Never to believe you had known enough. I thought it was definitely right.
This morning, our NLC group had a paper plan discussion. The motivation was that all the researchers should introduce your current papers ideas and then discuss with others. I believed it was just like a brainstorm meeting. In my opinion, such meeting should include many participators who had widely eyereach.
I hared some nice ideas of ongoing projects and papers. Each time others would give some nice suggestions and advices. They indexed many researchers' current works and many wonderful papers contribution. They could list them very clearly. Then I admired them very much!
It gave me a hint for research. You should keep reading every day. If not, you would behind the times.
2005年10月19日
2005年10月18日
Find encoding problem with French
Nowadays, I was processing some French corpus. I programmed in C#. Original I used
StreamReader SR = new StreamReader(FilePath);
StreamWriter SW = new StreamWriter(FilePath);
to read the files.
But there was some errors during the process. To English, it was right. But to French, all the hats of some characters changed their form. The hats were lose. It was a terrible thing.
After debugging and searching solution, I used
StreamReader SR = new StreamReader(InFilePath,System.Text.Encoding.Default);
StreamWriter SW = new StreamWriter(OutFilePath,false,System.Text.Encoding.Default);
to read and write file respectively.
Now I know, French should be used some western encoding format to read and write. In C#, you use System.Text.Encoding.Defaultcan solve it.
StreamReader SR = new StreamReader(FilePath);
StreamWriter SW = new StreamWriter(FilePath);
to read the files.
But there was some errors during the process. To English, it was right. But to French, all the hats of some characters changed their form. The hats were lose. It was a terrible thing.
After debugging and searching solution, I used
StreamReader SR = new StreamReader(InFilePath,System.Text.Encoding.Default);
StreamWriter SW = new StreamWriter(OutFilePath,false,System.Text.Encoding.Default);
to read and write file respectively.
Now I know, French should be used some western encoding format to read and write. In C#, you use System.Text.Encoding.Defaultcan solve it.
2005年10月17日
1st IJARC
The whole day, we joint in the First MS IJARC Symposium on Natural Language Processing. MS IJARC stood by Microsoft Institute for Japanese Academic Research Collaboration. It was established in 2005. We had the opportunity to study the lectures from Japanese researchers.
There were many nice presentations.
Bill Dolan, who was the manager of Redmond NLP group of MSR, introduced many research topics of them. I was interested in their Multi-document Summarization, MindNet, and paraphrase projects. They were very related to our research of IRLab.
Takashi Ninomiya, from Japan, introduced his beaming search for Probabilistic HPSG Parsing. The basic idea was using some dynamic programming technology for searching the best parsing result. Comparing common parsing technology, it was of very high speed. But I challenged his method losing some final precision.
It was a nice trick for parsing very huge corpus using little loss of precision for thousands of times speed. It was a good idea for huge-scale corpus processing.
During the break, I asked a question with Junichi Tsujii, who was professor of university of Tokyo. He was the Ph.D. supervisor of Hang Li. There was a parsing example of Takashi Ninomiya. It was "I saw the girl with telescope." It was the famous ambiguity sentence to paring. We could not make sure the final parsing result directly by the sentence itself. We should use some context information to disambiguate it. My question was how to use context information to solve this problem. And whether there were somebody had used such method. Prof. Junichi Tsujii explained it very clearly. He said because we could not model the local context well, this problem was very hard to solve. In the past twenty years, some researchers had tried to combining some heuristic rules but only got very little effect. Now there was not any nice result on it. So I believed it was a nice research topic based on the recent useful technologies.
Chengling Huang, who was an elder man of NLC group of MSRA, introduced the word segmentation error auto detecting method. His final words explained one good idea. To each NLP domain, you must give out a specification and define your problem very well and then you can do some works. I remembered some of my tagging works on coreference research. I should re-check it again after I returned to Harbin.
Cheng Niu, who was my mentor now, introduced his paper Word Independent Context Pair Classification Model for Word Sense Disambiguation. His idea was on context modeling with many features. But his research perspective on WSD was of coreference resolution. It meant that WSD processed the same mention with different meaning. It was a good idea. So we could re-define coreference problem based on other related research.
Nice chance for listening such meeting.
There were many nice presentations.
Bill Dolan, who was the manager of Redmond NLP group of MSR, introduced many research topics of them. I was interested in their Multi-document Summarization, MindNet, and paraphrase projects. They were very related to our research of IRLab.
Takashi Ninomiya, from Japan, introduced his beaming search for Probabilistic HPSG Parsing. The basic idea was using some dynamic programming technology for searching the best parsing result. Comparing common parsing technology, it was of very high speed. But I challenged his method losing some final precision.
It was a nice trick for parsing very huge corpus using little loss of precision for thousands of times speed. It was a good idea for huge-scale corpus processing.
During the break, I asked a question with Junichi Tsujii, who was professor of university of Tokyo. He was the Ph.D. supervisor of Hang Li. There was a parsing example of Takashi Ninomiya. It was "I saw the girl with telescope." It was the famous ambiguity sentence to paring. We could not make sure the final parsing result directly by the sentence itself. We should use some context information to disambiguate it. My question was how to use context information to solve this problem. And whether there were somebody had used such method. Prof. Junichi Tsujii explained it very clearly. He said because we could not model the local context well, this problem was very hard to solve. In the past twenty years, some researchers had tried to combining some heuristic rules but only got very little effect. Now there was not any nice result on it. So I believed it was a nice research topic based on the recent useful technologies.
Chengling Huang, who was an elder man of NLC group of MSRA, introduced the word segmentation error auto detecting method. His final words explained one good idea. To each NLP domain, you must give out a specification and define your problem very well and then you can do some works. I remembered some of my tagging works on coreference research. I should re-check it again after I returned to Harbin.
