dr. tv. geetha
TRANSCRIPT
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A Special Talk
am ompu ng am ompu ng
Dr.T.V.Geetha,
Tamil Com utin Lab TACOLA De t. of CSE & IST
College of Engineering Guindy, Anna University Chennai
Team Co-ordinators: Ranjani Parthasarathy & Dr.Madhan Karky
27th January 20121Tamil Computing
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Morphologically rich language
Morphological suffixes convey most of the
roles played in a sentenceAmbiguity at morphological level
Ambi uit at semantic level
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Our BasisOur Basis
Linguistics
Use of POS Tags
Use of Word Based Semantics with well defined semantic
constraints (primitives) UNL
Rule based Approach & FSA for TamilClustering Approaches
ro a s c pproac es a ve ayes, on ona an omField, HMM
Machine Learning Bootstrapping & Unsupervised Approach
Tamil Computing 4
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Tamil Computing 5
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Language ProcessingLanguage Processing
Morphological Analyzer
POS TaggingChunking
Named Entity Recognition
Word Sense Disambiguation
Anap ora Reso ut onSemantic Interpretation
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Morphological AnalyserMorphological Analyser -- IntroductionIntroduction
Most of the textual data contains compound,
.
Due to morphological richness, Tamil language
nee s an ng o ose wor s.
Development of an Integrated Morphologicalanalyser (Compound, Numeral, Colloquial)
Needed to tackle
News & Lyrics
analysis
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Morphological AnalyserMorphological Analyser Word processingWord processing
Morphological suffix stripping - (Conventional
-
The word W is processed by compound
The word W is processed by numeral
anal ser
(Right to left by concatenating vowels and
consonants and iterativel checkin al habets
in the Morph Dictionary and applying Tamil
grammar rules)
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MorphologicalMorphological AnalyserAnalyser
ompoun or epresen a on ompoun or epresen a on
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MorphologicalMorphological AnalyserAnalyser
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Compound AnalyserCompound Analyser RulesRules ClassicationClassication
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Morphological AnalyserMorphological Analyser
ompoun na yser
Based on Finite State Transducer (FST)
Not only handles simple compounding
Handling compounding between two words that may cause inflectional
variations during compounding process
Ex : (i) (Golden statue)Rule:
If, the second constituents first alphabet is Hard consonant
, ,
then No ModificationNo Modification
+
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Morphological AnalyserMorphological Analyser ompoun na yser
Ex : (Root tree)u e :
If, the second constituents first alphabet is Consonant , alphabet
(Root)+
InsertionInsertion
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Morphological AnalyserMorphological Analyser ompoun na yser
Ex : (Sand pot)u e :
If, the second constituents first alphabet is Hard ConsonantThen, the first constituents last alphabet - is replaced by
ep acemenep acemen
(Pot)+
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Morphological AnalyserMorphological Analyser ompoun na yser
Ex : (Banana)u e :
If, the second constituents first alphabet is Hard Consonant
Then, the first constituents last alphabet is the same Hard
consonant, then it is deleted
DeletionDeletion
(Fruit)
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Compound AnalyserCompound Analyser RulesRules ClassificationClassification
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Compound Word AnalyserCompound Word Analyser -- FSTFST
n e a e rans ucer
Two taps which describes the input (lexical form) and output
(Surface form) sequences
It has seven tuples
1 represents the finite alphabet, namely the input alphabet
2 represents the finite alphabet, namely the output alphabet
(bi1,......bik) s a n e se o s a es , , , , , ,
i Q is the initial state(S0 )
F is a subset of Q, the set of final states;(S6 )
Here a:b represents the replacement of a in the surface form to b
in the lexical form
form.
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Compound AnalyserCompound Analyser FSTFST
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Morphological AnalyzerMorphological Analyzer
Numeral analyzer
Based on Finite tate Transducer F T
Numbers, one to ten, hundred, thousand, lakh
and crore can be directly converted into numbers
Ex : (Ten)Rule: No modification
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Morphological AnalyserMorphological Analyser umera na yser
Ex : (Five Thousand)
If, the second constituents first alphabet is Vowel
,insert ''
* 5000
InsertionInsertion
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Morphological AnalyserMorphological Analyser umera na yser
Ex : (Twenty Five)u e :
If, the second constituent first alphabet is Vowel
, ,
then replace that with
ep acemenep acemen + 25
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Morphological AnalyserMorphological Analyser umera na yser
Ex : (Twenty Three)u e :
If, the second constituents first alphabet is Soft Consonant
,
then delete the hard consonant
DeletionDeletion (Three)+
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Morphological AnalyserMorphological Analyser
Colloquial Analyser
ase on pa ern mapp ng approac
To the best of our knowledge, no previous work
formal word.
mapping for transforming informal (colloquial)
written word into formal written word.
