Minimally Supervised Domain-Adaptive Parse Reranking for Relation Extraction

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 headdriven 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 reranking. The acquired reranking 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 taskspecific parse reranking does not require any annotated data beyond the semantic seed, which is needed anyway for the RE task.