Bootstrapping Ternary Relation Extractors

TL;DR

This paper introduces a minimally supervised method for creating training data for ternary relation extractors, addressing the challenge of data scarcity and effort in higher n-ary relation extraction.

Contribution

It presents a new resource and approach for generating training data for ternary relation extraction with minimal supervision.

Findings

Generated a large, high-quality ternary relation dataset

Demonstrated effectiveness of minimally supervised approach

Provided statistical analysis of dataset size and quality

Abstract

Binary relation extraction methods have been widely studied in recent years. However, few methods have been developed for higher n-ary relation extraction. One limiting factor is the effort required to generate training data. For binary relations, one only has to provide a few dozen pairs of entities per relation, as training data. For ternary relations (n=3), each training instance is a triplet of entities, placing a greater cognitive load on people. For example, many people know that Google acquired Youtube but not the dollar amount or the date of the acquisition and many people know that Hillary Clinton is married to Bill Clinton by not the location or date of their wedding. This makes higher n-nary training data generation a time consuming exercise in searching the Web. We present a resource for training ternary relation extractors. This was generated using a minimally supervised yet effective approach. We present statistics on the size and the quality of the dataset.