目前分類:未分類文章 (1084)
- Jun 24 Sat 2017 10:38
【食記】全聯自製便當─檸檬雞腿便當@《皆涉世》
- Jun 24 Sat 2017 09:38
【遊記】欣欣客運取回遺失手機@《皆涉世》
義大利文翻譯語言翻譯公司1999在專人接聽前會先告知標準法式會朗誦案件內容,是男聲接聽,對方也不太懂我意思,我猜是想要我打去表彰司機吧,對方也覺得是如許,就照著這個意思登打案件,並留取姓名和德律風。因為下大雨所以決議搭251歸去,在車上乖乖撥了1999,放三天還有電真不愧是NOKIA。
登打完畢照規定朗誦一遍,並給鉦昱翻譯社案件編號、平安密碼,可以查詢案件進度,但如同也不消留,隔天收到簡訊就有案件編號和平安密碼。「有什麼事嗎?」可能在期待發車的兩位司機年老,其中一位先作聲問我「來拿失物招領」「麻煩X哥了」沒聽清晰叫了櫃檯那位什麼,櫃檯的大哥說「來拿手機嗎?是這支嗎?」「不是」「什麼時辰掉的?有先打德律風來嗎?」「兩天前295...對是這支」彷佛有打德律風來問的遺失物城市先用一張紙包好放在待領取的地方。在挂號本裡面寫了遺失物的拾獲資訊之後「幫我在這裡填一下姓名、德律風」櫃台年老說中華電信有打給我說要替換2G什麼的,所以我猜若是機會正確的話會幫忙接聽德律風,之前打德律風的時機可能不合錯誤,都沒人接,卻只接到告白德律風,哈。
- Jun 24 Sat 2017 02:19
(免費!!!) Line行銷軟體2016新增功能大量群發訊息群邀加石友投稿記事 ...
- Jun 23 Fri 2017 19:37
以諾語:神秘的失蹤天使語 @ Project Xanadu
- Jun 23 Fri 2017 18:36
35個不要犯法的來由 @ 這是 mini 的部落格
- Jun 23 Fri 2017 11:26
【誠徵】大樂透算牌程式撰寫合作伙伴 @ 在家工作同盟
- Jun 23 Fri 2017 08:35
黉舍遍地室、辦公場所、設備及職位稱呼英文譯名一覽表@ 公益英語佈道 ...
- Jun 23 Fri 2017 07:58
為八仙樂園爆炸不測遇難者祈福 @ 在家工作聯盟
- Jun 22 Thu 2017 21:49
面相學公益電子書無使用刻日熱烈下載中... @ 公益英語傳教士& 克亞 ...
- Jun 22 Thu 2017 20:06
一五年十月還在談定單,十二月就突然沒單了
- Jun 22 Thu 2017 12:15
葛瑞芬飆36分 送給李佛斯生活生計800勝
快艇今在主場對上湖人,展開洛杉磯內戰,快艇靠明星大先鋒葛瑞芬(Blake Griffin)獨拿36分,明星控衛保羅(Chris Paul )飆29分,以115比104擊敗湖人,也送給總鍛練李佛斯(Doc Rivers)在NBA生活生計執教的第800勝翻譯快艇自從2012-13球季以來,在洛杉磯內戰中佔盡優勢,連同昨天在內,20次和湖人交手贏得18戰,快艇拿下3連勝和比來9戰的第7勝,追近和西區第4的爵士只剩1場勝差。 (廖柏璋/綜合報道)
- Jun 22 Thu 2017 09:55
Harry Styles 哈利斯泰爾
- Jun 22 Thu 2017 00:55
[諜報]新竹的【中英筆譯入門】招生中!!
