Xuehan Xiong's project abstract

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.