Publikation
Minimally Supervised Domain-Adaptive Parse Reranking for Relation Extraction
Feiyu Xu; Hong Li; Yi Zhang; Hans Uszkoreit; Sebastian Krause
In: Proceedings of International Conference on Parsing Technologies (IWPT 2011). International Conference on Parsing Technologies (IWPT-2011), 12th, October 5-7, Dublin, Ireland, Pages 118-128, 2011.
Zusammenfassung
The paper demonstrates how the generic parser of a minimally supervised
information extraction framework can be adapted to a given task and domain for
relation extraction (RE). For the experiments a generic deep-linguistic parser
was employed that works with a largely hand-crafted head-driven phrase
structure grammar (HPSG) for English. The output of this parser is a list of n
best parses selected and ranked by a MaxEnt parse-ranking component, which had
been trained on a more or less generic HPSG treebank. It will be shown how the
estimated confidence of RE rules learned from the n best parses can be
exploited for parse re-ranking. The acquired re-ranking model improves the
performance of RE in both training and test phases with the new first parses.
The obtained significant boost of recall does not come from an overall gain in
parsing performance but from an application-driven selection of parses that are
best suited for the RE task. Since the readings best suited for successful rule
extraction and instance extraction are often not the readings favored by a
regular parser evaluation, generic parsing accuracy actually decreases. The
novel method for task-specific parse re-ranking does not require any annotated
data beyond the semantic seed, which is needed anyway for the RE task.