Andreas Juffinger

Blog credibility ranking by exploiting verified content

People use weblogs to express thoughts, present ideas and share knowledge. However, weblogs can also be misused to influence and manipulate the readers. Therefore the credibility of a blog has to be validated before the available information is used for analysis. The credibility of a blogentry is derived from the content, the credibility of the author or blog itself, respectively, and the external references or trackbacks. In this work we introduce an additional dimension to assess the credibility, namely the quantity structure. For our blog analysis system we derive the credibility therefore from two dimensions. Firstly, the quantity structure of a set of blogs and a reference corpus is compared and secondly, we analyse each separate blog content and examine the similarity with a verified news corpus. From the content similarity values we derive a ranking function. Our evaluation showed that one can sort out incredible blogs by quantity structure without deeper analysis. Besides, the content based ranking function sorts the blogs by credibility with high accuracy. Our blog analysis system is therefore capable of providing credibility levels per blog.

Discovery and evaluation of non-taxonomic relations in domain ontologies

The identification and labelling of non-hierarchical relations are among the most challenging tasks in ontology learning. This paper describes a bottom-up approach for automatically suggesting ontology link types. The presented method extracts verb-vectors from semantic relations identified in the domain corpus, aggregates them by computing centroids for known relation types, and stores the centroids in a central knowledge base. Comparing verb-vectors extracted from unknown relations with the stored centroids yields link type suggestions. Domain experts evaluate these suggestions, refining the knowledge base and constantly improving the components accuracy. A final evaluation provides a detailed statistical analysis of the introduced approach.

Objectivity classification in online media

In this work, we assess objectivity in online news media. We propose to use topic independent features and we show in a cross-domain experiment that with standard bag-of-word models, classifiers implicitly learn topics. Our experiments revealed that our methodology can be applied across different topics with consistent classification performance.

Distributed Web2.0 crawling for ontology evolution

Semantic Web technologies in general and ontologybased approaches in particular are considered the foundation for the next generation of information services. While ontologies enable software agents to exchange knowledge and information in a standardised, intelligent manner, describing todays vast amount of information in terms of ontological knowledge and to track the evolution of such ontologies remains a challenge. In this paper we describe Web2.0 crawling for ontology evolution. The World Wide Web, or Web for short, is due, its evolutionary properties and social network characteristics a perfect fitting data source to evolve an ontology. The decentralised structure of the Internet, the huge amount of data and upcoming Web2.0 technologies arise several challenges for a crawling system. In this paper we present a distributed crawling system with standard browser integration. The proposed system is a high performance, sitescript based noise reducing crawler, which loads standard browser equivalent content from Web2.0 resources. Furthermore we describe the integration of this spider into our ontology evolution framework.

Automated Ontology Learning and Validation Using Hypothesis Testing

Semantic Web technologies in general and ontology-based approaches in particular are considered the foundation for the next generation of information services. While ontologies enable software agents to exchange knowledge and information in a standardized, intelligent manner, describing today\(\check{\rm S}\)s vast amount of information in terms of ontological knowledge remains a challenge.

A Comparison of Stylometric and Lexical Features for Web Genre Classification and Emotion Classification in Blogs

In the blogosphere, the amount of digital content is expanding and for search engines, new challenges have been imposed. Due to the changing information need, automatic methods are needed to support blog search users to filter information by different facets. In our work, we aim to support blog search with genre and facet information. Since we focus on the news genre, our approach is to classify blogs into news versus rest. Also, we assess the emotionality facet in news related blogs to enable users to identify people’s feelings towards specific events. Our approach is to evaluate the performance of text classifiers with lexical and stylometric features to determine the best performing combination for our tasks. Our experiments on a subset of the TREC Blogs08 dataset reveal that classifiers trained on lexical features perform consistently better than classifiers trained on the best stylometric features.

Crosslanguage retrieval based on Wikipedia statistics

In this paper we present the methodology, implementations and evaluation results of the crosslanguage retrieval system we have developed for the Robust WSD Task at CLEF 2008. Our system is based on query preprocessing for translation and homogenisation of queries. The presented preprocessing of queries includes two stages: Firstly, a query translation step based on term statistics of cooccuring articles in Wikipedia. Secondly, different disjunct query composition techniques to search in the CLEF corpus. We apply the same preprocessing steps for the monolingual as well as the crosslingual task and thereby acting fair and in a similar way across these tasks. The evaluation revealed that the similar processing comes at nearly no costs for monolingual retrieval but enables us to do crosslanguage retrieval and also a feasible comparison of our system performance on these two tasks.

Audio-Visual Feature Extraction for Semi-Automatic Annotation of Meetings

In this paper we present the building blocks of our semi-automatic annotation tool which supports multi-modal and multi-level annotation of meetings. The main focus is on the proper design and functionality of the modules for recognizing meeting actions. The key features, identity and position of the speakers, are provided by different modalities (audio and video). Three audio algorithms (voice activity detection, speaker identification and direction of arrival) and three video algorithms (detection, tracking and identification) form the low-level feature extraction components. Low-level features are automatically merged and the recognized actions are proposed to the user by visualizing them. The annotation labels are related but not limited to events during meetings. The user can finally confirm or if necessary, modify the suggestion, and then store the actions into a database

Evaluation of axiomatic approaches to crosslanguage retrieval

Integrating word sense disambiguation into an information retrieval system could potentially improve its performance. This is the major motivation for the Robust WSD tasks of the Ad-Hoc Track of the CLEF 2009 campaign. For these tasks we have build a customizable and flexible retrieval system. The best performing configuration of this system is based on research in the area of axiomatic approaches to information retrieval. Further, our experiments show that configurations that incorporate word sense disambiguation (WSD) information into the retrieval process did outperform those without. For the monolingual task the performance difference is more pronounced than for the bilingual task. Finally, we are able to show that our query translation approach does work effectively, even if applied in the monolingual task.

Cross-domain classification: Trade-off between complexity and accuracy

Text classification is one of the core applications in data mining due to the huge amount of not categorized digital data available. Training a text classifier generates a model that reflects the characteristics of the domain. However, if no training data is available, labeled data from a related but different domain might be exploited to perform cross-domain classification. In our work, we aim to accurately classify unlabeled blogs into commonly agreed newspaper categories using labeled data from the news domain. The labeled news and the unlabeled blog corpus are highly dynamic and hourly growing with a topic drift, so a trade-off between accuracy and performance is required. Our approach is to apply a fast novel centroid-based algorithm, the Class-Feature-Centroid Classifier (CFC), to perform efficient cross-domain classification. Experiments showed that this algorithm achieves a comparable accuracy than k-NN and is slightly better than Support Vector Machines (SVM), yet at linear time cost for training and classification. The benefit of this approach is that the linear time complexity enables us to efficiently generate an accurate classifier, reflecting the topic drift, several times per day on a huge dataset.