Cardinal Virtues: Extracting Relation Cardinalities from Text
This addresses a novel problem in information extraction for improving recall by capturing cardinalities, but it is incremental as it builds on existing IE methods.
The paper tackles the problem of extracting relation cardinalities from text, such as how many awards someone has won, and presents a distant supervision method that achieves precision between 3% and 55% depending on relation difficulty.
Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations.