This was the end day of 2005. After a nice discussion and preparation, we hold this celebrating New Year football match. Our opponent was ITNLP. At 9:00, we started our game on time.
We had 11 vs. 11 means. During the two hours, we had a nice competition. Finally, we had the score 2 vs. 2. It was just a deuce. We all fell tired. Liqi Gao and Jianguo Lin had been little injured during the match. We had encourage and physical strength in the game.
It was the first match between our IRLab and ITNLP. Nice match, just for New Year. Thanks to every body in the match!
2005年12月31日
2005年12月30日
Bug on Map
Map a nice container of C++. I love it. But this afternoon, after debugged four hours, I found that there was a bug in my mind about using map.
There was a sample code:
------------------------------------
#include <map>
#include <string>
using namespace std;
int main()
{
map mapTest;
mapTest["Good"] = 2;
mapTest["Morning"] = 3;
mapTest["To"] = 4;
mapTest["You"] = 5;
return 1;
}
------------------------------------
In the above program, before returning, the map content is:
=======Content of mapTest=======
Good 2
Morning 3
To 4
You 5
Then I used a search on this map by "YYThanks" as index, as following:
------------------------------------
#include
There was a sample code:
------------------------------------
#include <map>
#include <string>
using namespace std;
int main()
{
map
mapTest["Good"] = 2;
mapTest["Morning"] = 3;
mapTest["To"] = 4;
mapTest["You"] = 5;
return 1;
}
------------------------------------
In the above program, before returning, the map content is:
=======Content of mapTest=======
Good 2
Morning 3
To 4
You 5
Then I used a search on this map by "YYThanks" as index, as following:
------------------------------------
#include
2005年12月29日
Bayes Model for recognition
Doing is harder than thinking. It is my practice conclusion. These days, I was trying Naive Bayes Model for my gender recognition task. First, I deduced the formulation of Naives Bayes for my recognition problem. There were two experiments I should finish. But how to write program for realization the formulations? This was my first time using Naive Bayes Model. There were many little-big or big-little questions laying front to me.
For example, how to compute the model? Which parameter should be calculated? How to use the model for open testing? After asking help from many friends, I studied out the experiment plan for my plan.
After this process, I had been familiar with Naive Bayes Model a lot. Before today, I had been known Baive was very easy to use. However, now, I knew nothing was easy. If you doubt it, just do it?
For example, how to compute the model? Which parameter should be calculated? How to use the model for open testing? After asking help from many friends, I studied out the experiment plan for my plan.
After this process, I had been familiar with Naive Bayes Model a lot. Before today, I had been known Baive was very easy to use. However, now, I knew nothing was easy. If you doubt it, just do it?
2005年12月28日
Coreference Resolution Research State
This afternoon, our Text Mining Group had a weekly group meeting. This time, I presented the research situation of our CR(coreference resolution) sub-group.
Abstract :
After a long time doing projects, I return to my favorite research on coreference resolution. Our CR(Coreference resolution) group will do deep research. In the presentation, we will conclude the tortuous past works, show you the current wonderful research on gender and number recognition, and put forward our magnificent futures. Although it is a short draft, we will do it out and out.
Abstract :
After a long time doing projects, I return to my favorite research on coreference resolution. Our CR(Coreference resolution) group will do deep research. In the presentation, we will conclude the tortuous past works, show you the current wonderful research on gender and number recognition, and put forward our magnificent futures. Although it is a short draft, we will do it out and out.
2005年12月27日
On Enlish Learning
“骐骥一跃，不能十步；驽马十驾，功在不舍。”---《荀子》劝学篇
I have collected some nice websites for English learning. They are
VOA美国之音 http://www.voa.gov
英国BBC网站 http://www.bbc.co.uk
美国CNN网站 http://www.cnn.com
剑桥辞典在线 http://www.dictionary.cambridge.org
Merriam-Webster辞典在线 http://www.m-w.com
朗文网络辞典 http://www.longmanwebdict.com
柯林斯在线词库 http://www.cobuild.collins.co.uk
Encarta Online微软百科全书 http://www.encarta.msn.com
美国《商业周刊》网站 http://www.businessweek.com
英国《经济学人》网站 http://www.economist.com
美国《国家地理》网站 http://www.nationalgeographic.com
美国《首映》网站 http://www.premiere.com
美国Billboard音乐网站 http://www.billboard.com
新东方学校网站 http://www.neworiental.org
One more nice blog about English learning is as following:
古德明每日开讲.
I think it is very good!
I have collected some nice websites for English learning. They are
VOA美国之音 http://www.voa.gov
英国BBC网站 http://www.bbc.co.uk
美国CNN网站 http://www.cnn.com
剑桥辞典在线 http://www.dictionary.cambridge.org
Merriam-Webster辞典在线 http://www.m-w.com
朗文网络辞典 http://www.longmanwebdict.com
柯林斯在线词库 http://www.cobuild.collins.co.uk
Encarta Online微软百科全书 http://www.encarta.msn.com
美国《商业周刊》网站 http://www.businessweek.com
英国《经济学人》网站 http://www.economist.com
美国《国家地理》网站 http://www.nationalgeographic.com
美国《首映》网站 http://www.premiere.com
美国Billboard音乐网站 http://www.billboard.com
新东方学校网站 http://www.neworiental.org
One more nice blog about English learning is as following:
古德明每日开讲.
I think it is very good!
