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Exploiting FrameNet for Content-Based Book Recommendation Orph ee De Clercq , Michael Schuhmacher, Simone Paolo Ponzetto and V eronique Hoste LT 3 , Language and Translation Technology Team Ghent University orphee.declercq@ugent.be


  1. Exploiting FrameNet for Content-Based Book Recommendation Orph´ ee De Clercq , Michael Schuhmacher, Simone Paolo Ponzetto and V´ eronique Hoste LT 3 , Language and Translation Technology Team Ghent University orphee.declercq@ugent.be CBRecSys, 6 October 2014 De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 1 / 26

  2. Tastes differ De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 2 / 26

  3. Tastes differ De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 2 / 26

  4. Tastes differ De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 3 / 26

  5. Tastes differ De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 3 / 26

  6. Content-Based Book Recommendation Could techniques developed for NLP help? Books • Chronological sequence • Actions and Events • Infer more based on actual content = plot De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 4 / 26

  7. Content-Based Book Recommendation Could techniques developed for NLP help? Books • Chronological sequence • Actions and Events • Infer more based on actual content = plot Semantic text processing • Semantically enriched text features • Text-external = Linked Open Data (LOD) • Text-internal = semantic frame parsing De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 4 / 26

  8. Content-Based Book Recommendation Could techniques developed for NLP help? Books • Chronological sequence • Actions and Events • Infer more based on actual content = plot Semantic text processing • Semantically enriched text features • Text-external = Linked Open Data (LOD) • Text-internal = semantic frame parsing → Add and combine semantic information De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 4 / 26

  9. Overview 1 Frame-Enhancement 2 Experiments 3 Results 4 Conclusion De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 5 / 26

  10. Frame-Enhancement Frames Frame semantics (Filmore, 1982) Describe the meaning of a sentence by characterizing the background knowledge required to understand it. De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 6 / 26

  11. Frame-Enhancement Frames Frame semantics (Filmore, 1982) Describe the meaning of a sentence by characterizing the background knowledge required to understand it. Frame: KILLING The KILLER or CAUSE causes the death of the VICTIM. KILLER John drawned Martha. VICTIM I saw heretics beheaded. CAUSE The rockslide killed nearly half of FEs the climbers. INSTRUMENT It’s difficult to suicide with only a pocketknife . ..., kill.v, killer.n, killing.n, lethal.a, liquidate.v, liqui- LUs dation.n, liquidator.n, lynch.v, massacre.n,massacre.v, matricide.n, murder.n, murder.v, murderer.n,... De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 6 / 26

  12. Frame-Enhancement Frames Frame semantics (Filmore, 1982) Describe the meaning of a sentence by characterizing the background knowledge required to understand it. Frame: KILLING The KILLER or CAUSE causes the death of the VICTIM. KILLER John drawned Martha. VICTIM I saw heretics beheaded. CAUSE The rockslide killed nearly half of FEs the climbers. INSTRUMENT It’s difficult to suicide with only a pocketknife . ..., kill.v, killer.n, killing.n, lethal.a, liquidate.v, liqui- LUs dation.n, liquidator.n, lynch.v, massacre.n,massacre.v, matricide.n, murder.n, murder.v, murderer.n,... FrameNet (Filmore et al., 2003) • Lexical units (LUs) evoking the frame • Semantic roles or frame elements (FEs) • Release 1.5 = 877 frames and 155K sentences De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 6 / 26

  13. Frame-Enhancement Relations Hierarchy (Ruppenhofer et al., 2005) • Inheritance or is-a like relation • Child frame is a subtype of the parent frame • Additional semantic properties De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 7 / 26

  14. Frame-Enhancement Relations Hierarchy (Ruppenhofer et al., 2005) • Inheritance or is-a like relation • Child frame is a subtype of the parent frame • Additional semantic properties De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 7 / 26

  15. Frame-Enhancement Book Dataset ESWC challenge • LibraryThing dataset • Mapped to DBpedia URIs De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 8 / 26

  16. Frame-Enhancement Book Dataset ESWC challenge • LibraryThing dataset • Mapped to DBpedia URIs Dataset • Downloaded all plots from Wikipedia • Uniform/unambiguous link • 5,063 books with plot De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 8 / 26

  17. Frame-Enhancement Book Dataset DBpedia mapped dataset De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 9 / 26

  18. Frame-Enhancement Book Dataset DBpedia mapped dataset Wikipedia page De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 9 / 26

  19. Frame-Enhancement Frame parsing SEMAFOR (Dipanjan et al., 2014) • state-of-the-art parser • sentence-per-sentence basis De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 10 / 26