Cheng Niu, who was my mentor now, introduced his paper Word Independent Context Pair Classification Model for Word Sense Disambiguation. His idea was on context modeling with many features. But his research perspective on WSD was of coreference resolution. It meant that WSD processed the same mention with different meaning. It was a good idea. So we could re-define coreference problem based on other related research.
Nice chance for listening such meeting.
2005年10月16日
ICCV 2005
The tenth IEEE International Conference on Computer Vision(ICCV 2005) had been hold in Beijing in these days. The host was Microsoft Research Asia(MSRA). This afternoon, there was a MSRA Open House for ICCV2005. At the open house, MSRA exhibited some of their recent works and demos, and provided an opportunity for chatting with MSRA researchers and with each other in a relaxed environment. MSRA also hosted a reception at the end of the open house.
Before four days, Eileen talked with our six guys about touring MSRA in ICCV Open house. She invited us as volunteers for guiding and introducing shortly to the researchers all round the world. It was a first-class international conference on computer vision. Then there were about three hounded visitors. MSRA arranged one visiting tour for MSRA. We would be the guides for them.
This afternoon, the tour was on schedule. In B1 meeting room, at first they joined in a short meeting on routine introduction about MSRA by Harry and two managers. Then it was the time for our guiding. We six volunteers called them in front of the four lefts. We divided people into many groups each with ten. After sent five groups into the lefts, I stayed there for arranging the others. But the other five volunteers did not return soon. Finally, I only brought the final 16 visitors to the 5/F.
Fortunately we had a short preparation before some days. I guided them 5/F first. Our route was lounge, zero room, poster show, family day show, East region, drinking room. There were many questions from the researchers. Then I led them to the ATC region in 3/F. They all liked my introduction.
After my first turn, there were only 5 people for my touring. I repeated the same route and introduction. During the whole process, we used English only. Many of them were interested in the common life of us.
After our guiding for tour MSRA&ATC, there was a very nice demos show. I believed they were very beautiful. I saw many new demos. On schedule, we six guys had a nice dinner with the visitors.
This was my first chance for joining in such conference. I received a business card. His name was Chengyang, Xu. He was a PhD in Siemens Corporate Research, Inc. He was a nice man.
Before four days, Eileen talked with our six guys about touring MSRA in ICCV Open house. She invited us as volunteers for guiding and introducing shortly to the researchers all round the world. It was a first-class international conference on computer vision. Then there were about three hounded visitors. MSRA arranged one visiting tour for MSRA. We would be the guides for them.
