IA 1A5 (=E1R86), 1L1 (=E1R05) , IIA E2R40 , 2011 7 ( 10 ) - - PowerPoint PPT Presentation

SLIDE 1

2011-06-30

英語 IA 1A5 (=E1R86), 1L1 (=E1R05), 英語 IIA E2R40, 2011 第7回 (全10回)

黒田 航 (非常勤) 出口雅也 (非常勤) の代理

Wednesday, July 6, 2011

SLIDE 2

講義資料のWebページ

✤ URL

✤ http://clsl.hi.h.kyoto-u.ac.jp/~kkuroda/lectures.html

✤ The Feynman Lectures on Physics の音源ファイルや授業で

使ったスライドはこのページから入手可能

✤ 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

✤ 京都工芸繊維大学で使っている教材（過去の分）もあるの

で，自習に使って良いです

Wednesday, July 6, 2011

SLIDE 3

期末ボーナス試験

✤ 7/28 (木) に試験をします

✤ 試験をしつつ，4回分の補講をするのは無理 ✤ 補講は期間外にはできないそうです

✤ この試験は任意参加のボーナス試験です

✤ 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 Wednesday, July 6, 2011

SLIDE 4

本日の予定

✤ 前半30分

• 1. L5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

❖ 聞き取り訓練 L6

Wednesday, July 6, 2011

SLIDE 5

任意参加ではない方々

✤ 1A5

✤ 脇田 健史

✤ 2R

✤ 大塚 直通, 財前 雄太, 乗竹 剛志, 栗原 拓也, 浦 順貴, 大月

亮太, 大野 遼, 長谷川 栄貴, 小野原 龍一, 松井 孝憲, 三野 春樹, 福地 崇洋, 小嶋 和也, 原 拓矢

✤ 1L1

✤ 宮本 貴史, 松元 大周, 川崎 眞理子, 原 祐太, 窪田 かすみ

Wednesday, July 6, 2011

SLIDE 6

L5の結果 (Deb Roy: The birth of a word, Part 2)

Wednesday, July 6, 2011

SLIDE 7

L5の得点分布 1A5, 2R, 1L1

✤ 参加者: 67人

✤ 平均: 71.55; 標準偏差: 10.48 ✤ 最高: 90.83; 最低: 43.33

✤ 得点グループ

✤ 80点が中心のグループ Wednesday, July 6, 2011

SLIDE 8

L5の得点分布 1A5

✤ 受講者数: 23

✤ 平均: 42.83/n [71.38] 点

✤ 標準偏差: 6.26/n [ 9.62] 点

✤ 最高: 54.50/n [90.83] 点 ✤ 最低: 32.50/n [54.17] 点

✤ n = 60

✤ 得点グループ

✤ 65点, 75点, 85点, 95点が中心のグルー

プ

Wednesday, July 6, 2011

SLIDE 9

L5の得点分布 2R

✤ 受講者数: 16

✤ 平均: 40.13/n [66.88] 点

✤ 標準偏差: 7.56/n [12.59] 点

✤ 最高: 53.00/n [88.33] 点 ✤ 最低: 26.00/n [43.33] 点

✤ n = 60

✤ 得点グループ

✤ 55点, 75点が中心のグループ

Wednesday, July 6, 2011

SLIDE 10

L5の得点分布 1L1

✤ 受講者数: 28

✤ 平均: 44.63/n [74.38] 点

✤ 標準偏差: 5.49/n [ 9.15] 点

✤ 最高: 54.00/n [90.00] 点 ✤ 最低: 30.50/n [50.83] 点

✤ n = 60

✤ 得点グループ

✤ 75点, 80点, 95点が中心のグループ

Wednesday, July 6, 2011

SLIDE 11

得点の変遷 (L5まで)

Wednesday, July 6, 2011

SLIDE 12

L5の正解率分布 1A5, 2R, 1L1

✤ 参加者: 67人

✤ 平均値: 0.88 ✤ 最高値: 0.95; 最低値: 0.50 ✤ 標準偏差: 0.07

✤ 正答率のグループ

✤ 0.8辺りが中心のグループ Wednesday, July 6, 2011

SLIDE 13

L5の正答率分布 1A5

✤ 参加者: 23人

✤ 平均: 0.85; 標準偏差: 0.04 ✤ 最高: 0.92; 最低: 0.77

✤ 正答率のグループ

✤ 0.9が中心のグループ Wednesday, July 6, 2011

SLIDE 14

L5の正答率分布 2R

✤ 参加者: 16人

✤ 平均: 0.84; 標準偏差: 0.06 ✤ 最高: 0.94; 最低: 0.73

✤ 正答率のグループ

✤ 0.9が中心 Wednesday, July 6, 2011

SLIDE 15

L5の正答率分布 1L1

✤ 参加者: 28人

✤ 平均: 0.84; 標準偏差: 0.05 ✤ 最高: 0.93; 最低: 0.70

✤ 正答率のグループ

✤ 0.85が中心 Wednesday, July 6, 2011

SLIDE 16

正答率の変遷 (L5まで)

