The created thesaurus is then used to expand feature vectors to train a binary classifier .

Because this is a binary classification task (i.e.

We simply train a binary classifier using unigrams and bigrams as features from the labeled reviews in the source domains and apply the trained classifier on the target domain.

After selecting salient features, the SCL algorithm is used to train a binary classifier .

Using the extended vectors d’ to represent reviews, we train a binary classifier from the source domain labeled reviews to predict positive and negative sentiment in reviews.

Following previous work, we define cross-domain sentiment classification as the problem of learning a binary classifier (i.e.

thesaurus to expand feature vectors in a binary classifier at train and test times by introducing related lexical elements from the thesaurus.

(However, the method is agnostic to the properties of the classifier and can be used to expand feature vectors for any binary classifier ).

(2006) experiment), 2. binary classification with the split at each birth year from 1975-1988 and 3.

In contrast to Schler et al.’s experiment, our division does not introduce a gap between age groups, we do binary classification , and we use significantly less data.

0 Perform binary classification between blogs BEFORE X and IN/ AFTER X

Therefore, we experimented with binary classification into age groups using all birth dates from 1975 through 1988, thus including students from generation Y who were in college during the emergence of social media technologies.

We find five years where binary classification is significantly more accurate than other years: 1977, 1979, and 1982-1984.

We also use a supervised machine learning approach, but classification by gender is naturally a binary classification task, while our work requires determining a natural dividing point.

Then the thresholded output of this binary classifier is used as training data for learning a multi-class classifier for the 7 relation types (Bunescu and Mooney, 2005b).

When the binary classifier’s prediction probability is greater than 0.5, we take the prediction with the highest probability of the multi-class classifier as the final class label.

Specifically, we first train a binary classifier to distinguish between relation instances and non-relation instances.

Then rather than using the thresholded output of this binary classifier as training data, we use only the annotated relation instances to train a multi-class classifier for the 7 relation types.

given a test instance x , we first apply the binary classifier to it for relation detection; if it is detected as a relation instance we then apply the multi-class relation classifier to classify it4.

Given a global maximum beam-width b, we train 19 different binary classifiers , each using separate mapping functions (1)19, where the target value y produced by (13],, is 1 if Rm > k and 0 otherwise.

During decoding, we assign the beam-width for chart cell spanning wi+1...wj given models 60, 61, “.654 by finding the lowest value k such that the binary classifier 6k, classifies Rm 3 k. If no such k exists, RM is set to the maximum beam-width value b:

More generally, instead of a binary classification decision, we can also use this method to predict the desired cell population directly and get cell closure for free when the classifier predicts a beam-width of zero.

We first look at the binary classification of chart cells as either open or closed to full constituents, and predict this value from the input sentence alone.