This afternoon, the tour was on schedule. In B1 meeting room, at first they joined in a short meeting on routine introduction about MSRA by Harry and two managers. Then it was the time for our guiding. We six volunteers called them in front of the four lefts. We divided people into many groups each with ten. After sent five groups into the lefts, I stayed there for arranging the others. But the other five volunteers did not return soon. Finally, I only brought the final 16 visitors to the 5/F.
Fortunately we had a short preparation before some days. I guided them 5/F first. Our route was lounge, zero room, poster show, family day show, East region, drinking room. There were many questions from the researchers. Then I led them to the ATC region in 3/F. They all liked my introduction.
After my first turn, there were only 5 people for my touring. I repeated the same route and introduction. During the whole process, we used English only. Many of them were interested in the common life of us.
After our guiding for tour MSRA&ATC, there was a very nice demos show. I believed they were very beautiful. I saw many new demos. On schedule, we six guys had a nice dinner with the visitors.
This was my first chance for joining in such conference. I received a business card. His name was Chengyang, Xu. He was a PhD in Siemens Corporate Research, Inc. He was a nice man.
2005年10月15日
[collection]让你受用一生的做人做事36字诀
From:http://blog.china-pub.com/more.asp?name=yhqy&id=23994
一个人不管有多聪明，多能干，背景条件有多好，如果不懂得如何去做人、做事，那么他最终的结局肯定是失败。
让你受用一生的做人做事36字诀
一个人不管有多聪明，多能干，背景条件有多好，如果不懂得如何去做人、做事，那么他最终的结局肯定是失败。
做人做事是一门艺术，更是一门学问。很多人之所以一辈子都碌碌无为，那是因为他活了一辈子都没有弄明白该怎样去做人做事。
每一个人生活在现实社会中，都渴望着成功，而且很多有志之士为了心中的梦想，付出了很多，然而得到的却很少，这个问题不能不引起人们的深思：你不能说他们不够努力，不够勤劳，可为什么偏偏落得个一事无成的结局呢？这值得我们每一个人去认真思考。
从表面上看，做人做事似乎很简单，有谁不会呢？其实不然，比如说你当一名教师，你的主观愿望是当好教师，但事实上却不受学生欢迎；你去做生意，你的主观愿望是赚大钱，可偏偏就赔了本。抛开这些表层现象，去发掘问题的症结，你就会发现做人做事的确是一门很难掌握的学问。
可以这么说，做人做事是一门涉及现实生活中各个方面的学问，单从任何一个方面入手研究，都不可能窥其全貌。要掌握这门学问，抓住其本质，就必须对现实生活加以提炼总结，得出一些具有普遍意义的规律来，人们才能有章可循，而不至于迷然无绪。
读懂一个字诀，受用你一生！
一、社会交往字诀
——教你建功立业
（一）“谦”字诀
处世唯“谦”字了得，若一味狂妄自负、骄傲自大，只会失去处世的根本，落得个孤苦伶仃、千夫所指的骂名下场。
1、不可目中无人
2、得意不要忘形
3、有本事不必自夸
4、请教不择人
（二）“淡”字诀
为人处世，交朋待友，对势利纷华，似乎不必太过于苛求，当以“淡”字当头。看淡些，看开些，人生也就豁然开朗，有滋有味了。
正如“平平淡淡才是真”。
1、君子之交淡如水
2、淡看人生，善待生命
3、淡泊明志，莫为名利遮望眼
4、减少心欲，满足心灵
（三）“俭”字诀
不懂得“俭”字的人，不知道如何成功，任何成功的事业都在于点滴的积累；不懂得“俭”字的人，只会丧失成功，过分的骄奢多败人品质。
“俭以养德”，为人做事之良训。
1、从节省生活费开始
2、“穷大方”不可取
3、谨防变态的节俭：吝啬
4、欲路勿染，俭以养德
（四）“自”字诀
做一个有个性的人，给自己一点自信！成功的道路靠自己闯，美好的前途来自于自强自立，不屈服于任何权威，用自我的努力找到属于你的自尊。
男儿立世，自己拍板！
1、自强自立，与成功有约
2、独品人生百态
3、用自我来挑战权威
4、自信——任你东南西北风
（五）“礼”字诀
生在礼仪之邦，做一个彬彬有礼之人。有礼之人会做人，有人缘，多朋友。有礼之人会做事，注重形象，有教养，不树敌，成功路上事事顺。
1、以礼待人
2、彬彬有礼，礼多人不怪
3、注重礼仪着装，给人良好印象
（六）“正”字诀
做一个正直的人，做一个人格健全完善的人，受人崇敬。做一个自私的人，做欺心的事，疾贤防能，与成功无缘。
1、己所不欲，勿施于人
2、嫉妒乃方正之人之大忌
3、不做欺心事，本身是一种愉悦
二、形象塑造字诀
——教你品格高雅
（七）“志”字诀
给自己一根足够长的杠杆，希望转动地球。
给自己的人生立个志愿，树个目标，树个偶像，脚踏实地，成功的意识需要培养，先立志，再与成功约会。
1、度德量力，以志立身
2、先立志，有志就有希望
3、培养成功意识：立志为王
4、树立偶像，改变自己
（八）“时”字诀
做人要惜时，做事要守时。塑造自己的形象，现代人离不开时间观念。合理安排自己的时间，有效利用自己的时间，守时、惜时、不拖延。
切记：时间就是金钱。
1、一秒值万金
2、别漠视业余时间
3、盗窃他人时间，等于谋财害命
4、按重要性办事，更能有效利用时间
（九）“勤”字诀
多一些努力，便多一些成功的机会。无数事实证明：成功的最短途径是勤奋。不要光耍嘴皮子，不要好逸恶劳，勤字当头，苍天不负有心人，天道酬勤！
1、成功的最短途径：勤奋
2、多一些努力，多一些机会
3、勤于行动，胜于勤说
（十）“实”字诀
踏踏实实做人，实实在在办事。任何一个双手插在口袋里的人，都爬不上成功的梯子。给人留下一个实在的形象，给自己的成功增添一份夯实的基础，从实际出发，对自己负责。
1、敬业，实干家的成功保障
2、把每一份工作都做好
3、双手插在口袋里的人，爬不上成功的梯子
（十一）“专”字诀
有专才有恒，有恒才有我。
你生活在一个知识大爆炸的时代，如果你是一个天才，不专心就成了你的不幸；如果你资质平凡，请不要悲观，只要你下定决心一辈子做好一件事，你就能成功。年轻人，千万别给人留下一个朝三暮四的形象。
