Transfer learning helps to build a system to recognize and apply knowledge and experience learned in previous tasks (source task) to new tasks or new domains (target task), which share some commonality. The two important factors that impact the performance of transfer learning models are: (a) the size of the target dataset and (b) the similarity in distribution between source and target domains...

Current statistical parsers tend to perform well only on their training domain and nearby genres. While strong performance on a few related domains is sufficient for many situations, it is advantageous for parsers to be able to generalize to a wide variety of domains. When parsing document collections involving heterogeneous domains (e.g. the web), the optimal parsing model for each document is...

Cross-domain recommender systems represent an emerging research topic as users generally have interactions with items from multiple domains. One goal of a cross-domain recommender system is to improve the quality of recommendations in a target domain by using user preference information from other source domains. We observe that, in many applications, users interact with items of different type...

Knowledge transfer from multiple source domains to a target domain is crucial in transfer learning. Most existing methods are focused on learning weights for different domains based on the similarities between each source domain and the target domain or learning more precise classifiers from the source domain data jointly by maximizing their consensus of predictions on the target domain data. H...

Domain adaptation is an important research topic in sentiment analysis area. Existing domain adaptation methods usually transfer sentiment knowledge from only one source domain to target domain. In this paper, we propose a new domain adaptation approach which can exploit sentiment knowledge from multiple source domains. We first extract both global and domain-specific sentiment knowledge from t...

An R-module M is observable ii all its elements can be distinguished by observing them by means of linear morphisms from M to R. We show that free observable R-modules can be explicitly described as the cores of the nal power domains with characteristic semiring R. Then, the general theory is applied to the cases of the lower and the upper semiring. All lower modules are observable, whereas the...

In previous work by Ahrens et al. (2003) and Chung et al. (2003a, 2003b), the extraction of conceptual metaphors was carried out based on prototypicality. Based on this view, the most prototypical mappings of source to target domains in the corpora help determining the Mapping Principle of the conceptual metaphors (Ahrens 2002). However, this approach is limited in the aspect that the identific...

The variety of power domain constructions proposed in the literature is put into a general algebraic framework. Power constructions are considered algebras on a higher level: for every ground domain, there is a power domain whose algebraic structure is speciied by means of axioms concerning the algebraic properties of the basic operations empty set, union, singleton, and extension of functions....

In this paper, we describe three distinct monoids over domains, each with a commutative analog, the latter of which define bag domain monoids. Our results were inspired by work by Varacca (Varacca 2003), and they lead to a constructive approach to his Hoare indexed valuations over a continuous poset P . We use our constructive approach to describe an analog of the probabilistic power domain and...