Ontological Smoothing for Relation Extraction with Minimal Supervision

By Congle Zhang, Raphael Hoffmann, Daniel S. Weld. 2012

28 citations recorded by the source corpus.

Relation extraction, the process of converting natural language text into structured knowledge, is increasingly important. Most successful techniques use supervised machine learning to generate extractors from sentences that have been manually labeled with the relations' arguments. Unfortunately, these methods require numerous training examples, which are expensive and time-consuming to produce. This paper presents ontological smoothing, a semi-supervisedtechnique that learns extractors for a set of minimally-labeledrelations. Ontological smoothing has three phases. First, itgenerates a mapping between the target relations and a backgroundknowledge-base. Second, it uses distant supervision toheuristically generate new training examples for the targetrelations. Finally, it learns an extractor from a combination of theoriginal and newly-generated examples. Experiments on 65 relationsacross three target domains show that ontological smoothing candramatically improve precision and recall, even rivaling fully supervisedperformance in many cases.

Subfields: Artificial Intelligence, Information Extraction

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Citations in the corpus

Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions (2016, methodology): “We use the ILP formalism, which has been successful in several NLP tasks (Roth and Yih 2004; Srikumar and Roth 2011; Zhang, Hoffmann, and Weld 2012) but, to our knowledge, has not been applied directly to question-answering using semi-structured knowledge.”
Scaling Semantic Parsers with On-the-Fly Ontology Matching (2013, methodology): “, 2007), which has inspired more recent work on distant supervision for relation extraction with Freebase (Zhang et al., 2012).”
CORE: Context-Aware Open Relation Extraction with Factorization Machines (2015, background): “Information such as bag-of-words, part-of-speech tags, entity types, or parse trees have been integrated into many existing systems (Mintz et al., 2009; Zhang et al., 2012; Takamatsu et al., 2011; Zhou et al., 2007; de Lacalle and Lapata, 2013; Akbik et al., 2012).”