1、把所有的鸡蛋放入一个篮子
2、多才多艺，莫如练就“独门暗器”
3、专一，让劣势变成优势
（十二）“慎”字诀
人生漫长，又短暂，关键的就几步。人性丛林，职场事业，利益多多、诱惑多多。老成不怕多，凡事应多三思，不怕一万，就怕万一。一旦伸错手，入错行，做错事，于名誉，于事业，于形象皆有不救之危。
“慎”之！
1、千万别入错行
2、想好了你再“跳”
3、不要草率行事
三、自我提升字诀
——教你拯救命运
（十三）“硬”字诀
做人难，做事难，面对千难万阻，要提升自我，不来点“硬”的怎么行？如果事有勉强，应该敢于说“不”；如果是正当利益，则应当仁不让；甚至，有时还得来点霸王硬上弓，要有“脸皮厚”的时候，也要有“头皮硬”的时候。
1、拒绝是一门艺术
2、该我的，就不要客气
3、怒发冲冠之功
4、厚脸皮做人，硬头皮做事
（十四）“小”字诀
一家海鲜连锁餐厅的老板很可能当初是水产市场练滩儿的，而一家皮鞋连锁店的老板当初可能是擦鞋的。欲做大事，赚大钱，必先做小事，赚小钱，放下架子，舍得小利。从细微处入手，先扫一屋，再扫天下！
1、一屋不扫，何以扫天下
2、先做小事，赚小钱
3、一枚钉子改变一个人的一生
（十五）“锐”字诀
小小麻雀，飞飞跳跳、争分夺秒，不停地寻觅食物。人生亦如此，面对残酷竞争，惟有锐意进取，做一个好先锋，把下一个进球当目标，敢于冒险，敢于闯荡，守株待兔的事情毕竟很渺茫。
1、不以现有成就为满足
2、锐意追求，绝不退缩
3、锐气不可抛，成功是迟早
（十六）“创”字诀
提升自我，就要有胆有识去超越自我。何谓超越？超越就是吃螃蟹，就是创新。同时创新就意味着冒险，所谓富贵险中求。想人家想不到的，做别人不敢做的，敢为天下先，在于思维的转换。
1、敢为天下先
2、打破规则的创意
3、人弃我取也能创奇迹
4、逆向思维的攻守之道
（十七）“通”字诀
穷则思变，变则通。识时务者为俊杰，通机变者为英豪。通往成功的道路不是一条，又何必在一棵树上吊死呢？抓住成功的关键，东方不亮西方亮，不管它是黑猫白猫，重要的是它能否逮“耗子”。
1、巧妙地以变应变
2、条条大道通罗马
3、成功在于通，有通才有赢
（十八）“言”字诀
把赞扬送给别人，就像把食物施舍给饥饿的乞丐一样。古往今来，不知有多少人，凭着三寸不烂之舌，改变了自己平凡的命运。说话幽默，找共同语言……一个“言”字，一生受用。
1、投其所好找话题
2、恭维是最好的“润滑剂”
3、成功人生，幽默机智
4、“流行语”为你添姿着色
四、人际互动字诀
——教你赢得朋友
（十九）“宽”字诀
人际互动，应着眼于未来，不念旧恶。原谅别人，是对待自己的最好方式——为你的仇敌而怒火中烧，烧伤的是你自己。做人做事，心胸不可太狭隘。海纳百川，靠一棵宽容的心！
1、宽恕你的敌人
2、宽容做人，宽容成事
3、乐于忘记，不念旧恶
（二十）“和”字诀
在人海中，如果我们不想孤立，那么就学会如何与人相处吧！林子大了，什么鸟都有，不要求你喜欢所有的人，但同时世上也没有什么最牛的人。
和为贵嘛，就要互相留台阶，大家给面子。
1、为他人着想，为自己铺路
2、你给别人留面子，别人给你做好事
3、夫妻之道，亦和亦智
（二十一）“信”字诀
有多少人信任你，你就拥有多少次成功的机会，“信”是什么东西？信是一种人格的力量，是超越金钱的友情，是了解、是欣赏、是覆水，具有不可逆转性。所以，言必行，行必果，能帮的忙则帮，但不可轻易许诺！
1、能帮则帮，不轻易许诺
2、言而有信，做人讲原则
3、做事先做人，做人先取信
4、信誉基石，生死友情
（二十二）“帮”字诀
“好风凭借力，送我上青天”。人际交往，互利互惠。帮助别人，就是在为自己的人情信用卡储蓄，特别是在人患难之际施于援手，救落难英雄于困顿。真心助人，其回报不言而喻。
1、助人发财，自己沾光
2、好风凭借力，借梯能登天
3、掌握时机，拉人一把
（二十三）“敬”字诀
人要面子树要皮。人存在于社会上，要扮演各种各样角色，特别是在互相的交往中，需要一定的尊严来支撑，这是人性的弱点。明白了这点，才能体会到“敬”字的必要性。
1、为尊者讳，为上司讳
2、在失意者面前不谈你的得意
3、尊敬对方的“闪光点”
（二十四）“交”字诀
人情冷暖、世态炎凉，平常朋友平常过。交朋接友，不可急功近利，友情投资，宜走长线，拜拜冷庙，烧烧冷灶，平时多烧香，哪怕是只言片语的问候，亦是交友之道。
1、闲时多烧香，急时有人帮
2、友情投资，宜走长线
3、拜冷庙，烧冷灶，交落难英雄
五、解困渡厄字诀
——教你轻松快乐
（二十五）“坚”字诀
面对挫折与困难，铭记丘吉尔的名言：“永远，永远，永远不要放弃！”其实世界上并没有什么幸运的事，就是有，也是坚持的结果。为了最后的胜利，应以坚毅不拔之志，面对种种暂时之屈辱，执着追求，不到黄河心不死！
1、厚积薄发，耐得寂寞
2、谁笑到最后，谁笑得最甜
3、执着追求，永不放弃
4、不到黄河心不死
（二十六）“谋”字诀
做人有困惑，做事有困境，面对“山重水复”之关卡，光有坚强的毅志不行，硬闯也不行。解决难题靠的是脑袋，脑袋产生思考，让思考发威，在出人意料之处轻松解决问题。
1、巧妇能为无米之炊
2、从“山重水复”到“柳暗花明”
3、思考的威力
（二十七）“屈”字诀
要摆脱人与事的困境，就难免要求人，求人就难免要低三下四，但着眼于未来的成功，即使像蟑螂一样的生活也应在所不惜，风水毕竟轮流转。放下架子，该屈就屈，能屈能伸，以屈为伸方为英雄！
1、像蟑螂一样生活
2、放下身段，前方是大道
3、你敬我一尺，我敬你一丈
4、低人一级“屈”不死人
（二十八）“静”字诀
“不在沉默中爆发，就在沉默中灭亡！”凡遇大事需静气，平心静气是一种境界，一种气度，一种修养。冷静之中的决定往往是摆脱困境的最佳方案，同时冷静也是一种智慧，以静待变，乱中取胜！
1、把冷板凳坐成经理椅
2、心宁智生，智生事成
3、沉着冷静心自怡
4、沉得住气方为人杰
（二十九）“乐”字诀
世上没有绝对幸福的人，只有不肯快乐的心。人生苦短，与其事事张弓拔弩，不如“幽它一默”。记住，成功是从微笑开始的，人生不如意事常***，乐观点，自己营造快乐，学会轻松解决难题。
1、成功从微笑开始
2、学会营造快乐
3、学会轻松愉快地解决难题
4、世上没有绝对幸福的人，只有不肯快乐的心
（三十）“靠”字诀
人生不等不靠，没错，天上不会掉馅饼，守株待兔饿死人，但一点不靠也不行，亲戚朋友、同学、老乡，这是一种“人力资源”，谁人没个三灾六难，能靠则靠，靠不上创造条件也要靠！
1、让朋友成为你的靠山
2、出门落难靠老乡
3、亲戚亲戚，越走越亲
4、恰同学少年，该靠靠一把
六、不败人生字诀
——教你人生辉煌
（三十一）“愚”字诀
学学猫头鹰，睁一只眼，闭一只眼。你说我糊涂，其实我不傻！只是世事多变幻，创业难，败家快，人说水至清则无鱼，人至察则无徒。其实是，明哲保身，大智者往往大愚，聪明者多，能过“愚”字关鲜矣！
1、糊涂人聪明一世
2、不要以为自己比别人聪明
3、处事不要太认真
4、谁是英雄？
（三十二）“忍”字诀
真的英雄，何必气短，善始善终，方为不败！忍能保身，忍能成事，忍是大智，大勇，更是大福！忍是厚，忍是黑，忍小人，忍豪强，忍天下难忍之事，不做性情中人，成常人难成之事。
1、忍是大智大勇大福
2、不做性情中人
3、不败人生，忍者无敌
（三十三）“退”字诀
久历江湖，练达人情之人都守一个“退”字。退是一种谋略，退是一种交换，更是一种维系生存的手段。哲人说的好，“不要把痰吐在井里，哪天你口渴的时侯，也要来井边喝水的。”
1、用心计较般般错，退步思量事事顺
2、拒绝妥协，就是拒绝成功
3、惹不起，躲得起
（三十四）“圆”字诀
