English Acquisition IA k , IIA f , 2011 10 ( 13 ) ( ) - PowerPoint PPT Presentation

English Acquisition IA k , IIA f , 2011 第 10 回 ( 全 13 回 ) 黒田 航 ( 非常勤 ) 2011/06/28 ( 火 ) Thursday, June 30, 2011

講義資料の Web ページ ✤ URL ✤ http://clsl.hi.h.kyoto-u.ac.jp/~kkuroda/lectures.html ✤ 予習や復習に使って下さい ✤ 解答もこのページから入手可能 Thursday, June 30, 2011

ボーナス試験 ✤ 最期の授業は任意参加のボーナス試験です ✤ 出席回数の足りない人は任意でないです ✤ 授業でやったのと同じ課題を行なう ✤ ハズレがアタリに ✤ アタリはアタリのまま Thursday, June 30, 2011

任意参加でない人たち ✤ 次の方々は今のままでは F です ✤ EA1Ak ✤ 中島 裕貴 ✤ EA2Af ✤ 原 将樹 , 西河 拓哉 , 武藤 弘平 , 藤本 拡二 Thursday, June 30, 2011

本日の予定 ✤ 前半 30 分 1. L9 の聞き取り課題の結果の報告 2. 正解の解説 ✤ 休憩 5 分 ✤ 後半 40 分 • TED を使った聴き取り訓練の 2 回目 (L10) • Laurie Santos: Monkey Economy as Irrational as Ours • テーマ : 比較心理学，意思決定論，経済学 Thursday, June 30, 2011

L9 の結果 (TED- Deb Roy The birth of a word から ) Thursday, June 30, 2011

L9 の得点分布 1A k, 2A f ✤ 参加者 : 46 人 ✤ 平均点 : 61.28; 標準偏差 : 10.09 ✤ 最高点 : 88.33; 最低点 : 41.07 ✤ n = 56 ✤ 得点グループ ✤ 65 点後半が中心のグループ ✤ 85 点後半が中心のグループ ? Thursday, June 30, 2011

L9 の得点分布 1A k ✤ 受講者数 : 28 ✤ 平均点 : 34.63/ n [61.83] 点 ✤ 標準偏差 : 4.50/ n [8.03] 点 ✤ 最高点 : 45.00/ n [80.36] 点 ✤ 最低点 : 25.00/ n [44.64] 点 ✤ n = 56 Thursday, June 30, 2011

L9 の得点分布 2A f ✤ 受講者数 : 18 ✤ 平均点 : 32.47/ n [57.98] 点 ✤ 標準偏差 : 6.17/ n [11.01] 点 ✤ 最高点 : 45.50/ n [81.25] 点 ✤ 最低点 : 23.00/ n [41.07] 点 ✤ n = 56 Thursday, June 30, 2011

平均得点の履歴 Thursday, June 30, 2011

L9 の正解率分布 1A k, 2A f ✤ 参加者 : 46 人 ✤ 平均 : 0.72; 標準偏差 : 0.08 ✤ 最高 : 0.88; 最低 : 0.47 ✤ 正答率のグループ ✤ 0.7 後半が中心のグループ Thursday, June 30, 2011

L9 の正答率分布 1A k ✤ 参加者 : 28 人 ✤ 平均 : 0.72; 標準偏差 : 0.06 ✤ 最高 : 0.82; 最低 : 0.58 ✤ 正答率のグループ ✤ 0.7 が中心のグループ Thursday, June 30, 2011

L9 の正答率分布 2A f ✤ 参加者 : 16 人 ✤ 平均 : 0.72; 標準偏差 : 0.10 ✤ 最高 : 0.88; 最低 : 0.47 ✤ 正答率のグループ ✤ 0.8 が中心のグループ Thursday, June 30, 2011

平均正解率の履歴 Thursday, June 30, 2011

L9 の解答 (FLP) Thursday, June 30, 2011

誤りの傾向 1. at 17. landscape ⇒ 31. gives ⇒ keeps 46. collect ⇒ correct ✤ ✤ ✤ ✤ wordscape 2. welcome ⇒ walking, 32. a ⇒ eight, 8 47. dynamics ⇒ ✤ ✤ ✤ open 18. peering ⇒ pearing, dinamics, dyinamics, ✤ 33. lives ⇒ leaves, was ✤ appearing dynamix 3. did ⇒ get ✤ 34. third ⇒ three ✤ 19. people 48. profound ⇒ found ✤ ✤ 4. me ⇒ mean ✤ 35. rendered ⇒ ✤ 20. following ⇒ phone, 49. reflect ⇒ flight, ✤ ✤ 5. leaving ⇒ living around, learning, ✤ form fight landing 6. freeze ⇒ free ✤ 21. take 50. gonna ✤ ✤ 36. that ✤ 7. see ✤ 22. turn 51. take ✤ ✤ 37. if ⇒ for, free ✤ 8. call ⇒ go ✤ 23. same 52. encouraging ⇒ ✤ ✤ 38. that ⇒ at, not ✤ 9. when encourage, in- ✤ 24. satellite ⇒ all ✤ 39. living ✤ 10. here’s 53. realizes ⇒ low ✤ ✤ 25. feeds ⇒ series ✤ 40. into 11. off ⇒ often, ask ✤ 54. kicks ⇒ keeps ✤ ✤ 26. magic ✤ 41. finding ✤ 12. power 55. back ✤ 27. looking ✤ ✤ 42. fan-out ⇒ final ✤ 13. it 56. walking ⇒ walking ✤ 28. except ✤ ✤ 43. address ⇒ brass, 14. with ✤ ✤ 29. are ⇒ relate grass, adress ✤ 15. wordscape ⇒ ✤ 30. sphere ⇒ 44. remarkable ✤ ✤ wordscapes experience, experiment 45. pulse ⇒ pose, ports 16. for ✤ ✤ Thursday, June 30, 2011

