Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports.   We  observe  that  when  the  type  set  spans  several  domains,  detection  of  entity mention becomes a limitation for supervised learning models.  The primary reason being lack  of  dataset  where  entity  boundaries  are  properly  annotated  while  covering  a  large spectrum of entity types.  Our work directly addresses this issue.  We propose Heuristics Allied with Distant Supervision (HAnDS) framework to automatically construct a quality dataset suitable for the FgER task.  HAnDS framework exploits the high interlink among Wikipedia  and  Freebase  in  a  pipelined  manner,  reducing  annotation  errors  introduced by naively using distant supervision approach.  Using HAnDS framework,  we create two datasets, one suitable for building FgER systems recognizing up to 118 entity types based on the FIGER type hierarchy and another for up to 1115 entity types based on the TypeNet hierarchy.  Our extensive empirical experimentation warrants the quality of the generated datasets.  Along with this, we also provide a manually annotated dataset for benchmarking FgER systems.

Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage

Abhishek Abhishek, Sanya Bathla Taneja, Garima Malik, Ashish Anand, Amit Awekar