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Colloquial AnalyserColloquial Analyser
Pattern based Approach based on spelling variation rules
Word processing Right to left
List of Spelling variation rules
Suffix Mapping of ending patterns
Suffix Mapping of ending patterns with Morphographemic
changes
u x mapp ng o en ng pa erns w c ec ng o one wo
preceding characters
In all the rules, pattern p1 of colloquial form is converted into
pattern p2 of normal form
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Suffix Mapping of ending patternsSuffix Mapping of ending patterns
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Colloquial AnalyserColloquial Analyser
Suffix Mapping of ending patterns
Endin attern 1 is re laced with attern 2
Ex : (irukean) irukireanPattern 1 Pattern 2
Replaced
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Suffix Mapping of ending patterns withSuffix Mapping of ending patterns with
C ll i l A lC ll i l A l
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Colloquial AnalyserColloquial Analyser
Morphographemic changes
,
passed for morphographemic changePattern 1 Pattern 2
(thambi kittey) (thambi yidam)
Replaced
morphographemic
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Suffix mapping of ending patterns withSuffix mapping of ending patterns with
C ll i l A lC ll i l A l
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Colloquial AnalyserColloquial Analyser
one/two preceding characters
n ng pattern p s rep ace w t pattern p , a ter
checking one or two preceding characters.Pattern 1 Pattern 2
x :
(kaa nju) (kaa yndhu)
Replaced
Check one preceding character
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Compound WordCompound Word-- SemanticSemantic
RelationRelation
Identifying the metaphor words Identif in the characteristics of the com onents
Identifying the comparison relation between the
components This relation are extracted by using the Tamil
grammar of compound word ( - thogai), the
part o speec tag an semant c constra nts
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Compound WordCompound Word-- Semantic RelationSemantic Relation
Relations 14 are identified
(black hair) + ++ hair (noun + pof>head)
Black
Noun+icl>color
concept Semantic Constraint
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POS TaggingPOS Tagging
What is POS tagging?
Part-of-speech tagging is a process of assigning a part-of-speech like noun, verb,
pronoun, preposition, adverb, adjective or
other lexical class marker to each word in a
sen ence.
Tag sets for different languages
For Tamil , a tag set is formulated by aliterature survey a view of the standard tag set
for English language like Penn tree bank, wall
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oun a egory
N Noun NP Noun Phrase NN Noun + noun
SP Sub-ordinate clause conjunction Phrase SCC Sub-ordinate clause conjunction
Other category
NNP Noun + Noun Phrase
IN Interrogative noun INP Interrogative noun phrase PN Pronominal Noun
ar ar c e adj Adjective
Iadj Interrogative adjective Dadj Demonstrative adjective Inter Intersection
VN Verbal Noun VNP Verbal Noun Phrase Pn Pronoun PnP Pronoun Phrase
Int Intensifier CNum Character number Num Number 25
DT Date time ,
V Verb
Nn Nominal noun NnP Nominal noun Phrase
Verb category
VP Verbal phrase Vinf Verb Infinitive Vvp Verb verbal participle Vrp Verbal Relative participle
ux ary ver FV Finite Verb NFV Negative Finite Verb adv Adverb
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Characteristics by analyzing 4,70,000 wordsTamil take on more than one morphological suffix;
going up to 13.The role of the sequence of the morphological-
of-speech tag.
79 morpheme components were identified, which
combination of integrated suffixes
Using these morpheme properties we design a
Analysis of morphology of words and design of
morpheme components
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ChunkingChunking
What is Chunking?
Chunking is the task of identifying andegmenting the text into syntactically related
non overlapping groups of words.
Need for chunking
one of the important preprocessing for all other
aid to extract crux part of information fromsentences and documents
The chunk types are
ADJP, ADVP, CONJP, INTJ, NP, PP and VP.
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Our ApproachOur Approach
Our Approach
The morpheme features of words contributein identifying boundaries of chunking.
the features ,CRF model is designed.
Conditional Random Fields models for identifyingchunking boundaries
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words Transliteration POS chunk
[intha] B -NPState features
F(word(-2),Ctag)
Transition features
F(word(-2),word(-1), Ctag)
[thakavalin] I-NP [atippataiyil] I-NP [pOlicAr] B-NP
F(word(-1),Ctag )
F(word(0),Ctag )F(word(1),Ctag )
F(word(-1),word(0), Ctag)
F(word(0),word(1), Ctag)
[andthandtha] B-NP [mAvatta] I-NP [pOlish] I-NP
F(word(2),Ctag )
F(POS(-2),Ctag )
F POS -1 C
wor ,wor , tag
F(POS(-2),POS(-1),Ctag )
F(POS(-1),POS(0),C )
[cOthanaic] I-NP [cAvati] I-NP
F(POS(0),Ctag )
F(POS(1),Ctag )
F(POS(0),POS(1),Ctag )
F(POS(1),POS(2),Ctag ) - [vAkanac] B-NP [cOthanaiyil] I-NP
, tag
[Itupattanar] B-VP
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Named Entity Recognition (NER)Named Entity Recognition (NER)
Locate and classify atomic elements in text into
redefined cate ories
Proper names (people, organizations, locations)
expressions of time
monetary values
percentagesNeed for NER- Robust handling of proper names essential
for many applications
Pre-processing for different classification levels
Key part of Information Extraction system
Information filtering
Information linkin
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d ld l
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Indian language NERIndian language NER
use two levels of linguistic evidence to
e o m mo e g:
Context cues
Attributes to identify entities.
