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This paper introduces a novel approach for document re-ranking in information retrieval based on topic-comment structure of texts. While most information retrieval models make the assumption that relevant documents are about the query and that aboutness can be captured considering bags of words only, we rather consider a more sophisticated analysis of discourse to capture document relevance by distinguishing...
Online health forums provide a large repository for patients, caregivers, and researchers to seek valuable information. The extraction of patient-reported personal health experience from the forums has many important applications. For example, medical researchers can discover trustable knowledge from the extracted experience. Patients can search for peers with similar experience and connect with them...
The study of German literature is mostly based on literary canons, i.e., small sets of specifically chosen documents. In particular, the history of novels has been characterized using a set of only 100 to 250 works. In this paper we address the issue of genre classification in the context of a large set of novels using machine learning methods in order to achieve a better understanding of the genre...
In this paper, we propose a method for conversation summarization. For the method, we combine two approaches, a scoring method and a machine learning technique (SVMs). First we compare important utterance extraction by the scoring method and SVMs. In the machine learning technique, we introduce verbal features, such as relations between utterances and anaphora features, and nonverbal features. Next...
Inference of high-level context is becoming crucial in development of context-aware applications. An example is social context inference - i.e., deriving social relations based upon the user's daily communication with other people. The efficiency of this mechanism mainly depends on the method(s) used to draw inferences based on existing evidence and sample information, such as a training data. Our...
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