方圆做人，八面玲珑；圆满做事，事事顺心。人心叵测，凡事最好留一手，有闲时，可研究一下“模糊哲学”，人生这套马车，如若安上方方正正的轮子，你没听说过，我也没听说过，寸步难行嘛！
1、方圆做人，圆满做事
2、做老二，不要做老大
3、人情练达即文章，处世圆通慎言语
（三十五）“危”字诀
“豪华尽出成功后，逸乐安知与祸双？”历史教训如此，平头百姓亦如此。居家过日，工作职场等都逃不过一个“危”字，人无远虑，必有近忧。
1、远虑在先，近处无危
2、郭子仪屏退侍女免祸患
3、上山下乡当农民——范蠡富贵终身
（三十六）“装”字诀
人生在世一台戏，你方唱罢我上场，不管你会不会演，就看你会不会装。充英雄容易，扮弱者难。俗话说得好，枪打出头鸟，不怕贼偷就怕贼惦记着，当你还不具备实力时，请把你过剩的才华藏起来！
1、故意示弱有好处
2、用“拟态”和“保护色”
3、成功需要诈死与佯败
一个人不管有多聪明，多能干，背景条件有多好，如果不懂得如何去做人、做事，那么他最终的结局肯定是失败。
让你受用一生的做人做事36字诀
一个人不管有多聪明，多能干，背景条件有多好，如果不懂得如何去做人、做事，那么他最终的结局肯定是失败。
做人做事是一门艺术，更是一门学问。很多人之所以一辈子都碌碌无为，那是因为他活了一辈子都没有弄明白该怎样去做人做事。
每一个人生活在现实社会中，都渴望着成功，而且很多有志之士为了心中的梦想，付出了很多，然而得到的却很少，这个问题不能不引起人们的深思：你不能说他们不够努力，不够勤劳，可为什么偏偏落得个一事无成的结局呢？这值得我们每一个人去认真思考。
从表面上看，做人做事似乎很简单，有谁不会呢？其实不然，比如说你当一名教师，你的主观愿望是当好教师，但事实上却不受学生欢迎；你去做生意，你的主观愿望是赚大钱，可偏偏就赔了本。抛开这些表层现象，去发掘问题的症结，你就会发现做人做事的确是一门很难掌握的学问。
可以这么说，做人做事是一门涉及现实生活中各个方面的学问，单从任何一个方面入手研究，都不可能窥其全貌。要掌握这门学问，抓住其本质，就必须对现实生活加以提炼总结，得出一些具有普遍意义的规律来，人们才能有章可循，而不至于迷然无绪。
读懂一个字诀，受用你一生！
一、社会交往字诀
——教你建功立业
（一）“谦”字诀
处世唯“谦”字了得，若一味狂妄自负、骄傲自大，只会失去处世的根本，落得个孤苦伶仃、千夫所指的骂名下场。
1、不可目中无人
2、得意不要忘形
3、有本事不必自夸
4、请教不择人
（二）“淡”字诀
为人处世，交朋待友，对势利纷华，似乎不必太过于苛求，当以“淡”字当头。看淡些，看开些，人生也就豁然开朗，有滋有味了。
正如“平平淡淡才是真”。
1、君子之交淡如水
2、淡看人生，善待生命
3、淡泊明志，莫为名利遮望眼
4、减少心欲，满足心灵
（三）“俭”字诀
不懂得“俭”字的人，不知道如何成功，任何成功的事业都在于点滴的积累；不懂得“俭”字的人，只会丧失成功，过分的骄奢多败人品质。
“俭以养德”，为人做事之良训。
1、从节省生活费开始
2、“穷大方”不可取
3、谨防变态的节俭：吝啬
4、欲路勿染，俭以养德
（四）“自”字诀
做一个有个性的人，给自己一点自信！成功的道路靠自己闯，美好的前途来自于自强自立，不屈服于任何权威，用自我的努力找到属于你的自尊。
男儿立世，自己拍板！
1、自强自立，与成功有约
2、独品人生百态
3、用自我来挑战权威
4、自信——任你东南西北风
（五）“礼”字诀
生在礼仪之邦，做一个彬彬有礼之人。有礼之人会做人，有人缘，多朋友。有礼之人会做事，注重形象，有教养，不树敌，成功路上事事顺。
1、以礼待人
2、彬彬有礼，礼多人不怪
3、注重礼仪着装，给人良好印象
（六）“正”字诀
做一个正直的人，做一个人格健全完善的人，受人崇敬。做一个自私的人，做欺心的事，疾贤防能，与成功无缘。
1、己所不欲，勿施于人
2、嫉妒乃方正之人之大忌
3、不做欺心事，本身是一种愉悦
二、形象塑造字诀
——教你品格高雅
（七）“志”字诀
给自己一根足够长的杠杆，希望转动地球。
给自己的人生立个志愿，树个目标，树个偶像，脚踏实地，成功的意识需要培养，先立志，再与成功约会。
1、度德量力，以志立身
2、先立志，有志就有希望
3、培养成功意识：立志为王
4、树立偶像，改变自己
（八）“时”字诀
做人要惜时，做事要守时。塑造自己的形象，现代人离不开时间观念。合理安排自己的时间，有效利用自己的时间，守时、惜时、不拖延。
切记：时间就是金钱。
1、一秒值万金
2、别漠视业余时间
3、盗窃他人时间，等于谋财害命
4、按重要性办事，更能有效利用时间
（九）“勤”字诀
多一些努力，便多一些成功的机会。无数事实证明：成功的最短途径是勤奋。不要光耍嘴皮子，不要好逸恶劳，勤字当头，苍天不负有心人，天道酬勤！
1、成功的最短途径：勤奋
2、多一些努力，多一些机会
3、勤于行动，胜于勤说
（十）“实”字诀
踏踏实实做人，实实在在办事。任何一个双手插在口袋里的人，都爬不上成功的梯子。给人留下一个实在的形象，给自己的成功增添一份夯实的基础，从实际出发，对自己负责。
1、敬业，实干家的成功保障
2、把每一份工作都做好
3、双手插在口袋里的人，爬不上成功的梯子
（十一）“专”字诀
有专才有恒，有恒才有我。
你生活在一个知识大爆炸的时代，如果你是一个天才，不专心就成了你的不幸；如果你资质平凡，请不要悲观，只要你下定决心一辈子做好一件事，你就能成功。年轻人，千万别给人留下一个朝三暮四的形象。
1、把所有的鸡蛋放入一个篮子
2、多才多艺，莫如练就“独门暗器”
3、专一，让劣势变成优势
（十二）“慎”字诀
人生漫长，又短暂，关键的就几步。人性丛林，职场事业，利益多多、诱惑多多。老成不怕多，凡事应多三思，不怕一万，就怕万一。一旦伸错手，入错行，做错事，于名誉，于事业，于形象皆有不救之危。
“慎”之！
1、千万别入错行
2、想好了你再“跳”
3、不要草率行事
三、自我提升字诀
——教你拯救命运
（十三）“硬”字诀
做人难，做事难，面对千难万阻，要提升自我，不来点“硬”的怎么行？如果事有勉强，应该敢于说“不”；如果是正当利益，则应当仁不让；甚至，有时还得来点霸王硬上弓，要有“脸皮厚”的时候，也要有“头皮硬”的时候。
1、拒绝是一门艺术
2、该我的，就不要客气
3、怒发冲冠之功
4、厚脸皮做人，硬头皮做事
（十四）“小”字诀
一家海鲜连锁餐厅的老板很可能当初是水产市场练滩儿的，而一家皮鞋连锁店的老板当初可能是擦鞋的。欲做大事，赚大钱，必先做小事，赚小钱，放下架子，舍得小利。从细微处入手，先扫一屋，再扫天下！
1、一屋不扫，何以扫天下
2、先做小事，赚小钱
3、一枚钉子改变一个人的一生
（十五）“锐”字诀
小小麻雀，飞飞跳跳、争分夺秒，不停地寻觅食物。人生亦如此，面对残酷竞争，惟有锐意进取，做一个好先锋，把下一个进球当目标，敢于冒险，敢于闯荡，守株待兔的事情毕竟很渺茫。
1、不以现有成就为满足
2、锐意追求，绝不退缩
3、锐气不可抛，成功是迟早
（十六）“创”字诀
提升自我，就要有胆有识去超越自我。何谓超越？超越就是吃螃蟹，就是创新。同时创新就意味着冒险，所谓富贵险中求。想人家想不到的，做别人不敢做的，敢为天下先，在于思维的转换。
1、敢为天下先
2、打破规则的创意
3、人弃我取也能创奇迹
4、逆向思维的攻守之道
（十七）“通”字诀
穷则思变，变则通。识时务者为俊杰，通机变者为英豪。通往成功的道路不是一条，又何必在一棵树上吊死呢？抓住成功的关键，东方不亮西方亮，不管它是黑猫白猫，重要的是它能否逮“耗子”。
1、巧妙地以变应变
2、条条大道通罗马
3、成功在于通，有通才有赢
（十八）“言”字诀
把赞扬送给别人，就像把食物施舍给饥饿的乞丐一样。古往今来，不知有多少人，凭着三寸不烂之舌，改变了自己平凡的命运。说话幽默，找共同语言……一个“言”字，一生受用。