A standard list of attributes is maintained initially
s up a e - su a e earn ng a gor m.
Attributes are thus extracted and used to identify
NEs within the framework of the same s stem an
NER and its associated attribute extractor.
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N d E i R i iN d E i R i i
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Named Entity RecognitionNamed Entity Recognition
Challenges
Presence of a free word order
emma za on cu
Features
Postpositions
Case markers
PNG marker in Verb
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N d E i R i iN d E i R i i
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Named Entity RecognitionNamed Entity Recognition
For Persons:
Presence of titles and honorifics like , [thiru, thalaivar] Presence of suffices like [Ar]. , [Al] in the corresponding noun
phrase.
Presence of post-position , Presence of adjacent words like [ndakar]. [ndathi]. m va am
For Organizations:
Presence of adjacent words like [ndiRuvanam]. [thuRai] [ [For Time/Date:
Presence of adjacent words like [thEthi]. [ANtu]. m am
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Trainin data
Shallow parsing Semantic parsing Statistical processing
NE tableDictionary
Dictionary Entries
Clue
Extraction
Verb
Rules
Training data
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Steps in Expectation MaximizationSteps in Expectation Maximization
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Steps in Expectation MaximizationSteps in Expectation Maximization
Seed probability estimates
Related words Ordering
Smoothen the seed probability
Perform ambiguity resolution Maximized probability values
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Modified EM algorithmModified EM algorithm
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Modified EM algorithmModified EM algorithm
Two problems were encountered with the
traditional E-M al orithm:
Performed only positional analysis , and amodification was required for free word order
languages like Tamil
it was syntactically oriented, and modification was
The modification process called Quantum
entan lement solves both the above roblems.
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ExampleExample --
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En oc 0.49 . 0.01 0.01.
.
0.01
.
0.01
0.01
0.86
0.01.
Verb 0.01 0.01 0.12 0.92
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Vaanavil Tamil parserVaanavil Tamil parser
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Vaanavil Tamil parserVaanavil Tamil parser
Free word order
Simple sentences any number of nouns,
, ...
Clausal sentences identification using cue
wor s or su xesNested clauses
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3 Constituent Formation3 Constituent Formation
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3. Constituent Formation3. Constituent Formation
Two main components are noun and verb constituent
A noun constituent can contain only noun (Ex.
) or
can be of the following form
a ect ve a ect ve c ause a ect ve a ect veclause)* (adjective)* noun (case marker) (post position)
Ex.
or
noun clause Ex.
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ConstituentConstituent Formation (cont )Formation (cont )
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ConstituentConstituent Formation (cont..)Formation (cont..)
Verb Constituent :
(adverb clause)* (adverb)* verb (suffix)*
Ex.
.
2.
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ConstituentConstituent ormation in Simpleormation in Simple
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Words are grouped based on the function they perform.
. ect ves are groupe w t t e r nouns.
- Adjectives are adjacent to their noun.
- Adverbs can occur anywhere in the sentence prior to itsverb
x.
.
.
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Grouping of ClausesGrouping of Clauses
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Grouping of ClausesGrouping of Clauses
Distinguishing feature of the parser
cue suffixes or cue phrases (Ex Verbal
, , .Grouping is done by position of the cues
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Ex...
.
Noun clause :
Adjective clause :
Adverb clause :
Converted minimal simple sentence: .
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TreeTree GenerationGeneration
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Position of each word in the sentence is also
shown to take care of free word order
First the converted minimal simple sentence is
considered to generate the tree.
Ex.
The NCs and VC are expanded to generate the
tree for the actual input sentence.
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W lk th h f 4 h ith l
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Walk throu h of 4 hases with an exam le :
.
(After phase 1)
un en e a v (con) (N) (adv) (rpl)
(N) (N) (N) (vpl)(V).
(After phase 2)
a v con(N) (adv) (rpl) N N N v l(V).
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(After phase 3)
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(After phase 3)
( )
( ) .
