In addition, we showed that our system is a flexible and modular framework that is able to learn from data with different quality (perfect vs noisy markable detection) and domain; and is able to deliver good results for shallow information spaces and competitive results for rich feature spaces .

Typical systems use a rich feature space based on lexical, syntactic and semantic knowledge.

We view association information as an example of a shallow feature space which contrasts with the rich feature space that is generally used in CoRe.

The feature spaces are the shallow and rich feature spaces .

These researchers show that a “deterministic” system (essentially a rule-based system) that uses a rich feature space including lexical, syntactic and semantic features can improve CoRe performance.

To summarize, the advantages of our self-training approach are: (i) We cover cases that do not occur in the unlabeled corpus (better recall effect); and (ii) we use the leveraging effect of a rich feature space including distance, person, number, gender etc.

We build a contextual feature space , described in section 4.2, to enhance our baseline bag-of-words model.

4.2 Contextual Feature Space Additions

0 Baseline: This model uses a bag-of-words feature space as input to an SVM classifier.

We study the polarity-bearing topics extracted by J ST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset.

We study the polarity-bearing topics extracted by the JST model and show that by augmenting the original feature space with polarity-bearing topics, the performance of in-domain supervised classifiers learned from augmented feature representation improves substantially, reaching the state-of-the-art results of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset.

In this paper, we have studied polarity-bearing topics generated from the J ST model and shown that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance on both the movie review data and the multi-domain sentiment dataset.

First, polarity-bearing topics generated by the J ST model were simply added into the original feature space of documents, it is worth investigating attaching different weight to each topic

proposed a kemel-mapping function which maps both source and target domains data to a high-dimensional feature space so that data points from the same domain are twice as similar as those from different domains.

Because considering such features would increase the size of the feature space , we suspected that including these features would also benefit from algorithmic means of selecting n-grams that are indicative of particular lects, and even from binning these relevant n-grams into sets to be used as features.

Although this approach to partitioning is simple and worthy of improvement, it effectively reduced the dimensionality of the feature space .

Therefore, as we explored the feature space , small bins of different n-gram lengths were merged.