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: Natural language image search is described, for example, whereby natural language queries may be used to retrieve images from a store of images automatically tagged with image tags being concepts of an ontology (which may comprise a hierarchy of concepts). In various examples, a natural language query is mapped to one or more of a plurality of image tags, and the mapped query is used for retrieval. In various examples, the query is mapped by computing one or more distance measures between the query and the image tags, the distance measures being computed with respect to the ontology and/or with respect to a semantic space of words computed from a natural language corpus. In examples, the image tags may be associated with bounding boxes of objects depicted in the images, and a user may navigate the store of images by selecting a bounding box and/or an image.

Abstract: Natural language image search is described, for example, whereby natural language queries may be used to retrieve images from a store of images automatically tagged with image tags being concepts of an ontology (which may comprise a hierarchy of concepts). In various examples, a natural language query is mapped to one or more of a plurality of image tags, and the mapped query is used for retrieval. In various examples, the query is mapped by computing one or more distance measures between the query and the image tags, the distance measures being computed with respect to the ontology and/or with respect to a semantic space of words computed from a natural language corpus. In examples, the image tags may be associated with bounding boxes of objects depicted in the images, and a user may navigate the store of images by selecting a bounding box and/or an image.

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.

Abstract: Natural language image search is described, for example, whereby natural language queries may be used to retrieve images from a store of images automatically tagged with image tags being concepts of an ontology (which may comprise a hierarchy of concepts). In various examples, a natural language query is mapped to one or more of a plurality of image tags, and the mapped query is used for retrieval. In various examples, the query is mapped by computing one or more distance measures between the query and the image tags, the distance measures being computed with respect to the ontology and/or with respect to a semantic space of words computed from a natural language corpus. In examples, the image tags may be associated with bounding boxes of objects depicted in the images, and a user may navigate the store of images by selecting a bounding box and/or an image.