Multi-Document Coreference Resolution Task

A Multi-Document Coreference Resolution Task is an Entity Mention Coreference Resolution Task where the Entity Mentions can be in different Documents.

• AKA: Cross-Document Entity Tracking, Cross-Document Coreference.
• Context:
  • Input: two or more Documents.
  • output: sets of Entity Mention Identifiers that share Entity Referents.
  • It can be solved by a Multi-Document Coreference Resolution System (that implements a Multi-Document Coreference Resolution Algorithm.
• Example(s):
  • “John Smith” is a challenging mention because it can refer to many different people.
  • “Queen Elizabeth” and “Queen Elizabeth the 1^st” is a challenging mention because it can refer to a person or a ship.
  • Spock Entity Resolution Challenge.
  • Google Research's WikiLinks Dataset.
• See: Single-Document Coreference Resolution Task, Multi-Document Question-Answering Task, Multi-Document Summarization Task.

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References

2012

• http://www.iesl.cs.umass.edu/data/wiki-links
  • Cross-document coreference resolution is the task of grouping the entity mentions in a collection of documents into sets that each represent a distinct entity. It is central to knowledge base construction and also useful for joint inference with other NLP components. Obtaining large, organic labeled datasets for training and testing cross-document coreference has previously been difficult. We use a method for automatically gathering massive amounts of naturally-occurring cross-document reference data to create the Wikilinks dataset comprising of 40 million mentions over 3 million entities.

2006

• Anagha Kulkarni, and Ted Pedersen. (2006). “How Many Different "John Smiths", and Who Are They?.” In: Proceedings of AAAI 2006.

2004

• Michael B Fleischman, and Eduard Hovy. (2004). “Multi-Document Person Name Resolution.” In: Proceedings ofACL 2004.
• Chung Heong Gooi, and James Allan. (2004). “Cross-Document Coreference on a Large Scale Corpus.” In: HLT-NAACL 2004.

1998

• (Bagga & Baldwin, 1998) ⇒ Amit Bagga, and Breck Baldwin. (1998). “Entity-based cross-document coreferencing using the Vector Space Model.” In: Proceedings of the 17th International Conference on Computational Linguistics (COLING-ACL 1998).

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