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Information spreads rapidly across websites and other online media. The IDIOM research project analyses this process by identifying redundant content elements, mapping them to ontology concepts, and tracking their temporal and geographic distribution. Linguists define 'idiom' as an expression whose meaning is different from the literal meanings of its component words. Similarly, investigating information diffusion promises insights that cannot be inferred from individual network elements. Previous research often focused on particular media, or neglected important aspects of the human language. IDIOM addresses these gaps to reveal fundamental mechanisms of information diffusion across media with distinct interactive characteristics.

Rigorous evaluations and analyses of evaluation results are key towards improving Named Entity Linking systems. Nevertheless, most current evaluation tools are focused on benchmarking and comparative evaluations. Therefore, they only provide aggregated statistics such as precision, recall and F1-measure to assess system performance and no means for conducting detailed analyses up to the level of individual annotations. This paper addresses the need for transparent benchmarking and fine-grained error analysis by introducing Orbis, an extensible framework that supports drill-down analysis, multiple annotation tasks and resource versioning. Orbis complements approaches like those deployed through the GERBIL and TAC KBP tools and helps developers to better understand and address shortcomings in their Named Entity Linking tools. We present three uses cases in order to demonstrate the usefulness of Orbis for both research and production systems: (i) improving Named Entity Linking tools; (ii) detecting gold standard errors; and (iii) performing Named Entity Linking evaluations with multiple versions of the included resources.

Opinion holder and opinion target extraction are among the most popular and challenging problems tackled by opinion mining researchers, recognizing the significant business value of such components and their importance for applications such as media monitoring and Web intelligence. This paper describes an approach that combines opinion target extraction with aspect extraction using syntactic patterns. It expands previous work limited by sentence boundaries and includes a heuristic for anaphora resolution to identify targets across sentences. Furthermore, it demonstrates the application of concepts known from research on open information extraction to the identification of relevant opinion aspects. Qualitative analyses performed on a corpus of 100,000 Amazon product reviews show that the approach is promising. The extracted opinion targets and aspects are useful for enriching common knowledge resources and opinion mining ontologies, and support practitioners and researchers to identify opinions in document collections.

Annotation styles express guidelines that direct human annotators in what rules to follow when creating gold standard annotations of text corpora. These guidelines not only shape the gold standards they help create, but also influence the training and evaluation of Named Entity Linking (NEL) tools, since different annotation styles correspond to divergent views on the entities present in the same texts. Such divergence is particularly present in texts from the media domain that contain references to creative works. In this work we present a corpus of 1000 annotated documents selected from the media domain. Each document is presented with multiple gold standard annotations representing various annotation styles. This corpus is used to evaluate a series of Named Entity Linking tools in order to understand the impact of the differences in annotation styles on the reported accuracy when processing highly ambiguous entities such as names of creative works. Relaxed annotation guidelines that include overlap styles lead to better results across all tools.

Dependency parsing is considered a key technology for improving information extraction tasks. Research indicates that dependency parsers spend more than 95% of their total runtime on feature computations. Based on this insight, this paper investigates the potential of improving parsing throughput by designing feature representations which are optimized for combining single features to more complex feature templates and by optimizing parser constraints. Applying these techniques to MDParser increased its throughput four fold, yielding Syntactic Parser, a dependency parser that outperforms comparable approaches by factor 25 to 400.

Ontology evolution is an intrinsic phenomenon of any knowledge-intensive system, which can be addressed either implicitly or explicitly. This paper describes an approach to capture and visualize implicit data-driven ontology evolution using ontologies semi-automatically generated by extending small seed ontologies. This process captures ontology changes reflected in large document collections. Visualizing of these changes helps characterize the evolution process, and distinguish core, extended and peripheral relations between concepts. Finally, the paper presents an example of ontology evolution by monitoring and analyzing online media coverage on "energy sources" over a period of ten months.