Monday, April 17, 2006
SIGIR paper rejected
Hello there,
There are thousands of reasons why your paper may be rejected. There are very few why it should be accepted. For 2006, SIGIR accepted 74 out of 399 papers submitted this year and my paper belongs to those 325 papers that were just not good enough for the standard SIGIR poses. I am happy about one thing though. I got the review from some of the best known names in Information Retrieval field.
My paper was rejected basically for 3 reasons:
1. Presentation of the paper was poor - The paper I have written is marred by language and lack of good technical writing skills. It is not appealing to reviewers to read such work if they have to try hard to understand soemthing. They read hundreads of papers all the time. The quality of good paper is not just good work but also well written presentation of the work in the paper.
2. The dataset I have been using so far is classic3 dataset from cornell. It is really a tiny dataset and highly inappropriate especailly for matrix dimensionality reduction problems. The dataset to demonstrate dimensionality reduction has to be considerable in size like TREC datasets.
3. This last point was established by Dr. Xu who is also co author of this paper when he analysed why it was rejected. According to him, in a conference of the standard of SIGIR, if the paper has prpoblem defined prpoerly and the solution algorithm/approach presented clearly alongwith experimental results etc. then it is a bad paper. Surprised? I was too. But then he explained that a good paper is the one that not only explains the problem, solution approach/algorithm and experiments but it also conducts comparitive analysis of the algorithm with existing approaches. It also will focus on error bounds of the algorithm and it will conduct extensive research with many different datasets of different sizes and different fields. That's a good paper.
Considering the above 3 points mentioned, I must admit I have a long way to go. I am disappointed but have not given up. Failure like this is part of learning process. Being in a university like UALR, you learn to live with on-site training rather than established proven methodical approaches taught at big universities.
I have chosen this path and I have to make it work with perseverence and hard work.
Bye for now.
There are thousands of reasons why your paper may be rejected. There are very few why it should be accepted. For 2006, SIGIR accepted 74 out of 399 papers submitted this year and my paper belongs to those 325 papers that were just not good enough for the standard SIGIR poses. I am happy about one thing though. I got the review from some of the best known names in Information Retrieval field.
My paper was rejected basically for 3 reasons:
1. Presentation of the paper was poor - The paper I have written is marred by language and lack of good technical writing skills. It is not appealing to reviewers to read such work if they have to try hard to understand soemthing. They read hundreads of papers all the time. The quality of good paper is not just good work but also well written presentation of the work in the paper.
2. The dataset I have been using so far is classic3 dataset from cornell. It is really a tiny dataset and highly inappropriate especailly for matrix dimensionality reduction problems. The dataset to demonstrate dimensionality reduction has to be considerable in size like TREC datasets.
3. This last point was established by Dr. Xu who is also co author of this paper when he analysed why it was rejected. According to him, in a conference of the standard of SIGIR, if the paper has prpoblem defined prpoerly and the solution algorithm/approach presented clearly alongwith experimental results etc. then it is a bad paper. Surprised? I was too. But then he explained that a good paper is the one that not only explains the problem, solution approach/algorithm and experiments but it also conducts comparitive analysis of the algorithm with existing approaches. It also will focus on error bounds of the algorithm and it will conduct extensive research with many different datasets of different sizes and different fields. That's a good paper.
Considering the above 3 points mentioned, I must admit I have a long way to go. I am disappointed but have not given up. Failure like this is part of learning process. Being in a university like UALR, you learn to live with on-site training rather than established proven methodical approaches taught at big universities.
I have chosen this path and I have to make it work with perseverence and hard work.
Bye for now.
