Background: Research scientists and companies working in the domains of biomedicine and genomics are increasingly faced with the problem of efficiently locating, within the vast body of published scientific findings, the critical pieces of information that are needed to direct current and future research investment.

In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rulebased classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that though no labeled training data are required, it allows a classifier to capture in-domain knowledge by training a supervised classifier with in-domain features, such as bag of words, on instances labeled by a rule-based classifier. Thus, this approach can be considered as a simple and effective method for domain adaptation. Among the list of components of this approach, we investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. In particular, the former addresses the issue in how far linguistic modeling is relevant for this task. We not only examine how this method performs under more difficult settings in which classes are not balanced and mixed reviews are included in the data set but also compare how this linguistically-driven method relates to state-of-the-art statistical domain adaptation.

In a complex sentence comprised of one or more subclauses, the overt or hidden attitudes between the various entities depend on the factuality projection of the verbs, their polar effects, and the modality and affirmative status (negated or not) of the clauses. If factuality is given, some referents might even be considered to benefit or to suffer from the (effects of the) described situation, independently of their relations to the other referents. An interesting question is, how the reader evaluates all this from his/her perspective. We introduce an approach based on Description Logics that integrates these various perspectives into a joint model.

We argue that in order to detect stance, not only the explicit attitudes of the stance holder towards the targets are crucial. It is the whole narrative the writer drafts that counts, including the way he hypostasizes the discourse referents: as benefactors or villains, as victims or beneficiaries. We exemplify the ability of our system to identify targets and detect the writer's stance towards them on the basis of about 100 000 Facebook posts of a German right-wing party. A reader and writer model on top of our verb-based attitude extraction directly reveal stance conflicts.

This paper describes the details of our system submitted to the SemEval-2014 shared task about aspect-based sentiment analysis on review texts. We participated in subtask 2 (prediction of the polarity of aspect terms) and 4 (prediction of the polarity of aspect categories). Our approach to determine the sentiment of aspect terms and categories is based on linguistic preprocessing, including a compositional analysis and a verb resource, task-specific feature engineering and supervised machine learning techniques. We used a Logistic Regression classifier to make predictions, which were ranked above-average in the shared task.