Location Normalization for Information Extraction

Ambiguity is very high for location names. For example, there are 23 cities named `Buffalo' in the U.S. Country names such as `Canada', `Brazil' and `China' are also city names in the USA. Almost every city has a Main Street or Broadway. Such ambiguity needs to be handled before we can refer to location names for visualization of related extracted events. This paper presents a hybrid approach for location normalization which combines (i) lexical grammar driven by local context constraints, (ii) graph search for maximum spanning tree and (iii) integration of semi-automatically derived default senses. The focus is on resolving ambiguities for the following types of location names: island, town, city, province, and country. The results are promising with 93.8% accuracy on our test collections.

• Type: Journal
• Year: 2002
• Where: COLING
• Authors: Huifeng Li, Rohini K. Srihari, Cheng Niu, Wei Li 0003
• Keywords: City Names, COLING 2002, COLING 2008, Local Context Constraints, Location Names