Publications (Bibsonomy)

• [ 2014 ]
• [ 2013 ]
• [ 2012 ]
• [ 2011 ]
• [ 2010 ]
• [ 2009 ]
• [ 2008 ]
• [ 2007 ]
• [ 2006 ]

• 
  UIMA Ruta Workbench: Rule-based Text Annotation. Kluegl, Peter; Toepfer, Martin; Beck, Philip-Daniel; Fette, Georg; Puppe, Frank. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations, pp. 29–33. Dublin City University and Association for Computational Linguistics, Dublin, Ireland, 2014.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{kluegl-EtAl:2014:ColingDemo, address = {Dublin, Ireland}, author = {Kluegl, Peter and Toepfer, Martin and Beck, Philip-Daniel and Fette, Georg and Puppe, Frank}, booktitle = {Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations}, keywords = {workbench}, month = {08}, pages = {29–33}, publisher = {Dublin City University and Association for Computational Linguistics}, title = {UIMA Ruta Workbench: Rule-based Text Annotation}, year = 2014 }
• 
  UIMA Ruta: Rapid development of rule-based information extraction applications. Kluegl, Peter; Toepfer, Martin; Beck, Philip-Daniel; Fette, Georg; Puppe, Frank. In Natural Language Engineering, FirstView, pp. 1–40. 2014.
  • [ BibTeX ]
  • [ URL ]
  @article{NLE:9375265, author = {Kluegl, Peter and Toepfer, Martin and Beck, Philip-Daniel and Fette, Georg and Puppe, Frank}, journal = {Natural Language Engineering}, keywords = {rule}, month = 10, pages = {1–40}, title = {UIMA Ruta: Rapid development of rule-based information extraction applications}, volume = {FirstView}, year = 2014 }

• 
  Constraint-driven Evaluation in UIMA Ruta. Wittek, Andreas; Toepfer, Martin; Fette, Georg; Klügl, Peter; Puppe, Frank. In UIMA@GSCL, Vol. 1038 of CEUR Workshop Proceedings, P. Klügl, R. Eckart de Castilho, K. Tomanek (eds.), pp. 58–65. CEUR-WS.org, 2013.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{conf/gldv/WittekTFKP13, author = {Wittek, Andreas and Toepfer, Martin and Fette, Georg and Klügl, Peter and Puppe, Frank}, booktitle = {UIMA@GSCL}, crossref = {conf/gldv/2013uima}, editor = {Klügl, Peter and Eckart de Castilho, Richard and Tomanek, Katrin}, keywords = {extraction}, pages = {58-65}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Constraint-driven Evaluation in UIMA Ruta.}, volume = 1038, year = 2013 }
• 
  Exploiting Structural Consistencies with Stacked Conditional Random Fields. Kluegl, Peter; Toepfer, Martin; Lemmerich, Florian; Hotho, Andreas; Puppe, Frank. In Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics, 30, pp. 111–125. 2013.
  • [ Abstract ]
  • [ BibTeX ]
  Conditional Random Fields (CRF) are popular methods for labeling unstructured or textual data. Like many machine learning approaches, these undirected graphical models assume the instances to be independently distributed. However, in real-world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. We apply rule learning collectively on the predictions of an initial CRF for one context to acquire descriptions of its specific properties. Then, we utilize these descriptions as dynamic and high quality features in an additional (stacked) CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.

  @article{kluegl2013exploiting, abstract = {Conditional Random Fields (CRF) are popular methods for labeling unstructured or textual data. Like many machine learning approaches, these undirected graphical models assume the instances to be independently distributed. However, in real-world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. We apply rule learning collectively on the predictions of an initial CRF for one context to acquire descriptions of its specific properties. Then, we utilize these descriptions as dynamic and high quality features in an additional (stacked) CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.}, author = {Kluegl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank}, journal = {Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics}, keywords = {exploiting}, pages = {111-125}, title = {Exploiting Structural Consistencies with Stacked Conditional Random Fields}, volume = 30, year = 2013 }