1、投其所好找话题
2、恭维是最好的“润滑剂”
3、成功人生，幽默机智
4、“流行语”为你添姿着色
四、人际互动字诀
——教你赢得朋友
（十九）“宽”字诀
人际互动，应着眼于未来，不念旧恶。原谅别人，是对待自己的最好方式——为你的仇敌而怒火中烧，烧伤的是你自己。做人做事，心胸不可太狭隘。海纳百川，靠一棵宽容的心！
1、宽恕你的敌人
2、宽容做人，宽容成事
3、乐于忘记，不念旧恶
（二十）“和”字诀
在人海中，如果我们不想孤立，那么就学会如何与人相处吧！林子大了，什么鸟都有，不要求你喜欢所有的人，但同时世上也没有什么最牛的人。
和为贵嘛，就要互相留台阶，大家给面子。
1、为他人着想，为自己铺路
2、你给别人留面子，别人给你做好事
3、夫妻之道，亦和亦智
（二十一）“信”字诀
有多少人信任你，你就拥有多少次成功的机会，“信”是什么东西？信是一种人格的力量，是超越金钱的友情，是了解、是欣赏、是覆水，具有不可逆转性。所以，言必行，行必果，能帮的忙则帮，但不可轻易许诺！
1、能帮则帮，不轻易许诺
2、言而有信，做人讲原则
3、做事先做人，做人先取信
4、信誉基石，生死友情
（二十二）“帮”字诀
“好风凭借力，送我上青天”。人际交往，互利互惠。帮助别人，就是在为自己的人情信用卡储蓄，特别是在人患难之际施于援手，救落难英雄于困顿。真心助人，其回报不言而喻。
1、助人发财，自己沾光
2、好风凭借力，借梯能登天
3、掌握时机，拉人一把
（二十三）“敬”字诀
人要面子树要皮。人存在于社会上，要扮演各种各样角色，特别是在互相的交往中，需要一定的尊严来支撑，这是人性的弱点。明白了这点，才能体会到“敬”字的必要性。
1、为尊者讳，为上司讳
2、在失意者面前不谈你的得意
3、尊敬对方的“闪光点”
（二十四）“交”字诀
人情冷暖、世态炎凉，平常朋友平常过。交朋接友，不可急功近利，友情投资，宜走长线，拜拜冷庙，烧烧冷灶，平时多烧香，哪怕是只言片语的问候，亦是交友之道。
1、闲时多烧香，急时有人帮
2、友情投资，宜走长线
3、拜冷庙，烧冷灶，交落难英雄
五、解困渡厄字诀
——教你轻松快乐
（二十五）“坚”字诀
面对挫折与困难，铭记丘吉尔的名言：“永远，永远，永远不要放弃！”其实世界上并没有什么幸运的事，就是有，也是坚持的结果。为了最后的胜利，应以坚毅不拔之志，面对种种暂时之屈辱，执着追求，不到黄河心不死！
1、厚积薄发，耐得寂寞
2、谁笑到最后，谁笑得最甜
3、执着追求，永不放弃
4、不到黄河心不死
（二十六）“谋”字诀
做人有困惑，做事有困境，面对“山重水复”之关卡，光有坚强的毅志不行，硬闯也不行。解决难题靠的是脑袋，脑袋产生思考，让思考发威，在出人意料之处轻松解决问题。
1、巧妇能为无米之炊
2、从“山重水复”到“柳暗花明”
3、思考的威力
（二十七）“屈”字诀
要摆脱人与事的困境，就难免要求人，求人就难免要低三下四，但着眼于未来的成功，即使像蟑螂一样的生活也应在所不惜，风水毕竟轮流转。放下架子，该屈就屈，能屈能伸，以屈为伸方为英雄！
1、像蟑螂一样生活
2、放下身段，前方是大道
3、你敬我一尺，我敬你一丈
4、低人一级“屈”不死人
（二十八）“静”字诀
“不在沉默中爆发，就在沉默中灭亡！”凡遇大事需静气，平心静气是一种境界，一种气度，一种修养。冷静之中的决定往往是摆脱困境的最佳方案，同时冷静也是一种智慧，以静待变，乱中取胜！
1、把冷板凳坐成经理椅
2、心宁智生，智生事成
3、沉着冷静心自怡
4、沉得住气方为人杰
（二十九）“乐”字诀
世上没有绝对幸福的人，只有不肯快乐的心。人生苦短，与其事事张弓拔弩，不如“幽它一默”。记住，成功是从微笑开始的，人生不如意事常***，乐观点，自己营造快乐，学会轻松解决难题。
1、成功从微笑开始
2、学会营造快乐
3、学会轻松愉快地解决难题
4、世上没有绝对幸福的人，只有不肯快乐的心
（三十）“靠”字诀
人生不等不靠，没错，天上不会掉馅饼，守株待兔饿死人，但一点不靠也不行，亲戚朋友、同学、老乡，这是一种“人力资源”，谁人没个三灾六难，能靠则靠，靠不上创造条件也要靠！
1、让朋友成为你的靠山
2、出门落难靠老乡
3、亲戚亲戚，越走越亲
4、恰同学少年，该靠靠一把
六、不败人生字诀
——教你人生辉煌
（三十一）“愚”字诀
学学猫头鹰，睁一只眼，闭一只眼。你说我糊涂，其实我不傻！只是世事多变幻，创业难，败家快，人说水至清则无鱼，人至察则无徒。其实是，明哲保身，大智者往往大愚，聪明者多，能过“愚”字关鲜矣！
1、糊涂人聪明一世
2、不要以为自己比别人聪明
3、处事不要太认真
4、谁是英雄？
（三十二）“忍”字诀
真的英雄，何必气短，善始善终，方为不败！忍能保身，忍能成事，忍是大智，大勇，更是大福！忍是厚，忍是黑，忍小人，忍豪强，忍天下难忍之事，不做性情中人，成常人难成之事。
1、忍是大智大勇大福
2、不做性情中人
3、不败人生，忍者无敌
（三十三）“退”字诀
久历江湖，练达人情之人都守一个“退”字。退是一种谋略，退是一种交换，更是一种维系生存的手段。哲人说的好，“不要把痰吐在井里，哪天你口渴的时侯，也要来井边喝水的。”
1、用心计较般般错，退步思量事事顺
2、拒绝妥协，就是拒绝成功
3、惹不起，躲得起
（三十四）“圆”字诀
方圆做人，八面玲珑；圆满做事，事事顺心。人心叵测，凡事最好留一手，有闲时，可研究一下“模糊哲学”，人生这套马车，如若安上方方正正的轮子，你没听说过，我也没听说过，寸步难行嘛！
1、方圆做人，圆满做事
2、做老二，不要做老大
3、人情练达即文章，处世圆通慎言语
（三十五）“危”字诀
“豪华尽出成功后，逸乐安知与祸双？”历史教训如此，平头百姓亦如此。居家过日，工作职场等都逃不过一个“危”字，人无远虑，必有近忧。
1、远虑在先，近处无危
2、郭子仪屏退侍女免祸患
3、上山下乡当农民——范蠡富贵终身
（三十六）“装”字诀
人生在世一台戏，你方唱罢我上场，不管你会不会演，就看你会不会装。充英雄容易，扮弱者难。俗话说得好，枪打出头鸟，不怕贼偷就怕贼惦记着，当你还不具备实力时，请把你过剩的才华藏起来！
1、故意示弱有好处
2、用“拟态”和“保护色”
3、成功需要诈死与佯败
Recommand nice paper: On mining cross-graph quasi-cliques
Source
Conference on Knowledge Discovery in Data
Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Chicago, Illinois, USA
SESSION: Research track paper table of contents
Pages: 228 - 238
Year of Publication: 2005
ISBN:1-59593-135-X
Pdf (574 KB): download link
Authors
Jian Pei Simon Fraser University, Canada
Daxin Jiang State University of New York at Buffalo
Aidong Zhang State University of New York at Buffalo
ABSTRACT
