Elworthy, 1994 Does Baum-Welch re-estimation help taggers

Citation

David Elworthy, Does Baum-Welch Re-estimation :Help Taggers?. In Proceedings of the Fourth Conference on Applied Natural Language Processing, 53--58

Online version

ACL anthology

Summary

This Paper originated during mid-nineties when there was increasing interest in hidden markov models for the determining POS Tagging. It built on the work done at Xerox on supervised learning. It analysed the effect of the Baum-Welch over the quality of annotations of the data.

The paper discusses two experiments -

1. Effect of initial conditions on Baum-Welch re-estimation
2. Study the behavior of Baum-Welch on a given data.

Dataset

For the first experiment, the author constructs four corpora from the LOB corpus

1. LOB-B from part B
2. LOB-L from part L
3. LOB-B-G from parts B to G inclusive
4. LOB-B-J from parts B to J inclusive

The last part is used to train the model and the other three were used as untagged data.

Design

The experiment was designed to observe the effect of the quality of data over unsupervised learning. The hand-tagged corpus was stripped of its annotations step-wise to simulate the effect of poorer training -

Lexicon

D0 Undegraded lexical probabilities, calculated from $f(i,w)/f(i)$

D1 Lexical probabilities are correctly ordered, so that the most frequent tag has highest lexical probability, but the absolute values are unreliable.

D2 Lexical probabilities are proportional to tag frequencies but independent of the actual occurring of the tag.

D3 Uniform distribution over the lexical probability. This implies that there are no prior assumptions over the data.

Transitions

T0 Undegraded transition probabilities calculated from $f(i,j)/f(i)$.

T1 Uniform distribution over the transition probabilities.

= Patterns of re-estimation

The second experiment used Penn Treebank.

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