• 
  Stacked Conditional Random Fields Exploiting Structural Consistencies. Kluegl, Peter; Toepfer, Martin; Lemmerich, Florian; Hotho, Andreas; Puppe, Frank. In Proceedings of 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM), P. L. Carmona, J. S. Sánchez, A. Fred (eds.), pp. 240–248. SciTePress, Vilamoura, Algarve, Portugal, 2012.
  • [ Abstract ]
  • [ BibTeX ]
  • [ URL ]
  Conditional Random Fields (CRF) are popular methods for labeling unstructured or textual data. Like many machine learning approaches these undirected graphical models assume the instances to be independently distributed. However, in real world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. The approach incorporates three successive steps of inference: First, an initial CRF processes single instances as usual. Next, we apply rule learning collectively on all labeled outputs of one context to acquire descriptions of its specific properties. Finally, we utilize these descriptions as dynamic and high quality features in an additional (stacked) CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.

  @inproceedings{kluegl2012stacked, abstract = {Conditional Random Fields (CRF) are popular methods for labeling unstructured or textual data. Like many machine learning approaches these undirected graphical models assume the instances to be independently distributed. However, in real world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. The approach incorporates three successive steps of inference: First, an initial CRF processes single instances as usual. Next, we apply rule learning collectively on all labeled outputs of one context to acquire descriptions of its specific properties. Finally, we utilize these descriptions as dynamic and high quality features in an additional (stacked) CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.}, address = {Vilamoura, Algarve, Portugal}, author = {Kluegl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank}, booktitle = {Proceedings of 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM)}, editor = {Carmona, Pedro Latorre and Sánchez, J. Salvador and Fred, Ana}, keywords = {rule}, month = {02}, pages = {240-248}, publisher = {SciTePress}, title = {Stacked Conditional Random Fields Exploiting Structural Consistencies}, year = 2012 }
• 
  Collective Information Extraction with Context-Specific Consistencies. Klügl, Peter; Toepfer, Martin; Lemmerich, Florian; Hotho, Andreas; Puppe, Frank. In ECML/PKDD (1), Vol. 7523 of Lecture Notes in Computer Science, P. A. Flach, T. D. Bie, N. Cristianini (eds.), pp. 728–743. Springer, 2012.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{conf/pkdd/KluglTLHP12, author = {Klügl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank}, booktitle = {ECML/PKDD (1)}, crossref = {conf/pkdd/2012-1}, editor = {Flach, Peter A. and Bie, Tijl De and Cristianini, Nello}, keywords = {specific}, pages = {728-743}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Collective Information Extraction with Context-Specific Consistencies.}, volume = 7523, year = 2012 }

• 
  Information Extraction from Echocardiography Records. Fette, Georg; Kluegl, Peter; Ertl, Maximilian; Störk, Stefan; Puppe, Frank. In Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation. 2011.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{fette2011information, author = {Fette, Georg and Kluegl, Peter and Ertl, Maximilian and Störk, Stefan and Puppe, Frank}, booktitle = {Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation}, keywords = {extraction}, title = {Information Extraction from Echocardiography Records}, year = 2011 }
• 
  Towards Learning Error-Driven Transformations for Information Extraction. Eckstein, Benjamin; Kluegl, Peter; Puppe, Frank. In Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation. 2011.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{eckstein2011towards, author = {Eckstein, Benjamin and Kluegl, Peter and Puppe, Frank}, booktitle = {Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation}, keywords = {extraction}, title = {Towards Learning Error-Driven Transformations for Information Extraction}, year = 2011 }

