This paper describes the first edition of the Domain Adaptation Task at ImageCLEF 2014. Domain adaptation refers to the challenge of leveraging over knowledge acquired when learning to recognize given classes on a database, when using a different data collection. We describe the scientific motivations behind the task, the research challenge on which the 2014 edition focused, the data and evalua...

There has a large scale annotated newswire data for Chinese word segmentation. However, some research proves that the performance of the segmenter has significant decrease when applying the model trained on the newswire to other domain, such as patent and literature. The same character appeared in different words may be in different position and with different meaning. In this paper, we introdu...

In this paper we propose a method to improve the quality of extractive summarization for contact center dialogues in various domains by making use of training samples whose domains are different from that of the test samples. Since preparing sufficient numbers of training samples for each domain is too expensive, we leverage references from many different domains and employ the Augmented Space ...

In this paper we present a domain adaptation technique for formant estimation using a deep network. We first train a deep learning network on a small read speech dataset. We then freeze the parameters of the trained network and use several different datasets to train an adaptation layer that makes the obtained network universal in the sense that it works well for a variety of speakers and speec...

Classification systems are typically domain-specific, and the performance decreases sharply when transferred from one domain to another domain. Building these systems involves annotating a large amount of data for every domain, which needs much human labor. So, a reasonable way is to utilize labeled data in one existing (or called source) domain for classification in target domain. To address t...

We tackled the Evalita 2011 Domain Adaptation task with a strategy of active learning. The DeSR parser can be configured to provide different measures of perplexity in its own ability to parse sentences correctly. After parsing sentences in the target domain, a small number of the sentences with the highest perplexity were selected, revised manually and added to the training corpus in order to ...

We introduce an extension to CCG that allows form and function to be represented simultaneously, reducing the proliferation of modifier categories seen in standard CCG analyses. We can then remove the non-combinatory rules CCGbank uses to address this problem, producing a grammar that is fully lexicalised and far less ambiguous. There are intrinsic benefits to full lexicalisation, such as seman...

A subject independent computational framework is one which does not require to be calibrated by the specific subject data to be ready to be used on the subject. The greatest challenge in developing such a framework is the variation in parameters across subjects which is termed as subject based variability. Spectral and amplitude variations in surface myoelectric signals (SEMG) are analyzed to d...