DFG GRK 1994 Research Training Group AIPHES ("Adaptive Information Preparation from Heterogeneous Sources")

Motivation

The importance of thorough research under tight deadlines is increasing rapidly and the consequences for the quality of the research results are far-reaching, especially in decision-making processes. At the same time, the amount of information is growing exponentially, and there is a continuous increase of complexity, heterogeneity, and a high variation in the quality of electronic information sources.

Goals

The vision of the Research Training Group GRK 1994 AIPHES is to extract structured knowledge from heterogeneous sources using automated means in order to create dossiers of stylistically homogeneous content. 

 

                                    

Methods

GRK 1994 AIPHES develops methods able to adapt to different genres and domains, so that the results can easily be transferred to other tasks and – in later phases of the project – to other user groups and languages. The first project phase focuses on multi-document summarization (MDS) as a prototypical task. As a representative use-case, we choose German documents on educational topics extracted from heavily heterogeneous sources.

Adaptive information processing is a complex task which requires the involvement of researchers from four project areas: (A) the computational linguistic modeling of discourse phenomena in heterogeneous text genres, (B) the development of language technologies for heterogeneous MDS, (C) the representation and analysis of text-induced structures, and (D) the criteria and mechanisms for selecting and assessing the quality of heterogeneous sources and resulting summaries in information management. By investigating these questions in a densely connected research plan, the four research areas will jointly address adaptive information processing both in breadth and depth.The figure below represents the interaction of the four areas and the guiding themes of each area.

Qualification Concept

The qualification concept includes collaborations across disciplines and locations, intensive international networking, scientific consultation of at least two PhD advisors and of one international co-advisor for each doctoral project, and the responsible participation of excellent post-doctoral researchers in doctoral supervision and training jointly with experienced advisors. The graduate program will form a central location for the qualification of young researchers in the highly demanded academic field of adaptive information processing.

Team at UKP

Principal Investigators

  • Prof. Dr. Iryna Gurevych (Speaker)
  • Dr. Christian M. Meyer

Coordination

  • Dr. Sebastian Harrach

Associated Senior Researchers

  • Dr. Richard Eckart de Castilho
  • Prof. Dr. Margot Mieskes (Informationswissenschaft, Hochschule Darmstadt)
  • Dr. Ivan Habernal

Doctoral Researchers

  • Teresa Botschen
  • Andreas Hanselowski
  • Maxime Peyrard
  • Avinesh P.V.S.
  • Tobias Falke

Associated Doctoral Researchers

  • Nils Reimers
  • Daniil Sorokin
  • Christopher Tauchmann

Publications

Additional Attributes

Type

A Mention-Ranking Model for Abstract Anaphora Resolution

Ana Marasovic, Leo Born, Juri Opitz, Anette Frank
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), p. (to appear), September 2017
[Inproceedings]

Learning to Score System Summaries for Better Content Selection Evaluation

Maxime Peyrard, Teresa Botschen, Iryna Gurevych
In: Proceedings of the EMNLP workshow "New Frontiers in Summarization", p. (to appear), September 2017
[Inproceedings]

Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps

Tobias Falke, Iryna Gurevych
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), p. to appear, September 2017
[Online-Edition: https://www.ukp.tu-darmstadt.de/data/summarization/concept-map-summaries]
[Inproceedings]

GraphDocExplore: A Framework for the Experimental Comparison of Graph-based Document Exploration Techniques

Tobias Falke, Iryna Gurevych
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), p. to appear, September 2017
[Online-Edition: https://github.com/UKPLab/emnlp2017-graphdocexplore]
[Inproceedings]

Utilizing Automatic Predicate-Argument Analysis for Concept Map Mining

Tobias Falke, Iryna Gurevych
In: Proceedings of the 12th International Conference on Computational Semantics (IWCS 2017), p. (to appear), September 2017
[Inproceedings]

Context-Aware Representations for Knowledge Base Relation Extraction

Daniil Sorokin, Iryna Gurevych
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), p. (to appear), September 2017
[Inproceedings]

Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization

Maxime Peyrard, Judith Eckle-Kohler
In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vol. Volume 1: Long Papers, p. (to appear), August 2017
Association for Computational Linguistics
[Inproceedings]

A Principled Framework for Evaluating Summarizers: Comparing Models of Summary Quality against Human Judgments

Maxime Peyrard, Judith Eckle-Kohler
In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vol. Volume 2: Short Papers, p. (to appear), August 2017
Association for Computational Linguistics
[Inproceedings]

Prediction of Frame-to-Frame Relations in the FrameNet Hierarchy with Frame Embeddings

Teresa Botschen, Hatem Mousselly-Sergieh, Iryna Gurevych
In: Proceedings of th 2nd Workshop on Representation Learning for NLP (RepL4NLP, held in conjunction with ACL 2017), p. To appear, August 2017
[Inproceedings]

Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback

Avinesh P.V.S., Christian M. Meyer
In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vol. Volume 1: Long Paper, p. 1353-1363, July 2017
Association for Computational Linguistics
[Online-Edition: http://www.aclweb.org/anthology/P/P17/P17-1124]
[Inproceedings]

Funding

This project is funded by Deutsche Forschungsgemeinschaft (German Research Foundation).

Further Information

Detailed information on GRK 1994 AIPHES can be found it its web site: https://www.aiphes.tu-darmstadt.de

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