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Machine verification and identification of telugu metrical poetry 1.1


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Telugu Metrical Poetry.

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Machine verification and identification of telugu metrical poetry 1.1

  1. 1. Machine Verification and Identification of Telugu Metrical Poetry (Chandassu) Dileep Miriyala
  2. 2. Agenda Chandassu & padyam Need of Machine based Verification & Identification of Chandassu How it Works? Demo of chandaM © Case Studies What’s Next?
  3. 3. Chandassu  Chandas Sastra is a literature framework with set of rules to be followed to write a poem or prose.  Chandassu was first used in Vedas.  Telugu Chandassu was derived from Sanskrit but it has its own Set of rules.  The literary work that followed chandassu framework in Telugu is referred as padyam. SKIP
  4. 4. Features of a Chandssu  The features that define a chandassu are  gana structure  yati  prAsa  prAsa yati
  5. 5. Guru and Laghu  Syllables are classified into guru and laghu  Symbols associate with guru and laghu are U I  Other Symbols that are in-usage are Dot, Dash and Inverted U etc.  The classification of the Syllable is based on the time takes to pronounce the given syllable  Laghu syllable takes 1unit ,  Guru Syllable takes 2 units.  Syllable classification is also based on the position of the Syllable in a given word.  Ex:  Independent అ  Laghu I  But the word అమ్మ to have Sequence as UI  Chandas Sastra defines these rules.
  6. 6. gana  Sequences of Guru and laghu will form a gaNa.  Ganas are classified as  Named gana (Akshara)  The Symbol Sequences with a length of 1,2,3 are given with a name. Ex: la, ga, va, ha, ya, ma, ta, ra , ha , bha , na , sa  Compound gana: The sequences of named ganas to form compound ganas. Ex: la-la means II  Grouped gana  Set of Named Gana’s and Compound ganas are classified as Grouped ganas  Matra gana  Ganas are classified based on the total time takes to pronounces  Ex: la-la takes 2 units of time.
  7. 7. yati –prAsa- prAsa yati  yati  Is a position at which the word break place in Sanksrit Chandassu  Is a similar or yati friend syllable to the 1st Syllable of a pada  prAsa  Is usually 2nd Syllable or last syllable of a pada.  Same or prAsa friend syllable to be maintained at each line.  prAsa yati  It’s a relaxed feature when poet is not able to apply prAsa in some chandassu’s  yati and prAsa to form a group and a prAsa yati friendly group to be formed at yati position.
  8. 8. Classification of Chandassu’s gaNa Structure yati prAsa prAsa yati Jati Grouped gaNa ✓ ✓ upaJati Grouped gaNa ✓ ✓ ✓ vRutta Named gaNa ✓ ✓ matrA matra gaNa ✓* ✓* *Optional in many cases
  9. 9. How many?  Chandassu uses Binary symbols (U,I) to represent a sequence of Syllables.  There is no restriction on No. of Syllables per line or poem (padyam).  The no. of Sequences that can be formed With upto n Syllables are  21 +22 +23 +…. +2n=2n+1-2  Chandas Sastra named Chandassu’s up to 26 Syllables.  Ex: gayatri Chandassu means Any Sequence of 6 Syllable Symbols*  udduramala Chandassu is a name given to a chandassu with >26 Syllables.  Simply we can say the total possible Sequences are infinite.
  10. 10. How many?  If n=26 then total possible Sequences are 227-2=13,42,17,728.  Chandas Sastra defined very few of them around 2000 sequences only.  Ancient Telugu poets frequently used 30 Telugu Chandassu’s.  Ancient Sanskrit poets used more than 1200 Sequences but less than 1500 *  Many poets create their own Chandassu's in our time too.
  11. 11. Why?  The Quantity of Literature written under Chandassu framework got reduced by large amount.  This era of Digitization  Tools for publishers: To ensure the quality  Tools for Learners: To learn in a easy and interactive way.  Tools for professional poets: To experiment in new sequences and reducing effort of computation and validation against rules.  Tools for Language study and Analysis: To understand and distinguish the poets style and Language, vocabulary etc.. at that time. What If a tool can do all these related to Padyam?
  12. 12. ChandaM  Chandam © or ఛందం © is such a first generation chandassu tool    Basic Objective  Should verify the given padyam against a given chandassu  Should Identify the chandassu of the given padyam along with the errors (i.e. violated rules).
  13. 13. Matching Engine Raw Input White Listing Syllable Chunks Prepare laghu-guru Stream Extract Features Chandassu Match Features Match Result Text Analyzer Matcher
  14. 14. Matching Engine Raw Input White Listing Syllable Chunks Prepare laghu-guru Stream Extract Features Chandassu Match Features Match Result Text Analyzer Matcher
  15. 15. White Listing  Digitization might involve references to sources and sometime foreign characters.  Telugu[Targeted Language] Unicode Sub-Range characters will be filtered along with few identified punctuations.
