Darina Benikova


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Same same, but different: Compositionality of paraphrase granularity levels
Darina Benikova | Torsten Zesch
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

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.


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SemRelData ― Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines
Darina Benikova | Chris Biemann
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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.

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Bridging the gap between extractive and abstractive summaries: Creation and evaluation of coherent extracts from heterogeneous sources
Darina Benikova | Margot Mieskes | Christian M. Meyer | Iryna Gurevych
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Coherent extracts are a novel type of summary combining the advantages of manually created abstractive summaries, which are fluent but difficult to evaluate, and low-quality automatically created extractive summaries, which lack coherence and structure. We use a corpus of heterogeneous documents to address the issue that information seekers usually face – a variety of different types of information sources. We directly extract information from these, but minimally redact and meaningfully order it to form a coherent text. Our qualitative and quantitative evaluations 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 manually created 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.

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MDSWriter: Annotation Tool for Creating High-Quality Multi-Document Summarization Corpora
Christian M. Meyer | Darina Benikova | Margot Mieskes | Iryna Gurevych
Proceedings of ACL-2016 System Demonstrations

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EmpiriST: AIPHES - Robust Tokenization and POS-Tagging for Different Genres
Steffen Remus | Gerold Hintz | Chris Biemann | Christian M. Meyer | Darina Benikova | Judith Eckle-Kohler | Margot Mieskes | Thomas Arnold
Proceedings of the 10th Web as Corpus Workshop

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Bridging the gap between computable and expressive event representations in Social Media
Darina Benikova | Torsten Zesch
Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods


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NoSta-D Named Entity Annotation for German: Guidelines and Dataset
Darina Benikova | Chris Biemann | Marc Reznicek
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

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.