( After phase 4)
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After expanding with the three clausesAfter expanding with the three clauses
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Word Sense DisambiguationWord Sense Disambiguation
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gg
The rocess of identif in which sense of a word in a sentence, when
the word has multiple meanings
Noun and Verb sense Disambiguation
ootstrapp ng uses orp o og ca u xes, , emant c
constraints and UNL relations (for verbs)
Noun
Verb
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ExampleExample Noun DisambiguationNoun Disambiguation
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Word 1: showing use of context for noun sense disambiguation
Example 1: aaru river, number, get cold, heal
Sense POS
number NounExample 1.1:
pandiyanaarupadaikal kondu por thoduthaan
action>
river NounExample 1.2:
Tamilnattin periyaaarukaveri aagum
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ng>
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ExampleExample Verb DisambiguationVerb Disambiguation
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Word 2: showing use of context for verb sense disambiguation
Exam le 2: adai arm disease offer create.
offer VerbExample 2.1:
akthan iraivanukku azan alai adaiththaan
create Verb
.
iraivan makkalai padaithaan
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Anaphora ResolutionAnaphora ResolutionAnna
University
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ppy
The problem of resolving what a pronoun, or a
Approaches en er ng eory rennan e a ,
Hobbs algorithm (Hobbs, 1978)
Applications
Summarization
Question AnsweringInformation Retrieval
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Anaphora Resolution in TamilAnaphora Resolution in Tamil
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Resolving Anaphora in Tamil Text
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Morphologically rich language orp o og ca su xes convey mos o e
semantic roles played in a sentence
se o as e as s or eman crepresentation
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Our ApproachOur Approach
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Classification of Anaphora Persons, Places andEvents
Centering Theory - modified by incorporating -Word level semantics - UNL Semantic constraints
Graph based approach - Sentence level semantics
- UNL graphsAbsence of Case suffixes have been handled
using UNL graphs
Plural and Event pronouns associated withmultiple antecedents - tackled using UNL
graphs
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Classification of AnaphorClassification of Anaphor
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Anaphora representing Persons
Person Ana hora - Nouns Noun hrases
avan, avaL, avar, ivan, ivaL, ivar and plural pronouns avarkaL
and ivarkaL
Raju nandraaka padiththaan. avan thervu ezuthinaan
Maanavarkal nandraaka padiththaarkal. avarkaL thervu
ezu nar a
Anaphora representing Places
Place Anaphora - Nouns, Noun phrases - athu, ithu
Adverbs such as angu and ingu can also acts as pronouns representingplaces
Examples
tiruchy tamilnaattin periya nagarangalil ondru. Ingu ammankovil uLLathu. ithil aayiram thooNkal uLLana.
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Anaphora representing EventsAnaphora representing Events
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Event Anaphora - Verb phrases, clauses and segments ofsentences
Pronouns such as athu, ithu with dative case, accusative case
represent events
swami aanmeega soRppozhivu nikazhthinaar. athaikaaNa makkaL koodiyirunthanar.
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Ambiguous PronounsAmbiguous Pronouns
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Pronouns such as athu, ithu can represent
Higher level of semantics and verbseman cs s nee e
Examples
maduraiyil meenatchi kovil ullathu. Ithilaayiram thoonkal uLLana.
madurayil ulla meenatchi kovilil aanmeekasorpozhivu nadaipeRRathu. Ithileera amaana ma a pange anar.
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Semantics IntegratedSemantics Integrated CenteringCentering
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Word level semantics UNL Semanticonstra nts
Anaphora classification
Filter out the non-anaphoric expressions
Filter out the non-referring expressions
ura pronouns as een tac e to certa nextent
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Anaphora Resolution using UNLAnaphora Resolution using UNL
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Plural Pronouns having multiple concepts asa ece e s
Event Pronouns
Two components
Use of UNL relations to extract the conceptsfor anaphora resolution
Co-ordinating UNL Relations
Sub-ordinating UNL Relations
Use of UNL subgraphs for anaphorareso u on
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Use of UNL Relations to extract theUse of UNL Relations to extract the
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Co-ordinating UNL Relations
Relations obtained for referring expressions
exactly matches with the relations obtainedfor anaphor
Sub-ordinating UNL Relations
Relations obtained for anaphor depends onthe relations of referring expressions
Rules agt obj
ben agt
plc aoj78Tamil Computing
CoCo--ordinating UNL Relationsordinating UNL Relations --
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raju ramuvai mirattinaan. avan payanthaan
Mirattu threaten Payam
(agt>thing, obj>thing)
obj
scare(obj>thing)
Ramu
iof> erson
agt obj
RajuAvan, he
(Pronoun)
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Subordinating UNL RelationsSubordinating UNL Relations --
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raamalingaththai annan sabapathi kaNdiththaar.
Anaal avar avarukku kattu adavillai.