2005年12月26日
Reading Group--My presentation
This afternoon, from 16:00, we started our reading group. It was my turn to give presentation. After a week's preparation, I worked out 31 slides for the presentation. At the beginning, I introduced the reason why I chose the paper for gender recognition, and some research background about anaphora resolution research to our sub group.
I had invited many friends to my presentation, when it was starting, many Ph.D. candidates came here. My supervisor Prof. Tliu came here also. During the one hour's presentation, I introduced the paper in deep detail. And finally, I concluded the research of this paper and gave some plan on my current research. Many attendees gave nice advices and suggestion to my research. During the discussion, we found some doubts to this paper. I would send mail to discuss with the author.
Prof. Tliu gave me some good suggestion about my current research. He advised me to use some long distance context information for anaphora resolution. Chengjie Sun and Guanglu Sun thought using the gender type based on parsed corpus and web was not enough for anaphora resolution. After our discussion, we all believed that we should use the context to bind it. Hongfei Jiang took part in our reading group firstly. He gave me some suggestion about context modeling. But I listed the problems on context modeling. Maybe after some days, I would discuss it with Jiang. Wanxiang Che thought using linear kernel could not combine the expected value and variance squared enough. As I did not know more about SVM kernels, I could not discuss it more with Wanxiang. But I believed using SVM was a kind of combination for the 20 expected values and variance squared. Maybe we will discuss later.
Do you still remember the slogan of HIT machine learning group? Let intercommunion to be a habit. In that spirit, I knew the virtues of intercommunion. Yeah! After the reading group, I knew more about this research topic. There were so many suggestions and advices that I will learn. I liked this form.
After the meeting, in our research room, Prof. Tliu gave me three advices on my presentation. The first was my speech was so quick that many people could follow me. I should slow done my speed. Second, I would decrease the walking frequency. Audience would like pay more attention on the moving objects. If I was walking before the screen, the presentation effect would be decreased. The final problem was my poor English pronunciations. Yeah! It was a very serious problem to my English presentation. I had not spent more time on it. After the half and one hour's presentation, I felt little pain of my voice. I suggested to myself that I should learn and practice some on pronunciations and voice.
Yeah! I'd like to list the gains of my reading group presentation as following:
1. Considering the context modeling techniques.
2. Using long distance context information for enhancing the performance of anaphora resolution.
3. Learning more on SVM
4. Practice more on pronunciation and voice.
5. Reduce the walking frequency before the screens.
6. Discuss more with other researchers.
You can download my presentation slides here: Automatic Acquisition of Gender Information for Anaphora Resolution
I had invited many friends to my presentation, when it was starting, many Ph.D. candidates came here. My supervisor Prof. Tliu came here also. During the one hour's presentation, I introduced the paper in deep detail. And finally, I concluded the research of this paper and gave some plan on my current research. Many attendees gave nice advices and suggestion to my research. During the discussion, we found some doubts to this paper. I would send mail to discuss with the author.
Prof. Tliu gave me some good suggestion about my current research. He advised me to use some long distance context information for anaphora resolution. Chengjie Sun and Guanglu Sun thought using the gender type based on parsed corpus and web was not enough for anaphora resolution. After our discussion, we all believed that we should use the context to bind it. Hongfei Jiang took part in our reading group firstly. He gave me some suggestion about context modeling. But I listed the problems on context modeling. Maybe after some days, I would discuss it with Jiang. Wanxiang Che thought using linear kernel could not combine the expected value and variance squared enough. As I did not know more about SVM kernels, I could not discuss it more with Wanxiang. But I believed using SVM was a kind of combination for the 20 expected values and variance squared. Maybe we will discuss later.
Do you still remember the slogan of HIT machine learning group? Let intercommunion to be a habit. In that spirit, I knew the virtues of intercommunion. Yeah! After the reading group, I knew more about this research topic. There were so many suggestions and advices that I will learn. I liked this form.
After the meeting, in our research room, Prof. Tliu gave me three advices on my presentation. The first was my speech was so quick that many people could follow me. I should slow done my speed. Second, I would decrease the walking frequency. Audience would like pay more attention on the moving objects. If I was walking before the screen, the presentation effect would be decreased. The final problem was my poor English pronunciations. Yeah! It was a very serious problem to my English presentation. I had not spent more time on it. After the half and one hour's presentation, I felt little pain of my voice. I suggested to myself that I should learn and practice some on pronunciations and voice.
Yeah! I'd like to list the gains of my reading group presentation as following:
1. Considering the context modeling techniques.
2. Using long distance context information for enhancing the performance of anaphora resolution.
3. Learning more on SVM
4. Practice more on pronunciation and voice.
5. Reduce the walking frequency before the screens.
6. Discuss more with other researchers.
You can download my presentation slides here: Automatic Acquisition of Gender Information for Anaphora Resolution
2005年12月25日
Merry Christmas Day
Christmas is here! In the morning, Yajie and me went to our lab. We stuck some pictures on the wall. When we looked around of our lab room, we all believed it was beautiful scene.
It was Christmas day and Sunday. We went to watch movies in the Cultural Palace of Harbin Railway. The movie was the newest one: The Promise. Although it was Christmas day and Sunday, there were very few people. The scene of the film was great. We all liked it.
At five o'clock this afternoon, Yajie returned to her campus. It was a nice Christmas Day.
It was Christmas day and Sunday. We went to watch movies in the Cultural Palace of Harbin Railway. The movie was the newest one: The Promise. Although it was Christmas day and Sunday, there were very few people. The scene of the film was great. We all liked it.
At five o'clock this afternoon, Yajie returned to her campus. It was a nice Christmas Day.
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