Wednesday, July 6, 2011

SLIDE 17

全体の評価

✤ 1A5, 2R, 1L1の全クラスで ✤ 得点と正答率のいずれでも， ✤ 2番目に高い成績 ✤ 得点に関しては ✤ 1L1が堅調

Wednesday, July 6, 2011

SLIDE 18

L5の解答 (Deb Roy: The birth of a word)

Wednesday, July 6, 2011

SLIDE 19

誤りの傾向

✤

• 1. at

✤

• 2. welcome ⇒ walking,
• pen

✤

• 3. did ⇒ get

✤

• 4. me ⇒ mean

✤

• 5. leaving ⇒ living

✤

• 6. freeze ⇒ free

✤

• 7. see

✤

• 8. call ⇒ go

✤

• 9. when

✤

• 10. here’s

✤

• 11. off ⇒ often, ask

✤

• 12. power

✤

• 13. it

✤

• 14. with

✤

• 15. wordscape ⇒

wordscapes

✤

• 16. for

✤

• 17. landscape ⇒

wordscape

✤

• 18. peering ⇒ pearing,

appearing

✤

• 19. people

✤

• 20. following ⇒ phone,

form

✤

• 21. take

✤

• 22. turn

✤

• 23. same

✤

• 24. satellite ⇒ all

✤

• 25. feeds ⇒ series

✤

• 26. magic

✤

• 27. looking

✤

• 28. except

✤

• 29. are ⇒ relate

✤

• 30. sphere ⇒

experience, experiment

✤

• 31. gives ⇒ keeps

✤

• 32. a ⇒ eight, 8

✤

• 33. lives ⇒ leaves, was

✤

• 34. third ⇒ three

✤

• 35. rendered ⇒

around, learning, landing

✤

• 36. that

✤

• 37. if ⇒ for, free

✤

• 38. that ⇒ at, not

✤

• 39. living

✤

• 40. into

✤

• 41. finding

✤

• 42. fan-out ⇒ final

✤

• 43. link

✤

• 44. address ⇒ brass,

grass, adress

✤

• 45. remarkable

✤

• 46. pulse ⇒ pose, ports

✤

• 47. collect ⇒ correct

✤

• 48. dynamics ⇒

dinamics, dyinamics, dynamix

✤

• 50. profound ⇒ found

✤

• 51. reflect ⇒ flight,

fight

✤

• 52. guys ⇒ gaze,

guides

✤

• 53. gonna

✤

• 54. powerful

✤

• 55. take

✤

• 56. encouraging ⇒

encourage, in-

✤

• 57. realizes ⇒ low

✤

• 58. kicks ⇒ keeps

✤

• 59. back

✤

• 60. walking ⇒ walking

Wednesday, July 6, 2011

SLIDE 20

01/12

✤ 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

• f, 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.

Wednesday, July 6, 2011

SLIDE 21

02/12

✤ 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.

Wednesday, July 6, 2011

SLIDE 22

03/12

✤ 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

• ver to the left.

Wednesday, July 6, 2011

SLIDE 23

04/12

✤ 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”

• ccurs much more in a structured way. So we’re using these structures

to start predicting the order of language acquisition, and, and that’s

• ngoing 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. 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:

Wednesday, July 6, 2011

SLIDE 24

05/12

✤ 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.

Wednesday, July 6, 2011

SLIDE 25

06/12

✤ You have the event structure, the common ground that the words

are about, coming out of the television feeds; you’ve got the conversations that are about that, those topics; and through semantic analysis —and this is actually real data you’re [27. looking] at from our data processing— each yellow line is showing a link being made between a comment in the wild and a piece of event structure coming out of the television signal. And the same idea now can be built up. And we get this wordscape, [28. except] now words are not assembled in my living room. Instead, the context, the common ground activities, are the content on television that’s driving the conversations. And what we’re seeing here, these skyscrapers now, are commentary that [29. are] linked to content

• n television. Same concept, but looking at communication

dynamics in a different, very different [30. sphere].

Wednesday, July 6, 2011

SLIDE 26

07/12

✤ And so fundamentally, rather than, for example, measuring

content based on how many people are watching, this [31. gives] us the basic data for looking at engagement properties of

• content. And just like we can look at feedback cycles and

dynamics in, in a, in a family, we can now open up the same concepts and look at, uh, much larger groups of people. This is [32. a] subset of uh data from our database —just 50,000 out of several million— and the social graph that connects them through publicly available sources. And if you put them on one plain, a second plain is where the content [33. lives]. So we have the programs and the, the, the sporting events and the commercials, and all of the link structures that tie (up) them together make a content graph. And then the important [34. third] dimension.

