TY - JOUR
T1 - Molecular event extraction from link grammar parse trees in the BIONLP'09 shared task
AU - Hakenberg, Jörg
AU - Solt, Illés
AU - Tikk, Domonkos
AU - Nguyên, Vãu Há
AU - Tari, Luis
AU - Nguyen, Quang Long
AU - Baral, Chitta
AU - Leser, Ulf
PY - 2011/11
Y1 - 2011/11
N2 - The BioNLP'09 Shared Task deals with extracting information on molecular events, such as gene expression and protein localization, from natural language text. Information in this benchmark are given as tuples including protein names, trigger terms for each event, and possible other participants such as bindings sites. We address all three tasks of BioNLP'09: event detection, event enrichment, and recognition of negation and speculation. Our method for the first two tasks is based on a deep parser; we store the parse tree of each sentence in a relational database scheme. From the training data, we collect the dependencies connecting any two relevant terms of a known tuple, that is, the shortest paths linking these two constituents. We encode all such linkages in a query language to retrieve similar linkages from unseen text. For the third task, we rely on a hierarchy of hand-crafted regular expressions to recognize speculation and negated events. In this paper, we added extensions regarding a post-processing step that handles ambiguous event trigger terms, as well as an extension of the query language to relax linkage constraints. On the BioNLP Shared Task test data, we achieve an overall F1-measure of 32%, 29%, and 30% for the successive Tasks 1, 2, and 3, respectively.
AB - The BioNLP'09 Shared Task deals with extracting information on molecular events, such as gene expression and protein localization, from natural language text. Information in this benchmark are given as tuples including protein names, trigger terms for each event, and possible other participants such as bindings sites. We address all three tasks of BioNLP'09: event detection, event enrichment, and recognition of negation and speculation. Our method for the first two tasks is based on a deep parser; we store the parse tree of each sentence in a relational database scheme. From the training data, we collect the dependencies connecting any two relevant terms of a known tuple, that is, the shortest paths linking these two constituents. We encode all such linkages in a query language to retrieve similar linkages from unseen text. For the third task, we rely on a hierarchy of hand-crafted regular expressions to recognize speculation and negated events. In this paper, we added extensions regarding a post-processing step that handles ambiguous event trigger terms, as well as an extension of the query language to relax linkage constraints. On the BioNLP Shared Task test data, we achieve an overall F1-measure of 32%, 29%, and 30% for the successive Tasks 1, 2, and 3, respectively.
KW - event extraction
KW - parse tree database
KW - sentence parsing
KW - text mining
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U2 - 10.1111/j.1467-8640.2011.00404.x
DO - 10.1111/j.1467-8640.2011.00404.x
M3 - Article
AN - SCOPUS:82455189733
SN - 0824-7935
VL - 27
SP - 665
EP - 680
JO - Computational Intelligence
JF - Computational Intelligence
IS - 4
ER -