Histopathological whole slide images of the same organ stained with the same dye exhibit substantial inter-slide variation due to the manual preparation and staining process as well as due to inter-individual variability. In order to improve the generalization ability of a classification model on data from kidney pathology, we investigate a domain adaptation approach where a classifier trained ...

Come with us to read a new book that is coming recently. Yeah, this is a new coming book that many people really want to read will you be one of them? Of course, you should be. It will not make you feel so hard to enjoy your life. Even some people think that reading is a hard to do, you must be sure that you can do it. Hard will be felt when you have no ideas about what kind of book to read. Or...

The domain adaptation task was aimed at investigating techniques for adapting state–of–the–art dependency parsing systems to new domains. Both the language dealt with, i.e. Italian, and the target domain, namely the legal domain, represent two main novelties of the task organised at Evalita 2011. In this paper, we define the task and describe how the datasets were created from different resourc...

We describe our participation in TweetMT for three language pairs in both directions: Spanish from/to Catalan, Basque and Portuguese. We used a range of techniques: statistical and rule-based MT, morph segmentation, data selection with ParFDA and system combination. As for resources, our focus was on crawling vast amounts of tweets to perform monolingual domain adaptation. Our system was the be...

We propose a verb suggestion method which uses candidate sets and domain adaptation to incorporate error patterns produced by ESL learners. The candidate sets are constructed from a large scale learner corpus to cover various error patterns made by learners. Furthermore, the model is trained using both a native corpus and the learner corpus via a domain adaptation technique. Experiments on two ...

The main goal of this work is to provide automatic transcriptions of classroom lectures for e-learning and e-inclusion applications. The first experiments using a recognition system trained for Broadcast News resulted in word error rates near 60%, clearly confirming the need for adaptation to the specific topic of the lectures, on one hand, and for better strategies for handling spontaneous spe...

We address a challenging problem frequently faced by MT service providers: creating a domainspecific system based on a purely source-monolingual sample of text from the domain. We solve this problem by introducing methods for domain adaptation requiring no in-domain parallel data. Our approach yields results comparable to state-of-the-art systems optimized on an in-domain parallel set with a dr...

We propose a simple domain adaptation method for neural networks in a supervised setting. Supervised domain adaptation is a way of improving the generalization performance on the target domain by using the source domain dataset, assuming that both of the datasets are labeled. Recently, recurrent neural networks have been shown to be successful on a variety of NLP tasks such as caption generatio...

xi