  16. 16. Matching Engine Raw Input White Listing Syllable Chunks Prepare laghu-guru Stream Extract Features Chandassu Match Features Match Result Text Analyzer Matcher
  17. 17. Syllable Chunks  Building of Syllable Chunks are necessary to create Laghu-Guru Stream.  Any Syllable Extraction Mechanism can be used.  Ex:కొత్త  కొ , త్త
  18. 18. Matching Engine Raw Input White Listing Syllable Chunks Prepare laghu-guru Stream Extract Features Chandassu Match Features Match Result Text Analyzer Matcher
  19. 19. Laghu-Guru Stream  Each Syllable group will be assigned to a Symbol (U,I)  All laghu syllables will be checked for the influence of next syllable on it or not.  Ex: కొత్త కొ  [ I,1,0] త్త  [ I,1,1]  [Current Symbol, Check for next Syllable Influence, Can influence prev. Syllable]  i.e. కొ, త్త U, I
  20. 20. Matching Engine Raw Input White Listing Syllable Chunks Prepare laghu-guru Stream Extract Features Chandassu Match Features Match Result Text Analyzer Matcher
  21. 21. Extract Features Pairs parserGana parser Extract Features
  22. 22. gaNa Parser  Based on the target gaNa Characteristic Symbol Stream will be parsed.  Ex: U||U||U||U  Named gaNa:  Above gaNa sequence can be parsed as bha-bha-bha-ga or gala-laga-lala-gala-laga or many other  While parsing the Symbols next expected gaNa’s threshold will be considered.  Say for the above sequence feature is defined as bha-bha-bha-ga then threshold would be 3-3-3-1.  bha-bha-bha-ga
  23. 23. gaNa Parser  Grouped gaNa  Incase of Grouped gana’s expected threshold is not constant  Immediate Symbol Sequence is when expected group found or Symbol at which Max Threshold reached is considered as the gana.  Ex: U|UU|  U|- UU| Surya-Indra Min Threshold Max Threshold Surya (Brahma) 2 3 Indra (Vishnu) 3 4 Chandra (Rudra) 4 5
  24. 24. gaNa Parser  Matra gaNa  Immediate Symbol Sequence is found with expected Matra count reached or Exceeded.  Ex: : U|UU| can be parsed as UIU-UUI when expected matra gaNa is 5-5
  25. 25. Pairs Parser  yati, prAsa, prAsa-yati are the pair of syllables.  These will be extracted based on the position of yati  Position of yati  Usually a absolute number incase of vRutta’s  Ex: 10th place means 10 Syllable in each line.  Relative position from a given gaNa.  Ex: 1st Syllable of 3rd gaNa.  Ex: Last Syllable of 3rd gaNa.  Pairs of 1st and nth syllable extracted will be created for each line along with their previous Syllable,  prAsa:  prAsa is usually the 2nd or last syllable of each line.  Hence array of prAsa will be created with previous syllable too.  Previous Syllable has important role since it can influence the validity of the Yati, prAsa , prAsa-yati pairs
  26. 26. Matching Engine Raw Input White Listing Syllable Chucks Prepare laghu-guru Stream Extract Features Chandassu Match Features Match Result Text Analyzer Matcher
  27. 27. Match Features  Extracted Features (gaNa’s and Pairs) will be matched against Expected feature.  A Scoring System is defined to find the match percentage.  -1 → Key feature not found or mismatched.  0 → Feature not found or mismatched  +1 → Feature found and matched.  +2 → Key feature found and matched exactly.  Customised Scoring Systems are open for experiments.  Percentage of Match or Confidence (Sum of all features gained Score)*100 ____________________________________________ ((2*No. of Key features) + No. of Normal Features);
  28. 28. Match Results  Match Results may be delivered based on the user needs  HTML, PDF, Excel, TEXT etc.  Mismatches will be presented as Errors  Match score will be presented as Confidence of Matching.
  29. 29. Sample Result [HTML]
  30. 30. Chandassu Identification  Why?  To Determine the Chandassu of a unknown padyam.  To find the multiple matches if any.  Resolving the conflicts.  Mechanism  Match each and every chandassu against a given padyam  Identifying Chandassu for which the Max Score is obtained.  Can be applied only on Known Chandassu’s  To determine the Sama/Vishama pada Chandassu’s is also possible [Not in Scope]
  31. 31. Sorted Results Known Candidates Each Candidate Matching Engine Chandassu Identification
  32. 32. Identification Engine  Need of Optimization  Running Matching Engine on all known Chandassu’s could take a longer time.  Ex:  Consider the Known Chandassu’s size 400 (Incase of Telugu)  Total Avg. Time takes to match Features of a given Chandassu is 40-120 Milli Seconds.  Total time to Identify is 40*400 to 100*400 i.e. 16 Sec. to 40 sec. Size Min Time Max Time Telugu/Kannada 400 16 Sec. 40 sec. Sanskrit -1200 1200 48 Sec. 120 sec.