an ppu, co
(agt>thing, obj>thing)
attuppa u
Abide (agt>thing, obj>thing)
Annan saba athi
obj
agt
agt
ben
Ramalin am
icl>person
Avar, he
He, pronoun
(iof>person) pronoun
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Event PronounsEvent Pronouns -- ExampleExample
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..Happen
icl>action agree
Ramalingam
iof>person
,
agt
to
Spiritual
aoj>thing
aoj
Avar Athu (kku)
mod
S eech
icl>talk
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Semantic InterpretationSemantic Interpretation ,
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,
concepts that the system can understand
The rocess of ma in a s ntacticall anal sed text of natural
language to a representation of its meaning
Semantic Interpretation - Aspects
Word meaning & Word Sense Disambiguation
Lexical Disambiguation
Structural Disambi uation Semantic Relations
Issues
Coreference and Anaphora
Lexical Semantics
Logical Semantics
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Applications of Semantic InterpretationApplications of Semantic Interpretation
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Information Extraction
ex rac ng mean ng u emp a es ex
Summarization
important inter relation between concepts
Question Answerin Extracting semantic similar sentences that are
answers to questions
Multilingual Generation & Machine Translation
Intermediate semantic representation
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Purposed WorkPurposed Work
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Semantic Interpretation of Tamil Text
se o as e as s or eman c
representation Use of UNL based information for NLP
processing
Use of UNL graph for Summarization and
Question Answering
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Semantic Relation (UNL) ExtractionSemantic Relation (UNL) Extraction
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Morpho-Semantic Rule Based Approach x s ng pproac es
Most UNL Enconverters use syntactic parser orp o-syn ac c ea ures
Use of rich Morphological features of Tamil for
seman c re a on ex rac on
Design of Rules based on Morpho-Semantic
Use of semantic constraint information from
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Enconversion Process
Identify possible UNL relations of a word Wi ass
Disambiguate the relations, if multiple unl
re a ons ass gne or a wor Identify the connected concepts with the word
i
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Morphology - Case suffixes associated with the word
Connective Natural Language word,
Co-occurrence
aamanaa seyyappa a u
POS Part Of Speech tag of the word
oun, er , ect ve, ver
Semantics
icl>person, iof>place, icl>time etc.
88Tamil Computing
Construction of UNL Graph usingConstruction of UNL Graph using
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wo asseswo asses
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Pass 1 Pass 2
90Tamil Computing
Our ApproachOur Approach -- BootstrappingBootstrapping
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Pattern representation Generic pattern to tackle
Features
POS
Matching
Partial matching
gnore up es w c o no a e par n
identifying semantic relations91Tamil Computing
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Features used for Probability estimation
Morphological Suffix
POS
Semantic Constraints
Startin and Endin s mbols
Relation between concept pairs can occur
Semantic Similarity based on UNL ontology
92
eature agge corpus tagge us ng ru e-
based approachTamil Computing
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93Tamil Computing
Question ClassificationQuestion Classification
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Need for QC & Answer types
question type and then map it to an expected
answer typea s e gges c y n e n e a es
Question Type: Q_LOCATION_CITY
overall accuracy of a question answeringsystem
Morpheme based CRF approach to Questionass ca on an xpec e nswer ype e ec on
94Tamil Computing
Factoid typeWho [ Who is India's prime minister ?]
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Factoid typeWho - ? [ Who is India's prime minister ?]When - ?
en n a ecame n epen en coun ry
Where - ? [Where was Gandhiji born?]Which - ?
Abbreviation - ... ?[What is the expansion of IAS?]
How - [] ?How does DC generator operate?
Who - ? [ Who is Manmohan singh?]Define - - [ Define Kirchoffs Law]
List typeEnumerate - .[ Enumerate districts in Tamil nadu]List - List out states in India
95Tamil Computing
Question ClassificationQuestion Classification, , , ,
REASON,OTHER
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REASON,OTHER
NUM AGE, AREA, CODE, COUNT, DISTANCE, FREQUENCY,, , , ,
PRICE, RANGE, SPEED, TELCODE, TEMPERATURE,
WEIGHT, LIST, OTHER
HUM ALIAS, DESCRIPTION, ORGANIZATION, PERSON,LIST, OTHER
OBJ ANIMAL, CITY, COLOR, CURRENCY, ENTERTAIN,
FOOD, INSTRUMENT, LANGUAGE, PLANT, RELIGION,SUBSTANCE, VEHICLE, LIST, OTHER
LOC ADDRESS, CITY, CONTINENT, COUNTRY, ISLAND,LAKE, MOUNTAIN, OCEAN, PLANET, PROVINCE,
RIVER, LIST, OTHERTIME DAY, MONTH, RANGE, TIME, YEAR, LIST, OTHER
By TREC96Tamil Computing
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Factoid Type Question Factoid Type QuestionAnsweringAnswering
97Tamil Computing
Our ApproachOur Approach
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Bag of key words matching.
In extracted assa e the terms that is in uestion are removed.
The remaining concept or entity terms may be answers.
Person - Named Entity, Possible case marker, Question word case
marker
Location - Considering possible case markers
Time - Temporal word database, number range
Quantity - Possible words in database
() - After question term as definition term -
98Tamil Computing
Sentence to extract predicate relationSentence to extract predicate relation
Sentences
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Wordnet
reprocess ng
Predicate Extractionre cate re at on
ExtractionA (x ,y)
Rule Dictionary
Ta ed Predicate rule
training
document
learning
The relation graph gives semantic relation
This semantic information provide filteringout the required Answer part
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De initional T e uestion De initional T e uestionAnsweringAnswering
100Tamil Computing
Definitional QA ProcessDefinitional QA Process
Due to the free word nature of Tamil the ranked sentences will not be the
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prcise answer for the question.
o e e n on erms rom e sen ences are ex rac e us ng some s or
patterns (K Soo Han,2007)( Jinxi Xu, 2003) as given below.
lpiRanthAr
Am Andu
Am mAtham
ivarathu thanthaiyAr < father Name>, thAyAr
Am Andu maRainthAr
The leaf nodes of the answer graph give the details presented in the
sentence.
