Carolyn Penstein Rosé Language Technologies Institute Human-Computer Interaction Institute School of Computer Science With funding from the National Science Foundation and the Office of Naval Research 1 LightSIDE
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4 Click here to load a file
5 Select Heteroglossia as the predicted category
6 Make sure the text field is selected to extract text features from
Punctuation can be a “stand in” for mood “you think the answer is 9?” “you think the answer is 9.” Bigrams capture simple lexical patterns “common denominator” versus “common multiple” Trigrams (just like bigrams, but with 3 words next to each other) Carnegie Mellon University POS bigrams capture syntactic or stylistic information “the answer which is …” vs “which is the answer” Line length can be a proxy for explanation depth
Contains non-stop word can be a predictor of whether a conversational contribution is contentful “ok sure” versus “the common denominator” Remove stop words removes some distracting features Stemming allows some generalization Multiple, multiply, multiplication Removing rare features is a cheap form of feature selection Features that only occur once or twice in the corpus won’t generalize, so they are a waste of time to include in the vector space
Think like a computer! Machine learning algorithms look for features that are good predictors, not features that are necessarily meaningful Look for approximations If you want to find questions, you don’t need to do a complete syntactic analysis Look for question marks Look for wh-terms that occur immediately before an auxilliary verb
10 Click to extract text features
11 Select Logistic Regression as the Learner
12 Evaluate result by cross validation over sessions
13 Run the experiment
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A sequence of 1 to 6 categories May include GAPs Can cover any symbol GAP+ may cover any number of symbols Must not begin or end with a GAP
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Identify large error cells Make comparisons Ask yourself how it is similar to the instances that were correctly classified with the same class (vertical comparison) How it is different from those it was incorrectly not classified as (horizontal comparison) Positive Negative
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21 Error Analysis on Development Set
22 Error Analysis on Development Set
23 Error Analysis on Development Set
24 Error Analysis on Development Set
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Positive: is interesting, an interesting scene Negative: would have been more interesting, potentially interesting, etc. 27
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33 * Note that in this case we get no benefit if we use feature selection over the original feature space.
34 General Domain ADomain BGeneral Why is this nonlinear? It represents the interaction between each feature and the Domain variable Now that the feature space represents the nonlinearity, the algorithm to train the weights can be linear.
35 Healthcare Bill Dataset
36 Healthcare Bill Dataset
37 Healthcare Bill Dataset
38 Healthcare Bill Dataset
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40 Healthcare Bill Dataset
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42 Healthcare Bill Dataset
43 Healthcare Bill Dataset