  33. 33. Identification Engine  Eliminating redundant steps and Caching the results  Results of the Text Analysis will be cached.  Determining the Eligible Candidates  Find Syllable Count for each line Ex: 7, 12, 8, 15  Find the Range of Syllable Count i.e Min and Max Values Ex: 7-15  Find all the Candidates which fall under this Range Ex: 7-15 #If the Digitization has Errors Syllable count may not match the actual.  Extended Range will be calculated i.e. Say t% Digitization Errors. Floor(X1*((100-t)/100)) to Ceil(X2*((100+t)/100))  X1=Min Value, X2=Max Value.  Extended Range would be Ex: 6 -16.
  34. 34. Identification Engine Raw Input White Listing Syllable Chunks Prepare laghu guru stream Extract Features Match Features Match Result Result Available Candidates Eligible Candidates Each Candidate Match Results Text Analyzer Matcher Identifier Range Extractor
  35. 35. Sample Evaluation (Sorted Scores)
  36. 36. Performance Non Optimized Identification Engine Optimized Identification Engine Padyam -1 523 m.s 56 m.s. Padyam-2 186 m.s. 82 m.s Padyam-3 346 m.s 116 m.s.
  37. 37. Demo Of Chandam
  38. 38. ఛందం© యొక్క శక్తి దెప్పర మగు కాలముచే నెప్పపడు దేవతల కెలల నష్టం బగు నీ యొప్పపదముుఁ గృష్ణుఁ డరిగినుఁ దప్పుఁ గదా! తల్లల ! నీవు తలలడప్డుఁగ్. ప్ై ప్దయం భాగవతం లోని మొదటి స్కందం గోవృషభ సంవాదంబు(33 వ ఘట్ట ం) లోని ప్దయం(#397). “ ” JUMP to Questions
  39. 39. ఈ ప్దాయనిి ఛందం© తో గణంచినప్పడు వచిిన ఫల్లతం ఛందం© శక్తి JUMP to Questions
  40. 40. ఛందం© యొక్క శక్తి ఇక్కడ రండవ పాదం లో ‘ నె’ కు ‘ న’కు యతి మైతిి కుదరదు అని ఛందం© చెప్పపంది. టైప్పంగు తప్పపదమేమో అని ప్పస్తకానిి చూడబోతే తెలుగు సాహితయ అకాడమీ వారి ప్రచురణ లో పాఠ్యం అలానే ఉంది. మరో ప్పస్తక్ము లోనూ అంతే. JUMP to Questions
  41. 41. ఛందం© యొక్క శక్తి ఇది మరేదెైనా ప్రత్యేక్ యతిగా కూడా అనిప్పంచలేదదు. మరో ప్రచురణ ప్పస్తక్ం: తిరుమల తిరుప్తి దేవసాా నం వారిది చూడగా నషటం అనేది స్రైన పాఠ్యం కాదని, నిషటం అనేది స్రైన పాఠ్యం అని త్యలంది. JUMP to Questions
  42. 42. ఛందం© శక్తి స్రైన పాఠ్యం తో ప్దాయనిి ఛందం© తో గణంచినప్పడు వచిిన ఫల్లతం JUMP to Questions
  43. 43. ఛందం© ముఖచిత్రం JUMP to Questions
  44. 44. ఛందో గణనం JUMP to Questions
  45. 45. గణన ఫలతాలు JUMP to Questions
  46. 46. గణన ఫలతాలు ఇక్కడ మనం రండవ పాదంలో ఒక్ గణం తకుకవగా ఉండడానిి ఛందం © స్రిగాా ఎతితచూప్డానిి చూడవచుి. JUMP to Questions
  47. 47. ఇక్కడ మనం ఒక్ట్వ పాదం దోష్పూరితం అని అరధం చేసుకోవచుి. గణన ఫలతాలు JUMP to Questions
  48. 48. గణన ఫలతాలు రండవ పాదం లో పాా స్ యతిని స్రిగాా గురితంచలేడానిి కూడా గమనించలేవచుి. ముందు ప్దయంలో ఛాయనొస్గు బదులు ఛాయననొస్గు అని ఉండడానిి గమనించలేవచుి. JUMP to Questions
  49. 49. క్ంప్యేటరు వాాసిన క్ంద ప్దేం క్ంద ప్దయమే కాక్ అనిి తెలుగు ప్దయ ఛందసుులలోనూ కూడా ప్దాయలు వాా యగల్లగే సామరధయం ఛందం © కు ఉంది. అయితే స్,రి,గ,మ,ప్,ద,ని లతో మాతరమే JUMP to Questions
  50. 50. ష్ణ్మాత్రా శ్రరణ లో వాా యదగా ప్దయ ఛందసుుల శోధనా ఫల్లత్రలు ఛందస్సుల శోధన JUMP to Questions
  51. 51. మూలాయంక్నం అనేది ఛందం© ఛందసుును క్నుగొనడంలో అనుస్రించే ప్దదతి. ప్దాయనిి ప్రతీ# ఛందసుుతోనూ గణంచి ఏ ఛందసుు యొక్క నియమాలను ఎకుకవ శాతం స్ంతృప్పత ప్రిచిందో ఆ ఛందసుును ఆ ప్దయ ఛందసుు గా గురితసుత ంది. # ఏఏ ఛందసుులు గణనానికి ఎనుికోబడాద యో ఏ ఛందసుుకు ఎనిి మారుకలు దదా శాత్రలు వచ్చి యో మూలాయంక్నం లో చూసుకోవచుి.
