The article demonstrates how generic parsers in a minimally supervised information extraction framework can be adapted to a given task and domain for relation extraction (RE). For the experiments, two parsers that deliver n-best readings are included: (1) a generic deep-linguistic parser (PET) with a largely hand-crafted head-driven phrase structure grammar for English (ERG); (2) a generic statistical parser (Stanford Parser) trained on the Penn Treebank. It will be shown how the estimated confidence of RE rules learned from the n-best parses can be exploited for parse reranking for both parsers. 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 the successful extraction of rules and instances are often not the readings favoured by a regular parser evaluation, generic parsing accuracy actually decreases. The novel method for task-specific parse reranking does not require any annotated data beyond the semantic seed, which is needed anyway for the RE task.

Extracting instances of sentiment-oriented relations from user-generated web documents is important for online marketing analysis. Unlike previous work, we formulate this extraction task as a structured prediction problem and design the corresponding inference as an integer linear program. Our latent structural SVM based model can learn from training corpora that do not contain explicit annotations of sentiment-bearing expressions, and it can simultaneously recognize instances of both binary (polarity) and ternary (comparative) relations with regard to entity mentions of interest. The empirical evaluation shows that our approach significantly outperforms state-of-the-art systems across domains (cameras and movies) and across genres (reviews and forum posts). The gold standard corpus that we built will also be a valuable resource for the community.