1. Hindi POS Tagger By Naveen Sharma (02005010)
Prabhu Sachin H. ( )
Prateek Choudhary ( )
Gaurav Meena ( )

2. Problem Definition and Challanges
Pos Tagging
Identifying lexical category of a word on the basis of its context in sentence.
e.g. Shyam Khana Khayega
Shyam[NN] Khana[NN] Khayega[V].
Challenges :
Resolving Ambiguities
Multiple suffix
Multiple category
Handling Unknown words

3. Approach Possible approaches Rule Based Stochastic Hybrid
Take possible tags
Use Disambiguation rules
e.g If ( +1 A/ADV)
Eliminate Non ADV tags.
Stochastic
Probability based
Hybrid
Use features of both rule based and stochastic
Improved Accuracy

4. Rule Base Approach Basic Setup Components Rule Based
Morphological Analyzer
Takes word as input and generates all possible tags
Stemmer
Rule Generation
Mainly literature
Transformation Based.....Attempted

5. Algorithm for POS Tagging
POS_TAGGER ( sentence s ){
w<-􀃅 the first untagged word from the right in the sentence s
if ( some word is untagged in s ){
X 􀃅<- PPOS(w) /*X is the possible set of lexical categories that w can take*/
if (w is not the last word of the sentence) then
X <-􀃅 X ( Intesection) ∩ PREV(word immediately following w)
for each element e in ( X ){
if ( w tagged as e obeys semantic constraint set )
{tag w as e and call POS_TAGGER( s )}
else output the tagged sentence s}

6. Current Work Work Done Literature --- available Algos Corpora
Transformation based rule generation
Rule Based Tagging
Corpora
Limited...still searching

7. Future Schedule Stage Timeline Groundwork 10th - 25th March
Implemenation/1st run 1st April
Discussions/ Improvements 1st - 10th April
Final run Demo

8. References
Brill Eric. Transformation Based Error Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging. 21(4): , 1995
Ray P. R., Harish V., Sarkar S. and Basu A. Part of Speech Tagging and Local Word Grouping Techniques for Natural Language Parsing in Hindi.  Proceedings of International Conference on Natural Language Processing (ICON 2003), Mysore, (pp )