Unsupervised Resolution of Objects and Relations on the Web

The task of identifying synonymous relations and objects, or Synonym Resolution (SR), is critical for high-quality information extraction. The bulk of previous SR work assumed strong domain knowledge or hand-tagged training examples. This paper investigates SR in the context of unsupervised information extraction, where neither is available. The paper presents a scalable, fully-implemented system for SR that runs in O(KN log N) time in the number of extractions N and the maximum number of synonyms per word, K. The system, called RESOLVER, introduces a probabilistic relational model for predicting whether two strings are co-referential based on the similarity of the assertions containing them. Given two million assertions extracted from the Web, RESOLVER resolves objects with 78% precision and an estimated 68% recall and resolves relations with 90% precision and 35% recall.

• Type: Conference
• Year: 2007
• Where: NAACL
• Authors: Alexander Yates, Oren Etzioni
• Keywords: Computational Linguistics, Information Extraction, NAACL 2007, Paper Investigates Sr, RESOLVER Resolves Objects