Monitoring the reputation of entities such as companies or brands in microblog streams (e.g., Twitter) starts by selecting mentions that are related to the entity of interest. Entities are often ambiguous (e.g., "Jaguar" or "Ford") and effective methods for selectively removing non-relevant mentions often use background knowledge obtained from domain experts. Manual annotations by experts, however, are costly. We therefore approach the problem of entity filtering with active learning, thereby reducing the annotation load for experts. To this end, we use a strong passive baseline and analyze different sampling methods for selecting samples for annotation. We find that margin sampling--an informative type of sampling that considers the distance to the hyperplane used for class separation--can effectively be used for entity filtering and can significantly reduce the cost of annotating initial training data.

Hierarchical multi-label classification assigns a document to multiple hierarchical classes. In this paper we focus on hierarchical multi-label classification of social text streams. Concept drift, complicated relations among classes, and the limited length of documents in social text streams make this a challenging problem. Our approach includes three core ingredients: short document expansion, time-aware topic tracking, and chunk-based structural learning. We extend each short document in social text streams to a more comprehensive representation via state-of-the-art entity linking and sentence ranking strategies. From documents extended in this manner, we infer dynamic probabilistic distributions over topics by dividing topics into dynamic "global" topics and "local" topics. For the third and final phase we propose a chunk-based structural optimization strategy to classify each document into multiple classes. Extensive experiments conducted on a large real-world dataset show the effectiveness of our proposed method for hierarchical multi-label classification of social text streams.

Tree patterns with full text search form the core of both XQuery Full Text and the NEXI query language. On such queries, users expect a relevance-ranked list of XML elements as an answer. But this requirement may lead to undesirable behavior of XML retrieval systems: two queries which are intuitively (e.g., without ranking) equivalent return differently ordered lists of elements. We show that the best performing XML retrieval semantics has this behavior. We also show how minimization of tree patterns can efficiently solve this problem.

We demonstrate a web information system created for the European elections in June 2009. Based on their speeches in the EU parliament and their written questions, we created language models for each of the 736 members of the EU parliament. These language models were used to search for politicians responsible for a given topic, similar to expert search applications. Users prefer to see some kind of evidence for returning a hit after a search. We created a profile of each EU parlementarian by comparing her personal language model to the language model created from all EU parlementarians. The top 50 words best separating the individual from the avarage were shown as a wordcloud. These top 50 words and their scores were derived from a parsimonious language model.