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 perform better or worse than supervised stacking.

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. [1]

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 [2] contains web pages from 4 universities, labeled with whether they are professor, student, project, or other pages.

Techniques

First try out some basic semi-supervised learning algorithms as the base learner of stacking, such as K.Nigam, et al. [3]

   * Which data you plan to use.
   * What you plan to do with the data, what questions you plan to answer, and if appropriate, who will be working on what aspects of the problem.
   * Why you think this is interesting - and if you published the work, what community (eg, what conference) you think the work would be most relevant to.
   * Any relevant superpowers you might have
   * How you plan to evaluate your work- and if you need to do any labeling for training and evaluation, estimate how long this will take.
   * What techniques you plan to use - and some citations for these methods. If you plan to use existing implementations, even as baselines, also describe where they are available and what evidence you have that they are stable.