  52. 52. కొత్ి ఛందస్సు సృష్టట 'గోవంద' అనే ఛందసుును ఎంత సులభంగా నిరిాంచుకోవో ి చూడండి. దీనిని శ్రీ బెజ్జా ల మోహనరావుగారు నిరిాంచినారు. JUMP to Questions
  53. 53. కొత్ి ఛందస్సు సృష్టట నిరిాంచిన ఛందసుు యొక్క లక్షణ్మలను ఛందం© అరధం చేసుకొని, ఇతరులతో ప్ంచుకొనేవధంగా ఎంత వప్పలంగా చూప్పంచిదో చూడండి. ఇంతే వప్పలంగా అనిి తెలుగు , స్ంస్ృత ఛందసుుల లక్షణ్మలను కూడా చూప్పసుత ంది. JUMP to Questions
  54. 54. కొత్ి ఛందస్సు సృష్టట నూతన ఛందసుులో వాా సిన ప్దాయనిి కూడా ఛందం© చలేక్కగా గణంచలేడానిి చూడవచుి. JUMP to Questions
  55. 55. Case Studies-1 Telugu Bhagavatam  Sri U. Samba Siva Rao Digitized Telugu Bhaghavatam  Total Padyams & 10061(7400) with 900K Words 16K unique Words  Total time taken run 7400 Padyams is 18min ~= 150ms. Per Padyam  After running this on Chandam results with 70% confidence. Percentage Result Examples 40 Human errors Spelling mistakes or misplaced punctuations 30 Human errors Misplace spaces and punctuations. Treating of Compound words as independent and Vice-versa 30 False Alarm Due to Limitations of the Tool.
  56. 56. Case Studies-2 Poets & Usage  Poets primary or intermediate skills at Writing Padyam's Credited tool on various forums  Improved Quality  Focusing more on Creative and Literature part  Computation is Outsourced.  Poets who mastered in writing Padyam’s  Experiment and Practice (or learn) new patterns.  Around 30-40 regular Telugu Poets are using Chandam  Avg. Poet Computations per Day:3-5
  57. 57. Case Studies-3 Research  Mr. M. Narasimha Rao & I started analyzing Annamaya kIrtaNa’s  To find if there any influence of Chandassu in his writings  To compute the Statistical Analysis of Chandassu Pattern's.  Mr. Sri Ganesh T working on Determining Author Style in Metrical Poetry.
  58. 58. Limitations  Handling Special Rules  In Determining Symbols Ex: అదుర చు, క్దుర చు  yati matching when there is Sandhi  ఆట్గా ఛందాలనల్లలంచి, యలరించి where ఛందాలనల్లలంచి = ఛందాలను+ అల్లలంచి #Yati matching based on achchu
  59. 59. What’s Next?  Resolving the Lines  Ancient poets used write the wrong padyam in single line.  Some cases No Indicator of Line Break and Chandassu Name.  Makes difficulty in determine the Chandassu.  With Little customization to Identification Engine can be resolved easily.  Discovering the Art Forms  Bandha or citra kavitva’s  Configured for Kannada Chandassu’s too. [Alpha Version]
  60. 60. Technologies.  Runs on WEB Client and Server , Windows Client Application  JS API is available for integration with external Sites.   Technologies  JAVA Script via Script #  HTML5  MONGO DB  C SHARP  ASP.NET  Ports for Java/PHP can contact me for collaborative working,
  61. 61. Dileep Miriyala  Contribution to Indic Languages:  Indic PDF  Telugu Bhaghavatam  Chandam :  7 Keyboard Layouts for Telugu [Web/Windows/Mac]  Importable Mac Keyboard Layout on Windows  ASCII to Unicode Fonts (Not TEXT Conversion)  Some Works in progress  Sandhi Merger and Identifier  Spell checker  Content Clustering
  62. 62. Questions?  Contact   Phone +91-8978559072   