Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature

By Claudio Giuliano, Alberto Lavelli, Lorenza Romano. Conference of the European Chapter of the Association for Computational Linguistics, 2006

345 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fd71613b23a8209d278bb6672e76848da9ea3873

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Citations in the corpus

An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature (2014, methodology): “N Giuliano et al. , 2006 [28]: This is a kernel-based machine learning method, which combined global and local context features.”
StatSnowball: a statistical approach to extracting entity relationships (2009, methodology): “Like many other relation extraction systems [1, 8, 9], we assume that entities are given.”
All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning (2008, unknown): “[28], Van Landeghem et al.”
Simple tricks for improving pattern-based information extraction from the biomedical literature (2010, background): “in our recent evaluation on the performance of PPI extraction systems on five PPI corpora using three different evaluation strategies [8], the shallow linguistic kernel based system [32], which uses only POS tags and tokens, performed almost equally well as the best systems using dependency parse tr”
Detection of drug‐drug interactions through data mining studies using clinical sources, scientific literature and social media (2018, unknown): “linguistic kernel approach [90], can also offer great assistance in DDI extraction from biomedical literature [91].”
Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, methodology): “…the usage of kernels in PPI extraction [102,103], and categorized kernels into the following categories: 1) shallow linguistic (SL) kernels [104]; 2) constituent parse tree based kernels, including subtree (ST) [105], subset tree (SST) [106] and partial tree (PT) [107] kernels that use…”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, methodology): “They categorized kernels into the following categories: (1) shallow linguistic (SL) kernels [140]; (2) constituent parse tree-based kernels, including subtree (ST) [141], subset tree (SST) [142] and partial tree (PT) [143] kernels that use increasingly generalized forms of subtrees, as well as a spe”
SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013) (2013, methodology): “Its system combines several kernel methods (APG (Airola et al., 2008) and Shallow Linguistic Kernel (SL) (Giuliano et al., 2006) among others), the Turku Event Extraction system (TEES) (Bj¨orne et al., 2011) 8 and the Moara system (Neves et al., 2009).”