Bootstrapping Ternary Relation Extractors
This addresses the challenge of data scarcity for higher n-ary relation extraction, which is incremental as it builds on existing binary methods.
The paper tackles the problem of generating training data for ternary relation extraction, which is more cognitively demanding than binary relations, by presenting a resource created using a minimally supervised approach, including statistics on dataset size and quality.
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.