01/11 ✤ But that's looking [ 1. at ] the speech context. What about the visual context? We’re now looking at— think of this as a dollhouse cutaway of our house. We’ve taken those circular fish-eye lens cameras, and we've done some optical correction, and then we can bring it into three-dimensional life. So [ 2. welcome ] to my home. This is a moment, one moment captured across multiple cameras. The reason we [ 3. did ] this is to create the ultimate memory machine, where you can go back and interactively fly around and then breathe video life into this system. What I'm going to do is give you an accelerated view of 30 minutes, again, of just life in the living room. That’s [ 4. me ] and my son on the floor. And there’s video analytics that are tracking our movements. My son is [ 5. leaving ] red ink, I am leaving green ink. We're now on the couch, looking out through the window at cars passing by. And finally, my son playing in a walking toy by himself. Thursday, June 30, 2011

02/11 ✤ Now we [ 6. freeze ] the action, 30 minutes, we turn time into the vertical axis, and we open up for a view of these interaction traces we’ve just left behind. And we [ 7. see ] these amazing structures--- these little knots of two colors of thread, we call social hot spots . The spiral thread, we [ 8. call ] a solo hot spot . And we think that these affect the way language is learned. What we’d like to do is start understanding the interaction between these patterns and the language that my son is exposed to to see if we can predict how the structure of when words are heard affects [ 9. when ] they’re learned —so in other words, the relationship between words and what they’re about in the world. ✤ So [ 10. here’s ] how we’re approaching this. In this video, again, my son is being traced out. He's leaving red ink behind. And there's our nanny by the door. Thursday, June 30, 2011

03/11 ✤ Nanny: You want water? ✤ Baby: Aaaa.) ✤ Nanny: All right. ✤ (Baby: Aaaa.) ✤ She offers water, and [ 11. off ] go the two worms over to the kitchen to get water. And what we’ve done is use the word “water” to tag that moment, that bit of activity. And now we take the [ 12. power ] of data and take every time my son ever heard the word “water” and the context he saw [ 13. it ] in, and we use it to penetrate through the video and find every activity trace that co-occurred [ 14. with ] an instance of water. And what this data leaves in its wake is a landscape. We call these wordscapes . This is the [ 15. wordscape ] for the word water, and you can see most of the action is in the kitchen. That’s where those big peaks are over to the left. Thursday, June 30, 2011

04/11 ✤ And just [ 16. for ] contrast, we can do this with any word. We can take the word “bye” as in “good bye.” And we’re now zoomed in over the entrance to the house. And we look, and we find, as you would expect, a contrast in the [ 17. landscape ] where the word “bye” occurs much more in a structured way. So we’re using these structures to start predicting the order of language acquisition, and that’s ongoing work now. ✤ In my lab, which we’re [ 18. peering ] into now, at MIT —this is at the media lab. This has become my favorite way of video graphing just about any space. Three of the key [ 19. people ] in this project, Philip DeCamp, Rony Kubat and Brandon Roy are pictured here. Philip has been a close collaborator on all the visualizations you’re seeing. Thursday, June 30, 2011

05/11 ✤ And Michael Fleischman was another Ph.D. student in my lab who worked with me on this home video analysis, and he made the [ 20. following ] observation: that “just the way that we’re analyzing how language connects to events which provide common ground for language, that same idea we can [ 21. take ] out of your home, Deb, and we can apply it to the world of public media.” And so our effort took an unexpected [ 22. turn ]. ✤ Think of mass media as providing common ground and you have the recipe for taking this idea to a whole new place. We’ve started analyzing television content using the [ 23. same ] principles —analyzing event structure of a TV signal— episodes of shows, commercials, all of the components that make up the event structure. And we’re now, with [ 24. satellite ] dishes, pulling and analyzing a good part of all the TV being watched in the United States. And you don't have to now go and instrument living rooms with microphones to get people’s conversations, you just tune into publicly available social media [ 25. feeds ]. So we’re pulling in about three billion comments a month. And then the [ 26. magic ] happens. Thursday, June 30, 2011