The definition answer has been created using the definition templates.
Use of statisticall rocessed seed information for classification
and scoring of sentences for inclusion in the answer graph
representing the definitional answer to who questions101Tamil Computing
arge erm ame o erson
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WEBDocument retrieval
us ng
Sentence classificationStatistical processing
Sentence tagging based on seed information
Definitional
Sentence
corpus Seed Information
Sentence rankingTerm Probability
Definition term extractor
Definition
102Tamil Computing
Sentence ClassificationSentence Classification
S. No Category Features( ) n dT W tN
=
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S. No Category Features
1 Birth (piRappu)( )
N
(piRanthAr)(thOnRiNar)2 Parent (peRROr)
nd= No. of documents,
in which the term t
occurred (thanthaiyAr)3 Education (kalvi)(padippu)
(
N= Total number of
documents
bt+=
(pa4 Work (paNi)(vElai) viruthu(parisu)6 Death (iRanthAr)(maRainthAr)where w is the weights vector of7 General (pErasiriyar)(vinjAni)(arasiyalvAthi)
features, b is intercept
103Tamil Computing
1931 15 . .
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. .
. . .
.1981 . .
1997 . . . .
. .
.
.
. . . . .
104Tamil Computing
.
. .
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. .
< > < >.1981 . 1990 . 1997 .
. . . . . .
. .
. < > < > .
. .
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1931 15
. .
. 1981 , 1990 , 1997 .
.
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Tamil Document SummarizationTamil Document SummarizationUsing Semantic Graph MethodUsing Semantic Graph Method
108Tamil Computing
Our WorkOur Work
Capturing semantic features of the
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Capturing semantic features of the.
Identifying key concepts and relations for.
Using machine learning model to identify
semantic graph.
109Tamil Computing
DetailedDetailed DesignDesign
Linguistic Analysis
SEMANTIC GRAPH GENERATION
EXTRACTING SUMMARYSENTENCES
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g ySyntactic and Analysing &
Semantic analysis Logical Form
Parsing
Co reference ResolutionSUB GRAPH
Linguistic
Analysisfor Named Entities
Identification of
Named EntitiesFeature Set
Identification
ApproachCoreference Resolution
Linguistic & SemanticGraph attributes,
Document discourse
structure
Semantic
Normalization
WordNet
Learning Algorithm
Construction of
Semantic Graph SVM110Tamil Computing
After training the learned model is used to predict theimportant nodes of the given documents semantic graph.
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important nodes of the given document s semantic graph.
ent cat on o u rap
The sub graph of the large semantic graph is generated
Extraction of Summary Sentences
graph are extracted from the input document.
111Tamil Computing
Sample Input DocumentSample Input Document
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112Tamil Computing
Morphological Analyzer OutputMorphological Analyzer Output
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113Tamil Computing
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114Tamil Computing
Graph GenerationGraph Generation
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115Tamil Computing
Identification ofIdentification of SubSub--GraphGraph
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116Tamil Computing
Extraction of Summary SentencesExtraction of Summary Sentences
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117Tamil Computing
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Tamil Summary Generation forTamil Summary Generation fora Cricket Matcha Cricket Match
Tamil Computing 118
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To propose a framework for automatic analysis and summary
generation for a cricket match in Tamil, with the scorecard of.
The framework proposes a method to evaluate the
interestingness of a cricket match.
The framework proposes a customization model for the
summary.
e ramewor a so proposes met o s or eva uat ng t e
humanness of the generated summary.
a a n ng an na y cs a a n ng an na y cs
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Modified version of Apriori algorithm is used to find the
association rules from the feature vectors.
performed, CoV is plotted against average to give an idea
about how consistent the player is.
The interestingness of the match is calculated based on theweighted average of the scores assigned to the factors
Individual records made, High run rate, Series state, Relative
position in international ranking, Reaction in social
ne wor s e c.
Sentence GenerationSentence Generation
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The sentence which is the most apt to the current event under
consideration is selected The vocabulary used in the sentence and the depth to which an
event is discussed is also varied based on the expert level of the user
generator along with the desired case endings and the generated
variants are added to the sentences.
The system uses the morphological generator developed at TaCoLa
+ =
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Tamil Computing 122
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Clustering for news eventClustering for news eventdetectiondetection
Tamil Computing 123
Why UNL context Cluster?Why UNL context Cluster?
Identifying semantic coherence between
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two sentences is based on overlapping of
terms between the sentences. In news paper article, the term overlap
between two sentences is minimal.
Each sentence can have more than one
-
sentences.