英語翻譯波士尼亞語語言翻譯公司【超強主打】中英筆譯入門(沐日班) http://edu.tcfst.org.tw/query_coursedetail.asp?courseidori=05L334&tcfst ★課程時間:2016/8/6起,每星期(六) ,09:00-16:00,共30小時 ★課程簡介: 本課程由具18年翻譯資歷,曾榮獲梁實秋翻譯評審獎、通過教育部中英翻譯能力「中翻英 筆譯」、「英翻中筆譯」、「慢慢口譯」之專業講師講課翻譯課程內容以英文轉換中文為主 ,將介紹筆譯工作與技能,透過實際練習訓練瞭解中英文的差異,進而提升英文鑑賞能力。課 程正視實作,除以電腦上機練習訓練,亦有課後作業於後續課程中檢討。期望學員能透過課 程掌握中英筆譯之重點觀念,跳脫逐字翻譯的毛病迷思。 ★課程師資: 連育德 Patrick Lien 英國巴斯大學口筆譯所碩士 教育部中英翻譯能力「中翻英筆譯」、「英翻中筆譯」、「慢慢口譯」檢定全數經由過程 曾獲梁實秋翻譯獎評審獎 ★諮詢專線:03-5623116#3233 張蜜斯 [email protected]
- Jun 21 Wed 2017 23:31
愛滋為非洲青少歲首號死因削減對女性的性暴力為關鍵
- Jun 18 Sun 2017 21:44
[試題] 104下 陳信希 天然說話處置 期中考
巴米萊克文翻譯族語言翻譯公司課程名稱︰自然語言處理 課程性質︰系內選修 課程教師︰陳信希 開課學院:電資學院 開課系所︰資訊工程學系 考試日期(年月日)︰2016/04/21 考試時限(分鐘):180 mins 試題 : 01. Machine translation (MT) is one of practical NLP applications. The development of MT systems has a long history翻譯社 but still has space to improve. Please address two linguistic phenomena to explain why MT systems are challenging. (10pts) 02. An NLP system can be implemented in a pipeline翻譯社 including modules of morphological processing翻譯社 syntactic analysis翻譯社 semantic interpretation and context analysis. Please use the following news story to describe the concepts behind. You are asked to mention one task in each module. (10pts) 這場地動可能影響日相安倍晉三的施政計畫翻譯安倍十八日說,消費睡調漲的 計畫不會改變。 03. Ambiguity is inherent in natural language. Please describe why ambiguity may happen in each of the following cases. (10pts) (a) Prepositional phrase attachment. (b) Noun-noun compound. (c) Word: bass 04. Why the extraction of multiword expressions is critical for NLP applications? Please propose a method to check if an extracted multiword expression meets the non-compositionality criterion, (10pts) 05. Mutual information and likelihood ratio are commonly used to find collocations in a corpus. Please describe the ideas of these two methods. (10pts) 06. Emoticons are commonly used in social media. They can be regarded as a special vocabulary in a language. Emoticon understanding is helpful to understand the utterances in an interaction. Please propose an "emoticon" embedding approach to represent each emoticons as a vector翻譯社 and find the most 5 relevant words to each emoticon. (10pts) 07. To deal with unseen n-grams, smoothing techniques are adopted in conventional language modeling approach. They are applied to n-grams to reallocate probability mass from observed n-grams to unobserved n-grams, producing better estimates for unseen data. Please show a smoothing technique for the conventional language model, and discuss why neural network language model (NNLM) can achieve better generalization for unseen n-grams. (10pts) 08. In HMM learning, we aim at inferring the best model parameters, given a skeletal model and an observation sequence. The following two equations are related to compute the state transition