2. POS Tagger and Chunker for Tamil
Presented by
V.Dhanalakshmi
M.Anand Kumar
CEN, Amrita.
Guided by Dr.K.P.Soman Head, CEN Amrita University. Dr.S.Rajendaran Head, Dept.Linguistics Tamil University.
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Overview
Introduction
Tamil POS Tagging
AMRITA Tagset
SVMTool
Chunking
Yamcha
Results
Conclusion
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Introduction
Part-of-speech (POS) tagging , also called grammatical tagging, is the process of assigning POS tags to each and every word in a sentence.
It is like assigning the grammatical category such as Noun, Verb, Adjective, Adverb etc .
The next process after POS tagging is chunking, which divides sentences into non recursive inseparable Phrases.
i.e. only one head in a phrase.
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Introduction
There are many Tools available for POS tagging and Chunking.
We have used SVM based Tools for Tamil POS tagging and Chunking.
SVMTOOL POS Tagging
YAMCHA Chunking
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Introduction
POS tagging and Chunking is considered as an important process in speech recognition, natural language parsing, information retrieval and machine translation.
Here POS Tagging problem is converted into classification problem.
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POS Tagging
INPUT: a string of words (sentence)
OUTPUT: a single best tag for each word (POS Tagged sentence)
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8. Example of Tamil POS Tagging
Assigning the words grammatical category in a sentence .
< Six feet tall bell is in the temple>
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Example of POS Tagging
NN CRD NN ADJ NN VF
<Six feet tall bell is in the temple>
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10. LEXICAL AMBIGUITY IN TAMIL.
Assign POS tags to words in a sentence considering its lexical ambiguity.
NN NN NN ADJ NN VF
NN CRD VF ADJ NNP VF
<Six feet tall bell is in the temple>
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POS Tagging Example
Assigning the words grammatical category considering its lexical ambiguity.
NN NN NN ADJ NN VF
NN CRD VF ADJ NNP VF
(Ambiguity tags)
Six feet tall bell is in the temple.
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12. COMPLEXITY IN TAMIL POS TAGGING
Tamil is a morphologically rich agglutinative language.
Mostly we depend on syntactic function or context to decide upon whether one word is a noun or adjective or adverb or post position. Example:
<varum> can be <VF> OR <VNAJ>
This leads to the complexity of Tamil in POS tagging.
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AMRITA TAGSET
Considering the Lexical ambiguities and syntactical complexities, we have created a new tag set <AMRITA tagset> to tag our corpus for SVM based POS Tagger for Tamil.
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AMRITA TAGSET
We considered the guidelines from “Annotating Corpora Guidelines For POS And Chunk Annotation For Indian Languages [IIIT, Hyderabad] ” while creating our AMRITA Tagset:
1. The tags should be simple.
2. Maintaining simplicity for Ease of Learning and Consistency in annotation.
3. POS tagging is not a replacement for morph analyzer.
4. A 'word' in a text carries grammatical category and grammatical features such as case, tense, person, number, gender, etc. The POS tag should be based on the 'category' of the word and the features can be acquired from the morph analyzer.
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AMRITA Tagset
Tagset is simple.
It is based on the 'category' of the word, does not considers the grammatical features of the word.
Tagset size:
32 Tags
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16. AMRITA Tag set for Tamil
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Corpus development :
We have developed our corpus of 2.50 LAKHS words, collecting corpora from Dinamani newspaper, yahoo tamil news, That’s Tamil, online Tamil short stories etc.
Three stages in corpus development
Pre-editing
Manual Tagging
Tagging using SVMTagger
Corpus size: 2.50 lakhs words
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18. SVM(Support Vector Machine)
Support vector machine is a training algorithm for learning classification and regression rules from data.
SVM is based on the idea of structural risk minimization, a principled technique for selecting a model which minimizes generalization error.
SVM is increasingly being used in processing NLP tasks
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SVMTool
This implementation is based on the principle of Support Vector Machines (SVM).
This Tool is developed by Jes´us Gim´enez and Llu´ıs M`arquez.
Trains efficiently and solve real NLP problems like POS tagging
SVMTool is freely available at
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Training Data Format
…….
