Generating Story Reviews Using Phrases

Generating Story Reviews Using Phrases

Hiroshi Ota and Kazuhide Yamamoto. Generating Story Reviews Using Phrases Expressing Emotion. Proceedings of the Annual meetings of the Pacific Asia Conference on Language, Information and Computation (PACLIC 22), pp.302-310 (2008.11)



November 30, 2008


  1. 1 Generating Story Review Using Phrases Expressing Emotion      Nagaoka University

    of Technology    Hiroshi OTA, Kazuhide YAMAMOTO
  2. 2 What we have done Construct the story review generator

    input: miss the medal output: The scene where he miss the medal is agonizing. Task: Generating text, Subjective expression
  3. 3 Review Flow [human] Oh…sad ① Read Feel Something Write

    the Review ② ③ The scene where … is sad.
  4. 4 Review Flow [system] ??? ① Input ② Judge the

    emotion Generate the Review ③ ???
  5. 5 Preparing the Lexicon • 2 types of the expression

    about emotion – directly : happy,sad,scare, ... – Indirectly: we can eat the special dinner • The story suggest the emotion – Indirectly • System needs the Indirectly Lexicon – Emotion Emerged Expression (E3)
  6. 6 E3 Lexicons joy sad fear のどを 潤す Wet one's whistle

    川に 落ちる Falls in the drink 事件が おきる happen accident 旅行に 行く Go trip 職場を 去る Go into retirement 泥沼に はまる Fall into slough 結婚式を あげる Celebrate a marriage 腰を やる Suffer from backache 指を 切る Cut one's finger
  7. 7 Construction of E3 Lexicons Blog 悲 Sad・E3 ①Extract Emotion

    Blogs ②Add to Lexicons 怖 喜 Sad Joy Fear 100 10 Deviation score for each phrases [Fujimura et al. 04]
  8. 8 Evaluation:E3 joy sad fear 327,702 37,439 13,238 Right:23%, Wrong:

    7% 70% lexicons are judged the No-emotion
  9. 9 Review Flow [system] Sad ① Input ②Judge the emotion

    Generating Review ③ ??? E3 Lexicons
  10. 10 Review sentence  Includes Object and Attitude  Emotion

    expression model [Nakayama et al.05] The scene where he holds back his tears is agonizing. E3 Emotion Expression Object/Scene Attitude/emotion
  11. 11 Generating Review  Use existing review as the template

    Existing review: The scene where he holds back his tears is too agonizing Miss the medal Generate Book(B)’s review Replace a scene of Book(A) a scene of Book(B)
  12. 12 Review Generator Template Review system ②Input: ⑤output:      ①Extract Review

    Sentence as Template ④Select Template “miss the medal” The scene where he miss the medal is agonizing E3 ③Judge the emotion
  13. 13  The object is clear  Include the emotion

    expression  Specific Noun + Particle + Emotion Expression Review Template The scene of the dialogue on the bus is pleasant. バスでの掛け合いの場面が面白い
  14. 14 How to select the template? • What is the

    good choice • Input emotion and Template emotion should be the same? • Coherence of the emotion in one sentence and the naturalness of sentence She receive a present that is sad.
  15. 15 Validation: Coherence and Naturalness Prepare the 2 pattern of

    sentences. Coherence and Incoherence about emotion  Generate 3 reviews against an E3.  An E3 has only one emotion.  3 reviews are made by 3 templates, each templates has different emotion expression.
  16. 16 Judge Natural / Unnatural Coherence sentence: 30/30 Incoherence sentence:

    11/60 (Input from E3 lexicon) The scene that *** is joyous. The scene that *** is sad.    The scene that *** is fear. Input: falls in the drink (sad) (coherent) (incoherent) (incoherent) (incoherent) Natural sentence / Evaluated Number
  17. 17 Discussion  Incoherent but natural sentence  Object is

    only two segment  Imagine some situation   Adding the Information of object, particular emotion may occur  Scene that falls in the drink  Who?When?Where?
  18. 18 Conclusion  Generate the sentence which include the subjective

    expression  Construct E3 Lexicon from blog  Propose the method generating Review sentence  Extract the review Flame in automatically  Emotion coherence and naturalness
  19. 19

  20. 20  Generate text which include Subjective expression  Opinion・Evaluate・Emotion

     Review
  21. 21 Construct flow of E3 Blog Sad SVM e Sad

    e other Sad・E3 ①Extract emotion blog ④Construct Lexicon ③Emotion Classification ②Construct classifier fear Joy
  22. 22 Extract Emotion blog  Sad blog requirements  1)

    Title include “sad”  2) “sad” is majority emotion expression in the text Table1.extract result Requirement (1) (2) (1)&(2) Accuracy (sad/extract) 0.5 (10/20) 0.6 (12/20) 0.85 (17/20)
  23. 23 Division Model  SVM  feature: content word 

    Training data  correct example: “Sad” blog  Wrong example: “Joy” blog, “fear” blog Table2.result of 10-fold cross validation     Classify Model joy sad fear Acc. 70.9 71.1 71.1
  24. 24 Evaluation about E3 joy sad fear correct 19 8

    7 wrong 0 2 3 no-emotion 20 29 21 Can't evaluate 7 6 9 原因を→調べる Research the cause 圧力を→思う Think pressure
  25. 25 Scoring [Fujimura04]  piece: syntactic piece  Pe(piece):probability occurring

    at e  scoree(piece):scoring about emotion-e
  26. 26 Unit of E3 Lexicons  Syntactic Piece  A

    modifier and a modificand [Aoki et al. 07]  Structure information
  27. 27 Review System hold back his tear oh、agnoizing.. ①Input Judge

    Emotion Generate review ② ③ E3 lexicons The scene where he holds his tears is agonizing.