Abstract: A computer-implemented technique is described herein for extracting facts from unstructured text documents provided by one or more information sources. The technique uses a pipeline to perform this operation that involves, at least in part, providing a corpus of information items, extracting candidate facts from the information items, merging synonymous argument values associated with the candidate facts, organizing the candidate facts into relation clusters, and assessing the confidence level of the candidate facts within the relation clusters.

Abstract: A computer-implemented technique is described herein for extracting facts from unstructured text documents provided by one or more information sources. The technique uses a pipeline to perform this operation that involves, at least in part, providing a corpus of information items, extracting candidate facts from the information items, merging synonymous argument values associated with the candidate facts, organizing the candidate facts into relation clusters, and assessing the confidence level of the candidate facts within the relation clusters.

Abstract: A computer-implemented technique is described herein for extracting facts from unstructured text documents provided by one or more information sources. The technique uses a pipeline to perform this operation that involves, at least in part, providing a corpus of information items, extracting candidate facts from the information items, merging synonymous argument values associated with the candidate facts, organizing the candidate facts into relation clusters, and assessing the confidence level of the candidate facts within the relation clusters.

Abstract: Retrieving from the Internet unstructured text related to a specified domain is described. Training data is accessed; the training data comprises unstructured text related to the specified domain. A first classifier is trained using features of the training data. It is used to classify unstructured text having plurality of features, to obtain unstructured text examples related to the domain. The unstructured text examples are used to retrieve from the Internet similar examples which do not have at least some of the plurality of features. Optionally, a second classifier is trained using the similar examples. Additional unstructured text is retrieved from the Internet and the second classifier is used to label the additional unstructured text for domain relevance.

Abstract: A computer-implemented technique is described herein for extracting facts from unstructured text documents provided by one or more information sources. The technique uses a pipeline to perform this operation that involves, at least in part, providing a corpus of information items, extracting candidate facts from the information items, merging synonymous argument values associated with the candidate facts, organizing the candidate facts into relation clusters, and assessing the confidence level of the candidate facts within the relation clusters.

Abstract: Retrieving from the Internet unstructured text related to a specified domain is described. Training data is accessed; the training data comprises unstructured text related to the specified domain. A first classifier is trained using features of the training data. It is used to classify unstructured text having plurality of features, to obtain unstructured text examples related to the domain. The unstructured text examples are used to retrieve from the Internet similar examples which do not have at least some of the plurality of features. Optionally, a second classifier is trained using the similar examples. Additional unstructured text is retrieved from the Internet and the second classifier is used to label the additional unstructured text for domain relevance.