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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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英語 中学生

どうやって覚えたらいいですか。

3年生ま ※1・2年生で登場したはページをイタリ ※1・2年生ですでに学んでいて、3年生では登場しない! 過去分詞形 cutting 33 Stand 過去形 cut hitting teach 現在形 10 QUEER ☐ tell stand(s) cut hit hurting 21 A-A-A THE PRI ☐ チェックページ cut(s) hit hurt letting 50 think teach(es) cut 59 hit(s) hurt let putting 34 think(s) hit hurt(s) let put 85 reading win D hurt let(s) put read D ②② let put(s) setting A-B-C read set D 8 put read(s) set チェックページ ☐ 23 read set(s) D 2 set □ D コ 16 come 7 63 run A-B-A チェックページ 23 become become(s) became come(s) run/s) 原形 現在形 過去形 過去分詞形 came ran become come 現在分詞形 becoming 11 原形 ☐ be 31 現在形 ☐ coming running 36 begin am/is/are understand tell(s) 過去形 stood told thought understand(s) understood win(s) won 過去分詞形 stood taught told thought standing understood teaching telling taught 現在分詞形 won thinking 過去形 understan winning bear ☐ run ☐ 736 begin(s) break bear(s) was/were began 過去分詞形 been 900 choose break(s) bore begun being 現在分詞形 ☐ do 31 choose(s) broke bom begin 過去分詞形 ☐ 過去形 B-B型 ページ 30 63 bring 現在形 原形 bought bought buying 27 buy's) buy bring(s) brought brought bringing ☐ 178 draw do(es) chose broken bear drink draw(s) did chosen brec building ☐ eat drink(s) drew done cho build(s) built built 51 build catch(es) caught caught catching ☐ 57 digging ☐ ②② catch dug dig(s) dug feeling ☐ felt ② dig feel(s) felt ¥2 feel 4 fight fight(s) fought fought fighting ☐ 5247 12 fall eat(s) drank drawn do fly fall(s) ate drunk dr ② forget fly/flies fell eaten d get forget(s) flew fallen find find(s) found found finding ☐ give get(s) forgot flown had having ☐ 75 have have/has had hear hear(s) heard heard hearing ☐ hold hold(s) held held holding ☐ 4334 go give(s) got forgotten go(es) gave gotten/got given grow went hide grow(s) gone grew keep keep(s) kept kept keeping know hide(s) grown hid ☐ eave leave(s) left left leaving 12 ride know(s) hidden knew ☐ se lose(s) lost lost losing ake make(s) made made making an mean(s) meant meant meaning et meet(s) met met meeting d rebuild(s) rebuilt rebuilt rebuilding say(s) said said saying sell(s) sold sold selling send(s) sent sent sending sit(s) sat sat sitting sleep(s) slept slept sleeping spend(s) spent spent spending 0000000000 10 52 602223 ride(s) known see rode see(s) ridden show saw sing show(s) showed seen shown 29 sing(s) speak sang Sung 2 steal speak(s) spoke spoker 37 swim steal(s) stole stolen swim(s) Swam SWUm 4 take take(s) took taken ①②1 throw throw(s) threw throw 2 wake wake(s) woke wok 49 wear wear(s) wore WO 10 write write(s) wrote WT

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