• 
  Conditional Random Fields for Local Adaptive Reference Extraction. Toepfer, Martin; Kluegl, Peter; Hotho, Andreas; Puppe, Frank. In Workshop on Knowledge Discovery, Data Mining, Machine Learning (KDML 2010). 2010.
  • [ Abstract ]
  • [ BibTeX ]
  • [ URL ]
  The accurate extraction of bibliographic information from scientific publications is an active field of research. Machine learning, especially sequence labeling approaches like Conditional Random Fields (CRF), are often applied for this reference extraction task, but still suffer from the ambiguity of reference notation. Reference sections apply a predefined style guide and contain only homogeneous references. Therefore, other references of the same paper or journal often can provide evidence how the fields of a reference are correctly labeled. We propose a novel approach that exploits the similarities within a document. Our process model uses information of unlabeled documents directly during the extraction task in order to automatically adapt to the perceived style guide. This is implemented by changing the manifestation of the features for the applied CRF. The experimental results show considerable improvements compared to the common approach. We achieve an average F1 score of 96.7% and an instance accuracy of 85.4% on the test data set.

  @inproceedings{2010-LWA-TKHP, abstract = {The accurate extraction of bibliographic information from scientific publications is an active field of research. Machine learning, especially sequence labeling approaches like Conditional Random Fields (CRF), are often applied for this reference extraction task, but still suffer from the ambiguity of reference notation. Reference sections apply a predefined style guide and contain only homogeneous references. Therefore, other references of the same paper or journal often can provide evidence how the fields of a reference are correctly labeled. We propose a novel approach that exploits the similarities within a document. Our process model uses information of unlabeled documents directly during the extraction task in order to automatically adapt to the perceived style guide. This is implemented by changing the manifestation of the features for the applied CRF. The experimental results show considerable improvements compared to the common approach. We achieve an average F1 score of 96.7% and an instance accuracy of 85.4% on the test data set.}, author = {Toepfer, Martin and Kluegl, Peter and Hotho, Andreas and Puppe, Frank}, booktitle = {Workshop on Knowledge Discovery, Data Mining, Machine Learning (KDML 2010)}, keywords = {fields}, title = {Conditional Random Fields for Local Adaptive Reference Extraction}, year = 2010 }
• 
  Local Adaptive Extraction of References. Kluegl, Peter; Hotho, Andreas; Puppe, Frank. In KI 2010: Advances in Artificial Intelligence, 33rd Annual German Conference on AI, of LNAI 6359, R. Dillmann, J. Beyerer, U. D. Hanebeck, T. Schultz (eds.), pp. 40–47. Springer, 2010.
  • [ Abstract ]
  • [ BibTeX ]
  • [ URL ]
  The accurate extraction of scholarly reference information from scientific publications is essential for many useful applications like BibTeX management systems or citation analysis. Automatic extraction methods suffer from the heterogeneity of reference notation, no matter wether the extraction model was handcrafted or learnt from labeled data. However, references of the same paper or journal are usually homogeneous. We exploit this local consistency with a novel approach. Given some initial information from such a reference section, we try to derived generalized patterns. These patterns are used to create a local model of the current document. The local model helps to identify errors and to improve the extracted information incrementally during the extraction process. Our approach is implemented with handcrafted transformation rules working on a meta-level being able to correct the information independent of the applied layout style. The experimental results compete very well with the state of the art methods and show an extremely high performance on consistent reference sections.

  @inproceedings{2010-KI-KHP, abstract = {The accurate extraction of scholarly reference information from scientific publications is essential for many useful applications like BibTeX management systems or citation analysis. Automatic extraction methods suffer from the heterogeneity of reference notation, no matter wether the extraction model was handcrafted or learnt from labeled data. However, references of the same paper or journal are usually homogeneous. We exploit this local consistency with a novel approach. Given some initial information from such a reference section, we try to derived generalized patterns. These patterns are used to create a local model of the current document. The local model helps to identify errors and to improve the extracted information incrementally during the extraction process. Our approach is implemented with handcrafted transformation rules working on a meta-level being able to correct the information independent of the applied layout style. The experimental results compete very well with the state of the art methods and show an extremely high performance on consistent reference sections.}, author = {Kluegl, Peter and Hotho, Andreas and Puppe, Frank}, booktitle = {KI 2010: Advances in Artificial Intelligence, 33rd Annual German Conference on AI}, editor = {Dillmann, Rüdiger and Beyerer, Jürgen and Hanebeck, Uwe D. and Schultz, Tanja}, keywords = {local}, pages = {40-47}, publisher = {Springer}, series = {LNAI 6359}, title = {Local Adaptive Extraction of References}, year = 2010 }

