Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The task of identifying synonymous relations and objects, or synonym resolution, is critical for high-quality information extraction.  investigates synonym resolution in the context of unsupervised information extraction, where neither hand-tagged training examples nor domain knowledge is available. The paper presents a scalable, fully-implemented system 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. On a set of two million assertions extracted from the b, RESOLVER resolves objects with 78% precision and 68% recall, and resolves relations with 90% precision and 35% recall. Several variations of RESOLVER's probabilistic model are explored, and experiments demonstrate that under appropriate conditions these variations can improve F1 by 5%. An extension to the basic RESOLVER system allows it to handle polysemous names with 97% precision and 95% recall on a data set from the TREC corpus.
['Alexander Yates', 'Oren Etzioni']
I am writing a research paper on a idea Unsupervised methods for determining object and relation synonyms on the web
[{"from": "human", "value": "I am writing a research paper on a idea Unsupervised methods for determining object and relation synonyms on the web"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The task of identifying synonymous relations and objects, or synonym resolution, is critical for high-quality information extraction.  investigates synonym resolution in the context of unsupervised information extraction, where neither hand-tagged training examples nor domain knowledge is available. The paper presents a scalable, fully-implemented system 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. On a set of two million assertions extracted from the b, RESOLVER resolves objects with 78% precision and 68% recall, and resolves relations with 90% precision and 35% recall. Several variations of RESOLVER's probabilistic model are explored, and experiments demonstrate that under appropriate conditions these variations can improve F1 by 5%. An extension to the basic RESOLVER system allows it to handle polysemous names with 97% precision and 95% recall on a data set from the TREC corpus."}]