Tamil Computing 124
UNL based context clustering for news event
etect on- vent na ys s
In natural lan ua e text, an event anal sis involves
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discovering the portion of text in a sentence that describes
an event par c pan s o e even
the actual event occurrence and time of the event.
All these event s ecific ro erties are obtained from
UNL(Universal Networking Language) representation.
These properties help in separating the event sentences with
- .
Contribution
the concept distance score as well as the TF/IDF score.
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Snapshot for Tamil News Event Search
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L rL rGenerationGeneration
Tamil Computing 128
Lyric MiningLyric Mining
We have processing using 2,000 lyrics
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Word level analysis
Concept co-occurence analysis
easan ness score This analysis has been mainly used in the lyric
generation and computing freshness scoring for
lyrics.
Lyric MiningLyric Mining
Word Level Analysis The fre uenc of words is used to associate a
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popularity score for each word.
Po ularit score of the word has beenidentified from lyrics.
In l rics the words are attached with suffix.
Root words - determine its frequency count.
Lyric MiningLyric Mining
Word Level Analysis - ResultsWORDS USAGE
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Lyric corpus of two thousand songs
were analysed for the word, rhyme
1153
and Co-occurence concepts usage. 793 965 857
List of top 5 usage words
in lyrics
Lyric MiningLyric Mining yme eve na ys s
Adapted Apriori Algorithm
requency coun o r yme a era on an
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requency coun o r yme, a era on an
end rhyme pairs of Tamil lyrics
EDHUGAI USAGE
, 2291, 3338
, 2028,
1973
,, 2947
, 1952,
, 2480
a op usage wor r yme
b) top 5 usage word
Alliteration
Lyric MiningLyric Mining
Concept Co-occurence Analysis
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from a lyric corpus Agaraadhi, an on-
Cancelling the ambiguous and the
po ysemy o wor s o mprove eaccuracy of the entire system. Example : The word whichhas the concept , , , ,, ,
Lyric MiningLyric Mining
Identify the pleasantness of a word based on 5
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3 models Language independent
2 models Language dependent
In all the models, first the given grapheme
word is converted into phoneme form using
. Models
Language Dependent Model I
Language Dependent Model II
Manner of articulation based model
Manner and place of articulation based model
Lyric MiningLyric Mining
Meaning based Model
Maintain the pleasant and unpleasant word
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Maintain the pleasant and unpleasant word
list Calculate the frequency of phoneme in
pleasant and unpleasant word list
Language Dependent Model I Judge the plesantness based on Vallinum,
Mellinum Idaiyinam classification,
Maathirai and kurukkams except
Language Dependent Model II
Lyric MiningLyric Mining
Manner of articulation based model
Category Manner of Articulation Phoneme
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Category Manner of Articulation o e e
reater oug etro ex, r ,
, , , , , ,
Intermediate Semivowels, Approximants ,,,,
Soft Nasal ,,, ,,
Lyric MiningLyric Mining
Manner and place of articulation based
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place of articulation score, categories which
arise from the arts near the oral cavit areconsidered pleasanter than those which go deeper.
Taking manner of articulation into consideration,
Nasals are given highest sweetness scorefollowed by Laterals, Fricatives, Stops and Trills.
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Tamil Computing 138
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COREECOREE The Conce t basedThe Conce t basedSearchSearch EngineEngine
139Tamil Computing
Components of a Search EngineComponents of a Search Engine
Crawler (or Worm or Spider)collects a es
checks for page changes
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checks for page changes
Indexerconstructs a sophisticated file structure to
enable fast page retrieval
SearcherSearches the indexed information that
satisfies user queries
Ranks output
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Search Engine ArchitectureSearch Engine Architecture
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141Tamil Computing
The Concept based SearchThe Concept based Search
Indexes concepts instead of words
Indexes concepts and relations etween concepts
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In this work
epresen ng e ocumenUse ofUNL ( Universal Networking Language) as intermediate
structure
cons s s o concep s an re a ons
Three indices Concept-Relation-Concept, Concept-Relation, Concept
Query converted into UNL representation
Searching and ranking based on concepts &
142Tamil Computing
COREECOREE--Archit ect ureArchitectureUNL IndexThesaurus
Input Processing
IL QueryQuery Expansion
Query Translation
[UNL Encoding]
UNL Expressions/
UNL GraphMorphological
Based listParsed
Query
UNL basedranking
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Light Weight
WSD NERMWE
List UNL BasedSet of
Documents
ranking
WWWUNL
Matching With UNLExpressions
UW List
Document Processing
Focused
Document
Processing
Indexing
Selection ofDetailed
Web Crawler using
Semantic
Approach
Tamil to
UNL Converter
UNL
Expressions
For Indexing
Expression
WSD NER
List
MWE
List
Searching
143Tamil Computing
Modules of COREEModules of COREE
Focussed CrawlingUNL based Document Processin
Sentence Extraction
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Enconversion
UNL based Input Processing
Query Expansion
uery rans a on
UNL based Searching
Matching and Ranking
UNL based Output Processing
Information Extraction Summar Generation
144Tamil Computing
Document ProcessingDocument Processing
WSD NER
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Extraction ofComponents
Enconversion(Concept &
Tamil
Document
UNL
Expression/Graph(multi-list)
o sen ences(TF based)
Tamil UW
list
Tamil Enconversion
Rules
145Tamil Computing
UNL ListsUNL Lists
UWList Universal Word List
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146Tamil Computing
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UW concepts are
iof>city
v aplf
p
the semantics of the concepts (iof>city)
147Tamil Computing
MULTILISTMULTILISTConce t
Head Node
Nodes
Nodes To Concept
Nodes
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148Tamil Computing
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149Tamil Computing
ExampleExample check fontcheck font --balajibalaji
Concept
Onl
Concept C t
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p
Relation -
Concept -
Concept
150Tamil Computing
The Index StructureThe Index Structure
Input - set of UNL graphs as a MultiList datastructure
Output are UNL indices stored in three BinarySearch Trees
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Search Trees.