• 
  Meta-level Information Extraction. Klügl, Peter; Atzmueller, Martin; Puppe, Frank. In KI, Vol. 5803 of Lecture Notes in Computer Science, B. Mertsching, M. Hund, M. Z. Aziz (eds.), pp. 233–240. Springer, 2009.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{conf/ki/KluglAP09, author = {Klügl, Peter and Atzmueller, Martin and Puppe, Frank}, booktitle = {KI}, crossref = {conf/ki/2009}, editor = {Mertsching, Bärbel and Hund, Marcus and Aziz, Muhammad Zaheer}, keywords = {extraction}, pages = {233-240}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Meta-level Information Extraction.}, volume = 5803, year = 2009 }
• 
  TextMarker: A Tool for Rule-Based Information Extraction. Kluegl, Peter; Atzmueller, Martin; Puppe, Frank. In Proceedings of the Biennial GSCL Conference 2009, 2nd UIMA@GSCL Workshop, C. Chiarcos, R. E. de Castilho, M. Stede (eds.), pp. 233–240. Gunter Narr Verlag, 2009.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{2009:GSCL:KAP:TextMarker, author = {Kluegl, Peter and Atzmueller, Martin and Puppe, Frank}, booktitle = {Proceedings of the Biennial GSCL Conference 2009, 2nd UIMA@GSCL Workshop}, editor = {Chiarcos, Christian and de Castilho, Richard Eckart and Stede, Manfred}, keywords = {rule}, pages = {233-240}, publisher = {Gunter Narr Verlag}, title = {TextMarker: A Tool for Rule-Based Information Extraction}, year = 2009 }
• 
  A Framework for Semi-Automatic Development of Rule-based Information Extraction Applications. Kluegl, Peter; Atzmueller, Martin; Hermann, Tobias; Puppe, Frank. In Proc. LWA 2009 (KDML - Special Track on Knowledge Discovery and Machine Learning), M. H. und Frederik Janssen (ed.), pp. 56–59. 2009.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{2009:LWA:KAHP:TextRuler, author = {Kluegl, Peter and Atzmueller, Martin and Hermann, Tobias and Puppe, Frank}, booktitle = {Proc. LWA 2009 (KDML - Special Track on Knowledge Discovery and Machine Learning)}, editor = {und Frederik Janssen, Melanie Hartmann}, keywords = {rule}, pages = {56-59}, title = {A Framework for Semi-Automatic Development of Rule-based Information Extraction Applications}, year = 2009 }

