All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning
BMC Bioinformatics2008Vol. 9(S11), pp. S2–S2
Citations Over TimeTop 10% of 2008 papers
Abstract
We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided.
Related Papers
- → Review of entity relation extraction(2023)16 cited
- → Information Extraction in the Medical Domain(2015)10 cited
- A Dataset for Inter-Sentence Relation Extraction using Distant Supervision(2018)
- Simple ontologies for practical information extraction and advanced information extraction for practical ontologies(2013)
- → RDRS: Represent Document-level Relation with Sentence-level Relation by Distant Supervision(2023)