This paper presents LUCHS, an autonomous, self-supervised system, which learns 5025 relational extractors — an order of magnitude greater than any previous effort.

In order to handle sparsity in its heuristically- generated training data, LUCHS generates custom lexicon features when learning each relational extractor .

Our experiments demonstrate a high Fl score, 61%, across the 5025 relational extractors learned.

We therefore choose a hierarchical approach that combines both article classifiers and relation extractors .

is likely to contain a schema, does LUCHS run that schema’s relation extractors .

(Mintz et al., 2009) uses Freebase to provide distant supervision for relation extraction .

They applied a similar heuristic by matching Freebase tuples with unstructured sentences (Wikipedia articles in their experiments) to create features for learning relation extractors .

(Akbik and BroB, 2009) annotated 10,000 sentences parsed with LinkGrammar and selected 46 general linkpaths as patterns for relation extraction .

noted in (de Marneffe and Manning, 2008), this collapsed format often yields simplified patterns which are useful for relation extraction .

Dependency Tree Kernel for Relation Extraction .

Kernel Methods for Relation Extraction .

Exploring Syntactic Features for Relation Extraction using a Convolution Tree Kernel.

Indeed, using kernel methods to mine structural knowledge has shown success in some NLP applications like parsing (Collins and Duffy, 2001; Moschitti, 2004) and relation extraction (Zelenko et al., 2003; Zhang et al., 2006).