• 
  Integrating the Rule-Based IE Component TextMarker into UIMA. Kluegl, Peter; Atzmueller, Martin; Puppe, Frank. In LWA-2008 (Special Track on Information Retrieval), J. Baumeister, M. Atzmueller (eds.), pp. 73–77. 2008.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{KAP08, author = {Kluegl, Peter and Atzmueller, Martin and Puppe, Frank}, booktitle = {LWA-2008 (Special Track on Information Retrieval)}, editor = {Baumeister, Joachim and Atzmueller, Martin}, keywords = {rule-based}, pages = {73-77}, title = {Integrating the Rule-Based IE Component TextMarker into UIMA}, year = 2008 }
• 
  Konzeption und Evaluation eines fallbasierten Trainingssystems im universitätsweiten Einsatz (CaseTrain). Hoernlein, Alexander; Ifland, Marianus; Kluegl, Peter; Puppe, Frank. In GMS Medizinische Informatik, Biometrie und Epidemiologie, GMS Med Inform Biom Epidemiol, 5(1). 2008.
  • [ BibTeX ]
  • [ URL ]
  @article{HIKP08, author = {Hoernlein, Alexander and Ifland, Marianus and Kluegl, Peter and Puppe, Frank}, journal = {GMS Medizinische Informatik, Biometrie und Epidemiologie, GMS Med Inform Biom Epidemiol}, keywords = {TextMarker}, title = {Konzeption und Evaluation eines fallbasierten Trainingssystems im universitätsweiten Einsatz (CaseTrain)}, volume = {5(1)}, year = 2008 }
• 
  Test-Driven Development of Complex Information Extraction Systems using TextMarker. Kluegl, Peter; Atzmueller, Martin; Puppe, Frank. In 4th International Workshop on Knowledge Engineering and Software Engineering (KESE 2008),31th German Conference on Artificial Intelligence (KI-2008), G. J. Naplepa, J. Baumeister (eds.), pp. 19–30. 2008.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{KESE_KAP08, author = {Kluegl, Peter and Atzmueller, Martin and Puppe, Frank}, booktitle = {4th International Workshop on Knowledge Engineering and Software Engineering (KESE 2008),31th German Conference on Artificial Intelligence (KI-2008)}, editor = {Naplepa, Grzegorz J. and Baumeister, Joachim}, keywords = {extraction}, pages = {19-30}, title = {Test-Driven Development of Complex Information Extraction Systems using TextMarker}, year = 2008 }
• 
  Rule-Based Information Extraction for Structured Data Acquisition using TextMarker. Atzmueller, Martin; Kluegl, Peter; Puppe, Frank. In LWA-2008 (Special Track on Knowledge Discovery and Machine Learning), J. Baumeister, M. Atzmueller (eds.), pp. 1–7. 2008.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{AKP08, author = {Atzmueller, Martin and Kluegl, Peter and Puppe, Frank}, booktitle = {LWA-2008 (Special Track on Knowledge Discovery and Machine Learning)}, editor = {Baumeister, Joachim and Atzmueller, Martin}, keywords = {TextMarker}, pages = {1-7}, title = {Rule-Based Information Extraction for Structured Data Acquisition using TextMarker}, year = 2008 }

• 
  Rapid Knowledge Capture Using Subgroup Discovery with Incremental Refinement. Atzmueller, Martin; Baumeister, Joachim; Kluegl, Peter; Puppe, Frank. In K-CAP ’07: Proceedings of the 4th International Conference on Knowledge Capture, pp. 31–38. Whistler, BC, Canada, 2007.
  • [ BibTeX ]
  @inproceedings{ABKP:KCAP:07, author = {Atzmueller, Martin and Baumeister, Joachim and Kluegl, Peter and Puppe, Frank}, booktitle = {K-CAP '07: Proceedings of the 4th International Conference on Knowledge Capture}, keywords = {refinement}, pages = {31-38}, title = {Rapid Knowledge Capture Using Subgroup Discovery with Incremental Refinement}, year = 2007 }
• 
  Erwartungsgesteuerte Informationsextraktion in Satzteilen. Kluegl, Peter; Schuhmann, Martin; Puppe, Frank; Buscher, H.-P. In Lernen - Wissen - Adaption (LWA). 2007.
  • [ BibTeX ]
  @inproceedings{KSPB07, author = {Kluegl, Peter and Schuhmann, Martin and Puppe, Frank and Buscher, H.-P.}, booktitle = {Lernen - Wissen - Adaption (LWA)}, keywords = {extraction}, title = {Erwartungsgesteuerte Informationsextraktion in Satzteilen}, year = 2007 }

• 
  Conservative and Creative Strategies for the Refinement of Scoring Rules. Baumeister, Joachim; Atzmueller, Martin; Kluegl, Peter; Puppe, Frank. In FLAIRS’06: Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference, pp. 408–413. AAAI Press, 2006.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{BAKP:FLAIRS:06, author = {Baumeister, Joachim and Atzmueller, Martin and Kluegl, Peter and Puppe, Frank}, booktitle = {FLAIRS'06: Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference}, keywords = {refinement}, pages = {408-413}, publisher = {AAAI Press}, title = {Conservative and Creative Strategies for the Refinement of Scoring Rules}, year = 2006 }