We also found that accuracy improvements vary when parsers are retrained with domain-specific data, indicating the importance of domain adaptation and the differences in the portability of parser training methods.

2006), the C&C parser (Clark and Curran, 2004), the XLE parser (Kaplan et al., 2004), MINIPAR (Lin, 1998), and Link Parser (Sleator and Temperley, 1993; Pyysalo et al., 2006), but the domain adaptation of these parsers is not straightforward.

We also measure the accuracy improvements obtained by parser retraining with GENIA, to examine the domain portability, and to evaluate the effectiveness of domain adaptation .

Accuracy improvements in this setting indicate the possibility of domain adaptation , and the portability of the training methods of the parsers.

When the parsers are retrained with GENIA (Table 2), the accuracy increases significantly, demonstrating that the WSJ-trained parsers are not sufficiently domain-independent, and that domain adaptation is effective.

It is an important observation that the improvements by domain adaptation are larger than the differences among the parsers in the previous experiment.

A large improvement from ENJU to ENJU-GENIA shows the effectiveness of the specifically designed domain adaptation method, suggesting that the other parsers might also benefit from more sophisticated approaches for domain adaptation .

In general, our evaluation methodology can be applied to English parsers based on any framework; however, in this paper, we chose parsers that were originally developed and trained with the Penn Treebank or its variants, since such parsers can be retrained with GENIA, thus allowing for us to investigate the effect of domain adaptation .

In the subproblem of domain adaptation , a model trained over a source domain is generalized to perform well on a related target domain, where the two domains’ data are distributed similarly, but not identically.

We introduce the concept of groups of closely-related domains, called genres, and show how inter-genre adaptation is related to domain adaptation .

When only the type of data being examined is allowed to vary (from news articles to e-mails, for example), the problem is called domain adaptation (Daumé III and Marcu, 2006).

0 domain adaptation , where we assume Y (the set of possible labels) is the same for both DSOWCG and Dtafget, while DSOWCG and Dtafget themselves are allowed to vary between domains.

Domain adaptation can be further distinguished by the degree of relatedness between the source and target domains.

This study contributes to the research on sentiment tagging, domain adaptation , and the development of ensembles of classifiers (l) by proposing a novel approach for sentiment determination at sentence level and delineating the conditions under which greatest synergies among combined classifiers can be achieved, (2) by describing a precision-based technique for assigning differential weights to classifier results on different categories identified by the classifier (i.e., categories of positive vs. negative sentences), and (3) by proposing a new method for sentiment annotation in situations where the annotated in-domain data is scarce and insufficient to ensure adequate performance of the corpus-based classifier, which still remains the preferred choice when large volumes of annotated data are available for system training.

(2007) applied structural correspondence learning (Drezde et al., 2007) to the task of domain adaptation for sentiment classification of product reviews.

In sentiment tagging and related areas, Aue and Gamon (2005) demonstrated that combining classifiers can be a valuable tool in domain adaptation for sentiment analysis.

The first part of this paper reviews the extant literature on domain adaptation in sentiment analysis and highlights promising directions for research.