Automatic Extraction of Examples for Word Sense Disambiguation

CHAPTER 6. AUTOMATIC EXTRACTION OF EXAMPLES FOR WSD 61
course, this is good especially for fully supervised approaches, since then the training data is
considerably less and exceptional cases can be represented only with a few instances. A negative
feature, however, is the fact that each instance is taken into account with all its features present
in the FV. Since memory-based learners are extremely sensitive to irrelevant features this leads
to a significant decrease in accuracy whenever such features are present.
For this purpose we use a forward-backward algorithm similar to (Dinu and Kübler, 2007)
or (Mihalcea, 2002) which improves the performance of a word-expert by selecting a subset of
features from the provided FV for the targeted word. This subset has less but more relevant for
the disambiguation process features.
The exact forward (9) and backward (10) algorithms that we used, following Dinu and Kübler
(2007) are as follows:
(9)
function ForwardAutomaticFeatureSelection
1: generate a pool of features PF = {Fi}
2: initialize the set of selected features with the empty set SF = {Ø}
3: extract training and testing corpora for the given target ambiguous word
4: repeat
5: for each feature Fi ∈ PF do
6: run a disambiguation experiment on the training set; each example in
the training set contains the features in SF and the feature Fi
7: determine the feature Fi leading to the best accuracy
8: remove Fi from PF and add it to SF
9: end for
10: until no improvements are obtained
(10)
function BackwardAutomaticFeatureSelection
1: generate a pool of features PF = {Fi}
2: extract training and testing corpora for the given target ambiguous word
3: repeat
4: for each PF ∩ Fi do
5: run a disambiguation experiment on the training set
6: determine the feature Fi leading to the worst accuracy
7: remove Fi from PF
8: end for
9: until improvements are obtained or the accuracy remains stable