Date: 2017-04-04 11:30
Another consideration when choosing the test set is the degree of similarity between instances in the test set and those in the development set. The more similar these two datasets are, the less confident we can be that evaluation results will generalize to other datasets. For example, consider the part-of-speech tagging task. At one extreme, we could create the training set and test set by randomly assigning sentences from a data source that reflects a single genre (news):
There's a second useful programming idiom at the beginning of , where we initialize a defaultdict and then use a for loop to update its values. Here's a schematic version:
The module _classify reaches just over 58% accuracy on the combined RTE test data using methods like these. Although this figure is not very impressive, it requires significant effort, and more linguistic processing, to achieve much better results.
Looking through this list of errors makes it clear that some suffixes that are more than one letter can be indicative of name genders. For example, names ending in yn appear to be predominantly female, despite the fact that names ending in n tend to be male and names ending in ch are usually male, even though names that end in h tend to be female. We therefore adjust our feature extractor to include features for two-letter suffixes:
Prognosis varies on an individual basis, depending on the cause, type, and severity of the language disorder. Those children who receive early intervention therapies are more likely to have a better outcome than those for whom services are delayed.
The reason that naive Bayes classifiers are called naive is that it's unreasonable to assume that all features are independent of one another (given the label). In particular, almost all real-world problems contain features with varying degrees of dependence on one another. If we had to avoid any features that were dependent on one another, it would be very difficult to construct good feature sets that provide the required information to the machine learning algorithm.
A history and physical examination are important in the evaluation of children with speech delay. The information obtained will help the physician select appropriate studies for further evaluation ( Tables 8 and 9 ).
Based on this feature extractor, we can create a list of labeled featuresets by selecting all the punctuation tokens, and tagging whether they are boundary tokens or not:
Rebuilding the classifier with the new feature extractor, we see that the performance on the dev-test dataset improves by almost 8 percentage points (from % to %):
The last parameter of sorted() specifies that the items should be returned in reverse order, . decreasing values of frequency.