Difference between revisions of "Xuehan Xiong's project abstract"
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In lots of NLP tasks, given a limited amount of labeled data semi-supervised learning is able to take advantage of | In lots of NLP tasks, given a limited amount of labeled data semi-supervised learning is able to take advantage of | ||
the "cheap" unlabeled data and outperform the same supervised techniques. | the "cheap" unlabeled data and outperform the same supervised techniques. | ||
| − | Stacked Sequential Learning | + | Stacked Sequential Learning also shows its advantage over probabilistic graphical models on various NLP tasks. |
However, little work has been done to extend stacking into a semi-supervised framework. | However, little work has been done to extend stacking into a semi-supervised framework. | ||
| Line 16: | Line 16: | ||
4. Analyze the reason why it performs better or worse than supervised stacking. | 4. Analyze the reason why it performs better or worse than supervised stacking. | ||
| + | |||
| + | == Techniques == | ||
| + | First try out some basic semi-supervised learning algorithms as the base learner of stacking, | ||
| + | such as K.Nigam, et al. [http://www.kamalnigam.com/papers/emcat-mlj99.pdf], | ||
| + | Y. Grandvalet [http://www.eprints.pascal-network.org/archive/00001978/01/grandvalet05.pdf], | ||
| + | and K. P. Bennett [http://www1.cs.columbia.edu/~dplewis/candidacy/bennett98semisupervised.pdf]. | ||
| + | Then, based on the outcome I will try other ways to improve the algorithm. | ||
== Experiments == | == Experiments == | ||
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contains web pages from 4 universities, labeled with whether they are professor, student, project, or other pages. | contains web pages from 4 universities, labeled with whether they are professor, student, project, or other pages. | ||
| − | + | 3. The same experiments that W. Cohen did in his stacking paper. | |
| − | + | In this case we can directly compare the supervised stacking and semi-supervised version. | |
| − | + | This depends on the availability of the data. | |
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Latest revision as of 00:12, 8 October 2010
Contents
Team
Xuehan Xiong. [xxiong@andrew.cmu.edu]
Motivation
In lots of NLP tasks, given a limited amount of labeled data semi-supervised learning is able to take advantage of the "cheap" unlabeled data and outperform the same supervised techniques. Stacked Sequential Learning also shows its advantage over probabilistic graphical models on various NLP tasks. However, little work has been done to extend stacking into a semi-supervised framework.
Goal
1. Extend stacked sequential learning to a semi-supervised base.
2. Compare this algorithm with other structural semi-supervised algorithms.
3. Compare this approach with the original stacking.
4. Analyze the reason why it performs better or worse than supervised stacking.
Techniques
First try out some basic semi-supervised learning algorithms as the base learner of stacking, such as K.Nigam, et al. [1], Y. Grandvalet [2], and K. P. Bennett [3]. Then, based on the outcome I will try other ways to improve the algorithm.
Experiments
To better understand the pros and cons of my algorithm, I will run different the algorithms over different tasks if time allows. The experiments to do as follows:
1. I will evaluate my algorithm on the task of Named Entity Recognition for emails. I will use a public available email datasets. [4]
2. Also I will run my algorithm on another popular task -- web page classification. Co-training has been shown to be very effective on this task. It would be interesting to compare my algorithm with co-training. This dataset [5] contains web pages from 4 universities, labeled with whether they are professor, student, project, or other pages.
3. The same experiments that W. Cohen did in his stacking paper. In this case we can directly compare the supervised stacking and semi-supervised version. This depends on the availability of the data.