Wednesday, July 6, 2011

SLIDE 27

08/12

✤ Each of the links that you’re seeing [35. rendered] here is an

actual connection made between something someone said and a piece of content. And there are, again, now tens of millions of these links [36. that] give us the connective tissue of social graphs and how they relate to content. And we can now start to probe the structure in interesting ways.

✤ So [37. if] we, for example, trace the path of one piece of content

that drives someone to comment on it, and then we follow where that comment goes, and then look at the entire social graph that becomes activated and then trace back to see the relationship between [38. that] social graph and content, a very interesting structure becomes visible. We call this a co-viewing clique, a virtual [39. living] room if you will. And there are fascinating dynamics at play.

Wednesday, July 6, 2011

SLIDE 28

09/12

✤ It’s not one way. A piece of content, an event, causes someone to talk.

They talk to other people. That drives tune-in behavior back [40. into] mass media, and you have these cycles that drive the overall behavior.

✤ Another example —very different— another actual person in our

database— and we’re [41. finding] at least hundreds, if not thousands,

• f these. We’ve given this person a name. This is a pro-amateur, or pro-

am, media critic who has this high [42. fan-out] rate. So a lot of people are following this person— very influential —and they have a propensity to talk about what’s on TV . So this person is a key [43. link] in connecting mass media and social media together.

✤ One last example from this data: Sometimes it’s actually a piece of

content that is special. So if we go and look at this piece of content, President Obama’s State of the Union [44. address] from just a few weeks ago,

Wednesday, July 6, 2011

SLIDE 29

10/12

✤ and look at what we find in, in this same data set, at the same scale,

the engagement properties of this piece of content are truly [45. remarkable]. A nation exploding in conversation in real time in response to what’s on, on the broadcast. And of course, through all of these lines are flowing unstructured language. We can X-ray and get a real-time [46. pulse] of a nation, real-time sense of the social reactions in the different circuits in the social graph being activated by content.

✤ So, to summarize, the idea is this: As our world becomes increasingly

instrumented and we have the capabilities to [46. collect] and connect the dots between what people are saying and the context they’re saying it in, what’s emerging is an ability to see new social structures and [48. dynamics] that have previously not been seen. It’s like building a microscope or telescope and revealing new structures about our own behavior around communication.

Wednesday, July 6, 2011

SLIDE 30

11/12

✤ And I think the implications here are [49. profound], whether it’s for

science, for commerce, for government, or perhaps most of all, for us as individuals.

✤ And so just to [50. return] to my son, when I was preparing this talk, he

was looking over my shoulder, and I showed him the clips I was going to show to you today, and I asked him for permission —granted. And then I went on to [51. reflect], “Isn’t it amazing, this entire database, all these recordings, I’m gonna hand off to you and to your sister?” who arrived two years later. “And you [52. guys] are going to be able to go back and re-experience moments that you could never, with your biological memory, possibly remember the way you can now.” And he was quiet for a moment. And I thought, “What am I thinking? He’s five years old. He is not [53. gonna] understand this.” And just as I was having that thought, he looked up at me and said, “So, that when I grow up, I can show this to my kids?” And I thought, “Wow, this is— this is [54. powerful] stuff.”

Wednesday, July 6, 2011

SLIDE 31

12/12

✤ So I want to leave you with one last memorable moment from our family. This

is our— the first time our son took more than two steps at once —captured on

• film. And I really want you to focus on something as I, as I [55. take] you
• through. It’s a cluttered environment; it’s natural life. My mother’s in the

kitchen, cooking, and, of all places, in the hallway, I realize he’s about to do it, about to take more than two steps. And so you hear me [56. encouraging] him, realizing what’s happening, and then the magic happens. Listen very

• carefully. About three steps in, he [57. realizes] something magic is happening.

And the most amazing feedback loop of all [58. kicks] in, and he takes a breath in, and he whispers “wow” and instinctively I echo, I echo back the

• same. And so let’s fly [59. back] in time to that memorable moment.

✤ DR: Hey. Come here. Can you do it? Oh, boy. Can you do it? ✤ Baby: Yeah. ✤ DR: Ma, he’s [60. walking]. ✤ (Laughter) (Applause) Thank you. (Applause)

Wednesday, July 6, 2011

SLIDE 32

TEDを使った聞き取りL6

✤ Laurie Santos: A monkey economy as irrational as ours の前半

✤ 今日の課題の長さ: 11分 ✤ 41まで ✤ 全体は19分30秒ほど

✤ 穴埋め方式

✤ 長い目のユニットごとに2回反復

Wednesday, July 6, 2011