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Learning to Extract Biological Event and Relation Graphs

Jari Björne, Filip Ginter, Juho Heimonen, Sampo Pyysalo, Tapio Salakoski, Learning to Extract Biological Event and Relation Graphs. In: Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009, NEALT Proceedings Series Vol. 4 (2009), 18-25, Northern European Association for Language Technology (NEALT), 2009.

Abstract:

While the overwhelming majority of information extraction efforts in the biomedical domain have focused on the extraction of simple binary interactions between named entity pairs, some recently published corpora provide complex, nested and typed event annotations that aim to accurately capture the diversity of biological relationships. We present the first machine
learning approach for extracting such relationships, utilizing both a graph kernel and a novel, task-specific feature set. We show that relationships can be predicted with 77% F-score, or 83% if their type and direction is disregarded. Using both gold standard and generated parses, we
determine the impact of parsing on extraction performance. Finally, we convert our predicted complex relationships to binary interactions, recovering binary annotation with 62% F-score, relating the new method to the large body of work available on binary interactions.

BibTeX entry:

@INPROCEEDINGS{inpBjGiHePySa09a,
  title = {Learning to Extract Biological Event and Relation Graphs},
  booktitle = {Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009},
  author = {Björne, Jari and Ginter, Filip and Heimonen, Juho and Pyysalo, Sampo and Salakoski, Tapio},
  volume = {Vol. 4 (2009)},
  series = {NEALT Proceedings Series},
  publisher = {Northern European Association for Language Technology (NEALT)},
  pages = {18-25},
  year = {2009},
}

Belongs to TUCS Research Unit(s): Turku BioNLP Group

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