Sgopal1 writeup open Information Extraction from Web
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Revision as of 03:39, 4 November 2009 by Sgopal1 (talk | contribs) (Created page with 'This is a review of reviewed paper::Banko_2007_open_information_extraction_from_the_web by reviewer::user:sgopal1. This paper introduces TextRunner - A scalable,domain i…')
This is a review of Banko_2007_open_information_extraction_from_the_web by user:sgopal1.
This paper introduces TextRunner - A scalable,domain independent way to extract information. It consists of three parts
- Self-supervised learner: Given a small corpus, it labels its own positive and negative instances on the corpus based on some heuristics using the parse of the sentence etc. It maps a subsequence of the words from the sentence to some feature vector, and labels the feature vector as either positive or negative. A NBC is trained on this self-labeled dataset.
- Single pass extractor: Another heuristic to extract subsequences between two noun-phrases is employed. Non-essential words in the subsequence is eliminated. This is then classified as positive/negative by the NBC previously trained.
- Query processing: It is possible to run queries and view the results in a faster way.
- I do not understand the self-labeling part. It seems a little vague as to what exactly they perform.
- In the Query processing module, each relation is allocated to a particular machine. I dont think that this is the way a general assignment, because a given query will be computed only a particular machine and this is ( although I'm not 100% sure ) considered bad - there is no parallelism for a given query. This module is not very impressive.