Joint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-market customer segmentation, a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market. As another example, in bioinformatics, by joint mining of gene expression data and protein interaction data, we can find clusters of genes which show coherent expression patterns and also produce interacting proteins. Such clusters may be potential pathways.In this paper, we investigate a novel data mining problem, mining cross-graph quasi-cliques, which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data. We build a general model for mining cross-graph quasi-cliques, show why the complete set of cross-graph quasi-cliques cannot be found by previous data mining methods, and study the complexity of the problem. While the problem is difficult, we develop an efficient algorithm, Crochet, which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques. A systematic performance study is reported on both synthetic and real data sets. We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics. The experimental results also show that algorithm Crochet is efficient and scala
Conference on Knowledge Discovery in Data
Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Chicago, Illinois, USA
SESSION: Research track paper table of contents
Pages: 228 - 238
Year of Publication: 2005
ISBN:1-59593-135-X
Pdf (574 KB): download link
Authors
Jian Pei Simon Fraser University, Canada
Daxin Jiang State University of New York at Buffalo
Aidong Zhang State University of New York at Buffalo
ABSTRACT
Joint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-market customer segmentation, a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market. As another example, in bioinformatics, by joint mining of gene expression data and protein interaction data, we can find clusters of genes which show coherent expression patterns and also produce interacting proteins. Such clusters may be potential pathways.In this paper, we investigate a novel data mining problem, mining cross-graph quasi-cliques, which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data. We build a general model for mining cross-graph quasi-cliques, show why the complete set of cross-graph quasi-cliques cannot be found by previous data mining methods, and study the complexity of the problem. While the problem is difficult, we develop an efficient algorithm, Crochet, which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques. A systematic performance study is reported on both synthetic and real data sets. We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics. The experimental results also show that algorithm Crochet is efficient and scala
2005年10月14日
Recommand nice paper: Anchor Text Mining
Source
ACM Transactions on Information Systems (TOIS)
Volume 22 , Issue 2 (April 2004)
Pdf (281 KB): download link
Authors
Wen-Hsiang Lu Academia Sinica and National Chiao Tung University, Tainan, Taiwan
Lee-Feng Chien Academia Sinica, Nangang, Taiwan
Hsi-Jian Lee National Chiao Tung University, Hsinchu, Taiwan
Pages: 242 - 269
Year of Publication: 2004
ISSN:1046-8188
ABSTRACT