probabilities. Σ_{t=1}^{T-1} ξ_t(i, j) \hat{a}_{ij} = --------------------------------------- Σ_{t=1}^{T-1} Σ_{j=1}^{N} ξ_t(i,j) α_t(i) a_{ij} b_j(o_{t+1}) β_{t+1}(j) ξ_t(i, j) = ----------------------------------------- α_T(q_F) Please answer the following questions. (10pts) (a) Intuitively, we can generate all possible paths for the given observation sequence, and compute total times of a transition which the observation passes. Which part in the above equations avoids the generation of all possible paths? (b) Which part in the above equations is related to prorate count to estimate the transition probability of a transition? 09. Many NLP problems can be cast as a sequence labelling problem. Part of speech tagging is a typical example. Given a model and an observation sequence, we aim at finding the most probable state sequence. Please explain why this process is called a decoding process. In addition, please give another application which can be also treated as a sequence labelling problem. (10pts) 10. What is long-distance dependencies or unbounded dependencies? Why such kinds of linguistic phenomena are challenging in NLP? (10pts) 11. Part of speech tagging can be formulated in the following two alternatives: Model 1: \hat{t}_1^n = argmax_{t_1^n} Π_{i=1}^n P(w_i|t_i) P(t_i|t_{i-1}) Model 2: \hat{t}_1^n = argnax_{t_1^n} Π_{i=1}^n P(t_i|w_i翻譯社 t_{i-1}) Please answer the following questions. (10pts) (a) Which one is discriminative model? (b) Which one can introduce more features? (c) Which one can use Viterbi algorithm to improve the speed? (d) Which one is derived on the basis of Bayes rule? 12. The following parsing tree is selected from Chinese Treebank 8.0. What NP and VP rules can be extracted from this parsing tree to form parts of a treebank grammer? (10pts) ( (IP (IP (NP-SBJ (NN 建築)) | (VP (VC 是) | | (NP-PRD (CP-APP (IP (NP-SBJ (-NONE- *pro*)) | | | | (VP (VV 開辟) | | | | | (NP-PN-OBJ (NR 浦東)))) | | | | (DEC 的)) | | | (QP (CD 一) | | | (CLP (M 項))) | | | (ADJP (JJ 首要)) | | | (NP (NN 經濟) | | | (NN 勾當))))) | (PU 。) | (IP (NP-SBJ (-NONE- *pro*)) | (VP (DP-TMP (DT 這些) | | | (CLP (M 年))) | | (VP (VE 有) | | (IP-OBJ (NP-SBJ (NP (QP (CD 數百) | | | | | (CLP (M 家))) | | | | | (NP (NN 建築) | | | | | (NN 公司))) | | | | (PU 、) | | | | (NP (QP (CD 四千餘) | | | | | (CLP (M 個))) | | | | | (NP (NN 建築) | | | | | (NN 工地)))) | | | (VP (VV 遍布) | | | | (PP-LOC (P 在) | | | | | (LCP (NP (DP (DT 這) | | | | | | (CLP (M 片))) | | | | | | (NP (NN 熱土))) | | | | | (LC 上)))))))) | (PU 。)) )
- Jun 18 Sun 2017 09:01
蔡英文台南勘災 點名賴清德「治水有功」
- Jun 17 Sat 2017 22:17
華嚴十地品—第二離垢地(7)劃分因果 @ 普獻法師專網
- Jun 17 Sat 2017 10:32
符文筆記:依赫. 依尼. 歐米(翻譯) Aons Review: Aon Ehe, Ene, Omi ...
- Jun 16 Fri 2017 23:28
Lyon Hart 里昂哈特
- Jun 16 Fri 2017 16:07
中美文化隔閡 高階翻譯人員體悟深
從西安外國語大學高級翻譯專業結業的吳曉思,原本來美國聖地牙哥大學攻讀傳媒,但願能從事公關工作,沒想到來洛杉磯後,愈來愈多的中美影視勾當讓她再次回到了翻譯的老本行。她說,一開始只是幫伴侶忙,了局被良多公關公司認識,取得很多推薦,才發現好萊塢這幾年急需中英文雙語人材翻譯而美國本土的華裔固然有的可以說中文,卻對中國市場及情面圓滑缺少領會。她暗示,把話翻譯準確不代表這些話兩邊真的能理解,好比中國很多代表會本著以和為貴,說良多排場上的客套話,不會很快就表態,讓美方很難捉摸;而美方則進展得到最快速的答覆,講求現實。美方代表也常常暗裏問她,關於中方的真正設法和態度。