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: Named entity recognition is described, for example, to detect an instance of a named entity in a web page and classify the named entity as being an organization or other predefined class. In various examples, named entity recognition results are used to augment text from which the named entity was recognized; the augmentation may comprise information retrieval results about the named entity mention. In various embodiments, labeled training sentences in many different languages and for many different classes, are obtained to train machine learning components of a multi-lingual, multi-class, named entity recognition system. In examples, labeled training sentences are obtained from at least two sources, a first source using a multi-lingual or monolingual corpus of inter-linked documents and a second source using machine translation training data. In examples, labeled training sentences from the two sources are selectively sampled for training the named entity recognition system.

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: Named entity recognition is described, for example, to detect an instance of a named entity in a web page and classify the named entity as being an organization or other predefined class. In various examples, named entity recognition results are used to augment text from which the named entity was recognized; the augmentation may comprise information retrieval results about the named entity mention. In various embodiments, labeled training sentences in many different languages and for many different classes, are obtained to train machine learning components of a multi-lingual, multi-class, named entity recognition system. In examples, labeled training sentences are obtained from at least two sources, a first source using a multi-lingual or monolingual corpus of inter-linked documents and a second source using machine translation training data. In examples, labeled training sentences from the two sources are selectively sampled for training the named entity recognition system.