an inverted list on the indices.
The indices are categorized into three different
ypes - o a re r eva o seman ca y re evandocuments CRC (Concept -Relation- Concept) Indices
CR (Concept -Relation) Indices
C (Concept Only) indices
151Tamil Computing
Query TranslationQuery Translation
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[s][w]; vivekanandar; iof>person; Entity; 1
pos; lecture; icl>action; Noun; 2[/w][r]
[r]2 pos 1[/r][/s]
152Tamil Computing
Query ExpansionQuery Expansion
NER WSD
Parsed
InputQuery
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Parsed
Quer
Query
MorphologicalProcessing
Query Expansion(indexbased/verb-noun
Expanded Query
pairs)
oma n spec cNoun verb pairs
na yzeIndex table
Other tools to be Integrated
153Tamil Computing
Query ExpansionQuery Expansion
Query Word Query word WithExpanded word
Relation
< - pos> < - pos>
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< pos>
< - pos> < - pos>
< - pos>
154Tamil Computing
SearchSearch
Indexed Concept,
UNL Based
Concept-relation and
Concept-Relation-Concept
Performing various levels of
Expanded
query
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UNL Based
Indexing UNL Based
Performing various levels of
matching
UNLquery
Exact match(CRC)Concept-RelationMatch
ase
RankingConcept OnlyMatch
Ranked set ofdocuments(O/P)
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Example Result Snap Shot in 5 WindowExample Result Snap Shot in 5 Window
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Actual query term Match Actual query term+Concept Match Conceptual Results
Single Term Match Expanded Term Match Results
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--DICTIONARY FRAMEWORKDICTIONARY FRAMEWORK
158Tamil Computing
OBJECTIVESOBJECTIVES
,
indexing and retrieving Tamil words, their
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, .
Framework to incorporate various unique
-
information to the user regarding the word that
.
159Tamil Computing
GARAADHIGARAADHI RAMEWORKRAMEWORK
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160Tamil Computing
Agaraadhi - Features
FeaturesFeatures
1. Morphological Analyzer 1. Lyric Related
2. Morphological Generator 2. Kural related
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3. S ellin su estion.
4. Equivalent word
.
4. Pleasantness score
5. Picture Dictionary 5. Bharathiyar Songs
. .
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Agaraadhi Meaning for the Word pookkalAgaraadhi Meaning for the Word pookkal
(example for case ending word)(example for case ending word)
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KuralagamKuralagam -- Concept Relation basedConcept Relation basedSearch Engine forSearch Engine for ThirukkuralThirukkural
164Tamil Computing
ec vesec ves
Thirukkural based on UNL Framework.
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Searching with keywords in kurals andintepretations
Concept based search based on CoReX conceptual
Bilingual search English and Tamil
165Tamil Computing
Kuralagam FrameworkKuralagam Framework
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166Tamil Computing
Online ProcessingOnline Processing
Search and Rankingfetches the Thirukkural number and its details.
Thirukkurals for a given query are fetched using the two
types of concept relation indices namely CRC and C.
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yp p y
The query concept is expanded using related CRC indices
pointing to the query concept.
the query not possible with key word Thirukkural searchengines.
The ranking is based on
priority to the indices in the order CRC>Cusage score
frequency occurrence of the query concept167Tamil Computing
KuralagamKuralagam
results for the query word paNamresults for the query word paNam
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KuralagamKuralagamconceptual results for the query word paNam isconceptual results for the query word paNam is
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KuralagamKuralagamMeaning for a particular kuralMeaning for a particular kural
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Tamil Word GameTamil Word GameMiruginajumbo (Jumble words)Miruginajumbo (Jumble words)
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Tamil Word GameTamil Word Game
Kattaboman (Scramble words)Kattaboman (Scramble words)
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Tamil Word GameTamil Word Game
Thookku Thookki (Hang man)Thookku Thookki (Hang man)
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COREEA r dhi
Tamil Language Based Games
Tamil Computing 176