To discover translation knowledge in diverse data resources on the Web, this article proposes an effective approach to finding translation equivalents of query terms and constructing multilingual lexicons through the mining of Web anchor texts and link structures. Although Web anchor texts are wide-scoped hypertext resources, not every particular pair of languages contains sufficient anchor texts for effective extraction of translations for Web queries. For more generalized applications, the approach is designed based on a transitive translation model. The translation equivalents of a query term can be extracted via its translation in an intermediate language. To reduce interference from translation errors, the approach further integrates a competitive linking algorithm into the process of determining the most probable translation. A series of experiments has been conducted, including performance tests on term translation extraction, cross-language information retrieval, and translation suggestions for practical Web search services, respectively. The obtained experimental results have shown that the proposed approach is effective in extracting translations of unknown queries, is easy to combine with the probabilistic retrieval model to improve the cross-language retrieval performance, and is very useful when the considered language pairs lack a sufficient number of anchor texts. Based on the approach, an experimental system called LiveTrans has been developed for English--Chinese cross-language Web search.
ACM Transactions on Information Systems (TOIS)
Volume 22 , Issue 2 (April 2004)
Pdf (281 KB): download link
Authors
Wen-Hsiang Lu Academia Sinica and National Chiao Tung University, Tainan, Taiwan
Lee-Feng Chien Academia Sinica, Nangang, Taiwan
Hsi-Jian Lee National Chiao Tung University, Hsinchu, Taiwan
Pages: 242 - 269
Year of Publication: 2004
ISSN:1046-8188
ABSTRACT
To discover translation knowledge in diverse data resources on the Web, this article proposes an effective approach to finding translation equivalents of query terms and constructing multilingual lexicons through the mining of Web anchor texts and link structures. Although Web anchor texts are wide-scoped hypertext resources, not every particular pair of languages contains sufficient anchor texts for effective extraction of translations for Web queries. For more generalized applications, the approach is designed based on a transitive translation model. The translation equivalents of a query term can be extracted via its translation in an intermediate language. To reduce interference from translation errors, the approach further integrates a competitive linking algorithm into the process of determining the most probable translation. A series of experiments has been conducted, including performance tests on term translation extraction, cross-language information retrieval, and translation suggestions for practical Web search services, respectively. The obtained experimental results have shown that the proposed approach is effective in extracting translations of unknown queries, is easy to combine with the probabilistic retrieval model to improve the cross-language retrieval performance, and is very useful when the considered language pairs lack a sufficient number of anchor texts. Based on the approach, an experimental system called LiveTrans has been developed for English--Chinese cross-language Web search.
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