Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most information extraction systems either use hand written extraction patterns or use a machine learning algorithm that is trained on a manually annotated corpus. Both of these approaches require massive human effort and hence prevent information extraction from becoming more widely applicable.   present URES (Unsupervised Relation Extraction System), which extracts relations from the b in a totally unsupervised way. It takes as input the descriptions of the target relations, which include the names of the predicates, the types of their attributes, and several seed instances of the relations. Then the system downloads from the b a large collection of pages that are likely to contain instances of the target relations. From those pages, utilizing the known seed instances, the system learns the relation patterns, which are then used for extraction.  present several experiments in which  learn patterns and extract instances of a set of several common IE relations, comparing several pattern learning and filtering setups.  demonstrate that using simple noun phrase tagger is sufficient as a base for accurate patterns. Hover, having a named entity recognizer, which is able to recognize the types of the relation attributes significantly, enhances the extraction performance.  also compare our approach with KnowItAll's fixed generic patterns.
['Benjamin Rosenfeld', 'Ronen Feldman']
I am writing a research paper on a idea URES : an Unsupervised Web Relation Extraction System
[{"from": "human", "value": "I am writing a research paper on a idea URES : an Unsupervised Web Relation Extraction System"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most information extraction systems either use hand written extraction patterns or use a machine learning algorithm that is trained on a manually annotated corpus. Both of these approaches require massive human effort and hence prevent information extraction from becoming more widely applicable.   present URES (Unsupervised Relation Extraction System), which extracts relations from the b in a totally unsupervised way. It takes as input the descriptions of the target relations, which include the names of the predicates, the types of their attributes, and several seed instances of the relations. Then the system downloads from the b a large collection of pages that are likely to contain instances of the target relations. From those pages, utilizing the known seed instances, the system learns the relation patterns, which are then used for extraction.  present several experiments in which  learn patterns and extract instances of a set of several common IE relations, comparing several pattern learning and filtering setups.  demonstrate that using simple noun phrase tagger is sufficient as a base for accurate patterns. Hover, having a named entity recognizer, which is able to recognize the types of the relation attributes significantly, enhances the extraction performance.  also compare our approach with KnowItAll's fixed generic patterns."}]