We present a simple semi-supervised relation extraction system with large-scale word clustering.

3.1 Relation Extraction

One of the well defined relation extraction tasks is the Automatic Content Extraction1 (ACE) program sponsored by the U.S. government.

Relation extraction is an important information extraction task in natural language processing (NLP), with many practical applications.

The goal of relation extraction is to detect and characterize semantic relations between pairs of entities in text.

For example, a relation extraction system needs to be able to extract an Employment relation between the entities US soldier and US in the phrase US soldier.

A second difference between this work and the above ones is that we utilize word clusters in the task of relation extraction which is very different from sequence labeling tasks such as name tagging and chunking.

(2005) and Chan and Roth (2010) used word clusters in relation extraction , they shared the same limitation as the above approaches in choosing clusters.

In this paper, we observe that there exists a second dimension to the relation extraction (RE) problem that is orthogonal to the relation type dimension.

Relation extraction (RE) has been defined as the task of identifying a given set of semantic binary relations in text.

In this paper we build on the observation that there exists a second dimension to the relation extraction problem that is orthogonal to the relation type dimension: all relation types are expressed in one of several constrained syntactico-semantic structures.

In the next section, we describe our relation extraction framework that leverages the syntactico-semantic structures.

Knowledge-based weak supervision, using structured data to heuristically label a training corpus, works towards this goal by enabling the automated learning of a potentially unbounded number of relation extractors .

automatically learn a nearly unbounded number of relational extractors .

(2009) used Freebase facts to train 100 relational extractors on Wikipedia.

Bunescu and Mooney (2007) connect weak supervision with multi-instance learning and extend their relational extraction kernel to this context.

ConceptResolver performs both word sense induction and synonym resolution on relations extracted from text using an ontology and a small amount of labeled data.

The relations extracted by systems like NELL actually apply to concepts, not to noun phrases.

Synonym resolution on relations extracted from web text has been previously studied by Resolver (Yates and Etzioni, 2007), which finds synonyms in relation triples extracted by TextRunner (Banko et al., 2007).