephen A. Della Pietra, 1996 .A maximum entropy approach to natural language processing, Computational Linguistics, v.22 n.1, p.39-71.
4. Akshay Singh, Sushma Bendre, and Rajeev Sangal. 2005. HMM based chunker for hindi. In Proceedings of IJCNLP-05. Jeju Island, Republic of Korea.
References
5. J. N. Darroch, D. Ratcliff, 1972. Generalized Iterative Scaling for Log-Linear Models, The Annals of Mathematical Statistics.
Thank you!
Questions ?
Example
Ram/PN aur/CC Sita/PN Shaadi/N karne/GRND ja/VM
rahen/VAUX hain/VAUX
Beam Search
Ram
N:0.3 CC:0.005 PN:0.4 CC:0.2
CC:0.15 CC:0.25 INJ:0.10
VA:0.05
Aur