  20. Frame-Enhancement Frame parsing SEMAFOR (Dipanjan et al., 2014) • state-of-the-art parser • sentence-per-sentence basis The [Prince], the protagonist, is [named] Alexander. His [father], [Prince] Baudouin, is [ murdered ] by PLOT the [King] of Cornwall, [King] [March]. [When] Alexander [comes] of [age], he [sets out] to Camelot to [seek] justice from [King] Arthur and to [ avenge ] his [father]’s [ death ]... Leadership, Appointing, Kinship, Leadership, Killing , FRAMES Leadership, Leadership, Calendric unit, Temporal collocation,Arriving, Calendric unit, Departing,Seeking to achieve, Leadership, Revenge , Death , Kinship. De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 10 / 26

  21. Frame-Enhancement Frame parsing The [Prince], the protagonist, is [named] Alexander. His [father], [Prince] Baudouin, is [ murdered ] by PLOT the [King] of Cornwall, [King] [March]. [When] Alexander [comes] of [age], he [sets out] to Camelot to [seek] justice from [King] Arthur and to [ avenge ] his [father]’s [ death ]... Leadership, Appointing, Kinship, Leadership, Killing , FRAMES Leadership, Leadership, Calendric unit, Temporal collocation,Arriving, Calendric unit, Departing,Seeking to achieve, Leadership, Revenge , Death , Kinship. De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 11 / 26

  22. Frame-Enhancement Frame parsing The [Prince], the protagonist, is [named] Alexander. His [father], [Prince] Baudouin, is [ murdered ] by PLOT the [King] of Cornwall, [King] [March]. [When] Alexander [comes] of [age], he [sets out] to Camelot to [seek] justice from [King] Arthur and to [ avenge ] his [father]’s [ death ]... Leadership, Appointing, Kinship, Leadership, Killing , FRAMES Leadership, Leadership, Calendric unit, Temporal collocation,Arriving, Calendric unit, Departing,Seeking to achieve, Leadership, Revenge , Death , Kinship. Revenge story → also royalty, family ,... # Avg Stdev # Avg unique Stdev Frames 197 205 96 61 Events 42 45 22 15 De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 11 / 26

  23. Frame-Enhancement Frames vs LOD Manual genre subdivision (Schuhmacher and Meilicke, 2014) • Parsing the abstract, genre and subject (DBpedia) • 30 distinct genres De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 12 / 26

  24. Frame-Enhancement Frames vs LOD Manual genre subdivision (Schuhmacher and Meilicke, 2014) • Parsing the abstract, genre and subject (DBpedia) • 30 distinct genres De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 12 / 26

  25. Frame-Enhancement Frames vs LOD Genre classification • Consider frames as features • Calculate gain ratio De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 13 / 26

  26. Frame-Enhancement Frames vs LOD Genre classification • Consider frames as features • Calculate gain ratio Science Fiction History Children Crime Beyond compare Representing Memorization Extradition Becoming dry Intentional traversing Measure area Go into shape Containment relation Dominate competitor Estimated value Exporting Dunking Getting vehicle underway Rope manipulation Becoming dry Exclude member Cause to rot Degree of processing Arson Representing Beyond compare Jury deliberation Measure area Jury deliberation Probability Bond maturation Dominate competitor Cause to rot Jury deliberation Intentional traversing Containment relation Medium Color qualities Cause to be dry Reading aloud Cause change of phase Get a job Drop in on Extreme point Change of consistency Eventive affecting Intentionally affect Endangering Immobilization Historic event Examination Posing as Execute plan Extradition Absorb heat Experience bodily harm Cause impact Surrendering possession Cause to experience Enforcing Reparation Corroding caused Fighting activity Cause to be wet Eventive affecting Dodging Dodging Intentionally affect Get a job Clemency Rope manipulation Intercepting Cause to be sharp Intentional traversing Intentional traversing Change resistance Cause to rot Cause to rot Drop in on Go into shape Cause change of phase Get a job Cause to be dry Extradition De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 13 / 26

  27. Experiments Overview 1 Frame-Enhancement 2 Experiments 3 Results 4 Conclusion De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 14 / 26

  28. Experiments Experimental Setup System • ESWC System (Schuhmacher and Meilicke, 2014) • two Naive Bayes classifiers 1 Global classifier (background model) 2 Per-user-neighborhood classifier (individual preferences) → Vary the features for item representation De Clercq et al. (LT 3 ) CBRecSys 10-06-2014 15 / 26

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