We analyze whether implicitness affects human perception of hate speech. To do so, we use Tweets from an existing hate speech corpus and paraphrase them with rules to make the hate speech they contain more explicit. Comparing the judgment on the original and the paraphrased Tweets, our study indicates that implicitness is a factor in human and automatic hate speech detection. Hence, our study suggests that current automatic hate speech detection needs features that are more sensitive to implicitness.

Paraphrases exist on different granularity levels, the most frequently used one being the sentential level.However, we argue that working on the sentential level is not optimal for both machines and humans, and that it would be easier and more efficient to work on sub-sentential levels.To prove this, we quantify and analyze the difference between paraphrases on both sentence and sub-sentence level in order to show the significance of the problem.First results on a preliminary dataset seem to confirm our hypotheses.

We present our system used for the AIPHES team submission in the context of the EmpiriST shared task on "Automatic Linguistic Annotation of Computer-Mediated Communication / Social Media".Our system is based on a rulebased tokenizer and a machine learning sequence labelling POS tagger using a variety of features.We show that the system is robust across the two tested genres: German computer mediated communication (CMC) and general German web data (WEB).We achieve the second rank in three of four scenarios.Also, the presented systems are freely available as open source components.* Eingetr.Lebenspartnersch.

In this paper, we present MDSWriter, a novel open-source annotation tool for creating multi-document summarization corpora. A major innovation of our tool is that we divide the complex summarization task into multiple steps which enables us to efficiently guide the annotators, to store all their intermediate results, and to record user-system interaction data. This allows for evaluating the individual components of a complex summarization system and learning from the human writing process. MDSWriter is highly flexible and can be adapted to various other tasks.

Semantic relations play an important role in linguistic knowledge representation. Although their role is relevant in the context of written text, there is no approach or dataset that makes use of contextuality of classic semantic relations beyond the boundary of one sentence. We present the SemRelData dataset that contains annotations of semantic relations between nominals in the context of one paragraph. To be able to analyse the universality of this context notion, the annotation was performed on a multi-lingual and multi-genre corpus. To evaluate the dataset, it is compared to large, manually created knowledge resources in the respective languages. The comparison shows that knowledge bases not only have coverage gaps; they also do not account for semantic relations that are manifested in particular contexts only, yet still play an important role for text cohesion.

In our work, we present a corpus of heterogeneous documents for summarization to address the issue that information seekers usually face a range of different types of information sources. A second issue we address, is the summary type, as most manual summaries are abstractive, whereas automatic methods mainly create extractive summaries, which are hard to compare to each other using standard evaluation methods. Therefore, we suggest a multi-step process for creating \emph{coherent extracts}, which are based on information taken directly from the source documents, but minimally redacted and meaningfully ordered to form a coherent text. Our qualitative and quantitative evaluation show that quantitative results are not sufficient to judge the quality of a summary and that other quality criteria, such as coherence, should also be taken into account. We find that our corpus is of high quality and that it has the potential to bridge the gap between reference corpora of abstracts and automatic methods producing extracts. Our corpus is available to the research community for further development.

An important goal in text understanding is making sense of events. However, there is a gap between computable representations on the one hand and expressive representations on the other hand. We aim to bridge this gap by inducing distributional semantic clusters as labels in a frame structural representation.

With this paper, we release a freely available statistical German Named Entity Tag-ger based on conditional random fields (CRF). The tagger is trained and evaluated on the GermEval 2014 dataset for named entity recognition and comes close to the performance of the best (proprietary) system in the competition with 76% F-measure test set performance on the four standard NER classes. We describe a range of features and their influence on German NER classification and provide a comparative evaluation and some analysis of the results. The software components, the training data and all data used for feature generation are distributed under permissive licenses, thus this tagger can be used in academic and commercial settings without restrictions or fees. The tagger is available as a command-line tool and as an Apache UIMA component.

This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-theart machine learning methods, combined with some knowledge-based features in hybrid systems.

We describe the annotation of a new dataset for German Named Entity Recognition (NER). The need for this dataset is motivated by licensing issues and consistency issues of existing datasets. We describe our approach to creating annotation guidelines based on linguistic and semantic considerations, and how we iteratively refined and tested them in the early stages of annotation in order to arrive at the largest publicly available dataset for German NER, consisting of over 31,000 manually annotated sentences (over 591,000 tokens) from German Wikipedia and German online news. We provide a number of statistics on the dataset, which indicate its high quality, and discuss legal aspects of distributing the data as a compilation of citations. The data is released under the permissive CC-BY license, and will be fully available for download in September 2014 after it has been used for the GermEval 2014 shared task on NER. We further provide the full annotation guidelines and links to the annotation tool used for the creation of this resource.