இந்த <DET>
ஆண்டில் <NN>
3500 <CRD>
பஸ்கள் <NN>
வாங்கப்படும்<VF>
. <DOT>
இதில் <PRP>
சென்னை <NNP>
…..
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21. Tagger Implementation
Corpus
Tokenization
Tagging
Training
UnTagged words
SVMTagger
Tagged words
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CHUNKING
A subsequent step after tagging focuses on the identification of basic structural relations between groups of words. This is usually referred to as phrase chunking.
Input: Word sequence and POS tags
Output : A single best Chunk Tag for each word along with its POS tag.
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Chunking in Tamil
Tamil being an agglutinative language have a complex morphological and syntactical structure.
It is a relatively free word order language but in the phrasal and clausal construction it behaves like a fixed word order language.
The process of chunking in Tamil is less complex compared to the process of POS tagging.
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EXAMPLE
Assigning Chunk Tags to words in a sentences.
B-NP
B-NP
I-NP
B-NP
I-NP
B-VP
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Chunk tagset
S.No
Chunk Tag
Tag Name
Possible POS Tags 1 NP
Noun Phrase
NN,NNP,NNPC,NNC,NNQ,PRP,
QTF,DET,CRD,ORD,ADJ,INT 2
AJP
Adjectival Phrase
CRD, ADJ 3
AVP
Adverbial Phrase
ADV,INT,CRD 4
VFP
Verb Finite Phrase
VF,VAX 5
VNP
Verb Nonfinite Phrase
VNAJ,VNAV,VINT,CVB 6
VGP
Verb Gerund Phrase
VBG 7
CJP
Conjunctional
CNJ 8
COMP
Complimentizer
COM 9
. ?
Symbols O
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Chunk Tagset
IOB Tag:
The IOB tags are used to indicate the boundaries for each chunk
B – the current word is the beginning of a chunk, which may be followed by another chunk.
O - indicates the boundary of the sentence.
I – the current word is inside a chunk.
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Yamcha
YamCha is a generic, customizable, and open source text chunker.
YamCha is using a state-of-the-art machine learning algorithm called Support Vector Machines (SVMs), first introduced by Vapnik in 1995.
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28. TRAINING AND TEST FILE FORMAT
Both the training file and the test file need to be in a particular format for Yamcha to work properly.
The training and test file must consist of multiple tokens.
A token consists of multiple (but fixed-numbers) columns. The tokens are simply correspond to words. Each token must be represented in one line, with the columns separated by white space (spaces or tabular characters). A sequence of token becomes a sentence. To identify the boundary between sentences, an empty line is put.
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29. TRAINING AND TEST FILE FORMAT
We can give as many columns as we like, however the number of columns must be fixed through all tokens.
There are some kinds of "semantics" among the columns. For example, First column is 'word', second column is 'POS tag' third column is ‘CHUNK tag' and so on.
The last column represents a true answer tag which is going to be trained by Yamcha.
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Training data - sample
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31. Tagger Implementation
POS TAGGED Corpus
Manual Tagging
Yamcha Training
POS Tagged Input
Trained Model
Chunked output
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CONCLUSION
Chunking plays an important role in various Natural language processing applications.
Chunked corpus can be used for parsing which will provide important syntactic information for machine translation.
Future possible work is to increase the corpus size i.e. To build Annotated corpus for Tamil.
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REFERENCES
Gim´enez, J. and L.M`arquez. “Fast and Accurate Part-of-Speech Tagging”: The SVM Approach Revisited”. In Proceedings of the Fourth RANLP, 2003.
Rajendran S, “ Parsing in tamil -Present state of art”, language in india, Volume 6 : 8-th August 2006
Abney S, “Parsing by Chunks”, Principle-based parsing. Kluwer Academic Publishers, Dordrecht, pp , 1991.
Sobha L, Vijay Sundar Ram R. “Noun Phrase Chunking in Tamil”, In proceeding of the MSPIL-06, Indian Institute of Technology,Bombay.pp
Taku Kudo, CRF++:Yet Another CRFToolkit
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நன்றி
THANK YOU
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