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英語 中学生

至急⚠️ 丸つけお願いいたします🙇🙇 明日の英語であるので💦

定詞 分詞/ 関係代名詞 している分がどこか考えよう 1 例にならって、下線の語句を修飾している部分に ( [例] There is a student(from India)in my school. 01 The bag on the desk is mine. 02 I want a book to read on the bus.) 03 That man taking pictures is my uncle. 04 This is a car (made in Japan.) 05 The food Emi likes the best is pizza. 06 I saw a cat that had blue eyes yesterday. 21:43 )をつけなさい。 (私の学校にはインド出身の生徒がいます) 机の上のかばんはぼくのものです) (私はバスの中で読む本がほしいです) (写真を撮っているあの男性は私のおじです) (これは日本で作られた車です) (エミが一番好きな食べものはビザです) ぼくは昨日、青い目を持ったネコを見ました) 1 / 6 日本との順のちがいをたしかめよう 2 | の語句を並べかえなさい。 07 歴史についての本 history a book about →A book about history 08 コーヒーを飲む時間 to coffee time have →>. have to coffee time 09 ドアのところに立っている女性 the door at standing the woman /4問 →The woman at the door standing 10 私が訪れたい国 want the country I to visit which →The country which I want to visit 3 文の話題をたしかめよう 次の文に 内の情報を付け足して書きかえなさい。 11 Ben is a precious member. (of our team) →Benis a precious of our team 12 That picture is beautiful. (on the wall ) →That picture 8/ ベンは私たちのチームの大切なメンバーです。 member 壁にかかっているあの写真はきれいです。 on the wall is beautiful. 13 Iknowagood place. (to watch the sunrise) -> 私は日の出を見るのにいい場所を知っています。 I know a good place to watch the sunrise. 14 What is the language? (used in Singapore ) シンガポールで使われている言語は何ですか。 → What is the used in Singapore language? 15 The girl is a new student. (walking with Bill) → The girl walking with Bill new 16 I will make everything. (that you want to eat) →I will make that you 17 The restaurant was nice. (Yuri recommended) ビルと歩いている女の子は新入生です。 student. 私は、あなたが食べたいものを全部作りますよ。 want to eat eveything. → Yuri recommended the restaurant 18 Do you know the man? (who was sitting here) → Do you 3年®AD know ユリがすすめてくれたレストランはよかったです。 was nice あなたは、ここに座っていた男性を知っていますか。 who was sitting here the man?

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英語 高校生

準動詞の問題です。 答えがなく丸つけができないため答えを教えて欲しいです。

Further 30 Lessons 準動詞 (不定詞・動名詞 分詞) Exercises 56 1 []から適切な語句を選びなさい。 (1) I heard him [sung/ singing / to sing] a song in the bathroom. (2)My mother made me [ to do / do / doing J the dishes. (3) I'm looking forward to visit / visiting / to visit ] your house. (4) Please remember [turn/turned / to turn ] off the light. (5)He caught a bad cold, so he gave up [ swim / to swim / swimming J in the sea. (6)Susan was worried about [be/being/her] late for the meeting. (7) Meg had her hair [ cut / cutting / to cut] at a beauty salon. (8)[Interesting / Interested ] in animals, he wants to work at the zoo. 2 日本語の意味に合うように、 ( )に適切な語を入れなさい。 (1) どこで勉強するべきか、 私に教えてください。 Please tell me ( -) ( (2) 彼はたまたま私の名前を知っていた。 He ( ) (. (3) 私の両親は私に留学してほしいと思っている My parents ( )me( ) ( ) know my name. ) ( ) abroad. ) table tennis tomorrow afternoon? ) him to say such a thing. ) anything. (4) 明日の午後に卓球をするのはどうですか。 How ( ) ( (5) そんなことを言うなんて, 彼は礼儀正しい。 It ( )( )( (6)リサは何もする気になれなかった。 Lisa didn't ( ) like ( 3 日本語の意味に合うように, [] の語句を並べかえて全文を書きなさい。 (1) 窓を開けてもよろしいでしょうか。 (1語不要) [ the window / mind / would / open / opening / you / my ]? (2) 彼は車の運転に慣れている。 [ is / used / he / driving / to ]. (3) 彼女は自分の犬を店の外に待たせておいた。 [ 'waiting / left / she / outside the shop / her dog ]. (4) その事故でけがをした少女が病院に運ばれた。 [ taken / the girl / the hospital / the accident / injured / was / in / to ]. (5) ケリーは家が買えるくらい十分に裕福だ。 (1語不足) [ is / buy / enough / Kelly/ahouse / rich J.

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TOEIC・英語 大学生・専門学校生・社会人

この長文問題の答えと解説をお願いします。

15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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