Abstract: Technologies are described herein for cross-language speech recognition and translation. An example method of speech recognition and translation includes receiving an input utterance in a first language, the input utterance having at least one name of a named entity included therein and being pronounced in a second language, utilizing a customized language model to process at least a portion of the input utterance, and identifying the at least one name of the named entity from the input utterance utilizing a phonetic representation of the at least one name of the named entity. The phonetic representation has a pronunciation of the at least one name in the second language.

Abstract: A method for learning a process behavior model based on a process past instances and on one or more process attributes, and a method for detecting an anomalous process using the corresponding process behavior model.

Abstract: Technologies are described herein for cross-language speech recognition and translation. An example method of speech recognition and translation includes receiving an input utterance in a first language, the input utterance having at least one name of a named entity included therein and being pronounced in a second language, utilizing a customized language model to process at least a portion of the input utterance, and identifying the at least one name of the named entity from the input utterance utilizing a phonetic representation of the at least one name of the named entity. The phonetic representation has a pronunciation of the at least one name in the second language.

Abstract: A method for learning a process behavior model based on a process past instances and on one or more process attributes, and a method for detecting an anomalous process using the corresponding process behavior model.

Abstract: A method for learning a process behavior model based on a process past instances and on one or more process attributes, and a method for detecting an anomalous process using the corresponding process behavior model.

Abstract: One embodiment of the disclosure can represent within a K-partite graph, weighting factors between a set of identifier elements and a set of data elements. The K-partite graph can also represent weighting factors between the set of identifier elements and a set of metadata elements. In the K-partite graph, the set of identifier elements, the set of data elements, and the set of metadata elements are independent and disjoint sets such that no two vertices within a same set are adjacent. A score vector can be calculated that ranks each data element in a set of data elements. The score vector can be calculated from the weighting factors. At least one data element from the set of data elements can be selected using the score vector and a predetermined selection criterion.

Abstract: One embodiment of the disclosure can represent within a K-partite graph, weighting factors between a set of identifier elements and a set of data elements. The K-partite graph can also represent weighting factors between the set of identifier elements and a set of metadata elements. In the K-partite graph, the set of identifier elements, the set of data elements, and the set of metadata elements are independent and disjoint sets such that no two vertices within a same set are adjacent. A score vector can be calculated that ranks each data element in a set of data elements. The score vector can be calculated from the weighting factors. At least one data element from the set of data elements can be selected using the score vector and a predetermined selection criterion.

Abstract: One embodiment of the disclosure can represent within a K-partite graph, weighting factors between a set of identifier elements and a set of data elements. The K-partite graph can also represent weighting factors between the set of identifier elements and a set of metadata elements. In the K-partite graph, the set of identifier elements, the set of data elements, and the set of metadata elements are independent and disjoint sets such that no two vertices within a same set are adjacent. A score vector can be calculated that ranks each data element in a set of data elements. The score vector can be calculated from the weighting factors. At least one data element from the set of data elements can be selected using the score vector and a predetermined selection criterion.

Abstract: A mechanism is provided for determining a second document of a set of documents in a second language having the same textual content as a first document in a first language. A first histogram that is indicative of the textual content of the first document is generated. A second histogram is generated for each document of the set of documents. Each second histogram is indicative of the textual content of a document of the set of documents. Each second histogram is compared with the first histogram to determine at least one histogram from the plurality of second histograms which matches the first histogram. The second document is then identified as the document having the at least one histogram.

Abstract: One embodiment of the disclosure can represent within a K-partite graph, weighting factors between a set of identifier elements and a set of data elements. The K-partite graph can also represent weighting factors between the set of identifier elements and a set of metadata elements. In the K-partite graph, the set of identifier elements, the set of data elements, and the set of metadata elements are independent and disjoint sets such that no two vertices within a same set are adjacent. A score vector can be calculated that ranks each data element in a set of data elements. The score vector can be calculated from the weighting factors. At least one data element from the set of data elements can be selected using the score vector and a predetermined selection criterion.

Abstract: The present invention is directed to a system, method and computer program for automatically extracting and mining relations and related entities from unstructured text. A method in accordance with an embodiment of the invention includes: extracting relations and related entities from unstructured text data, representing the extracted information into a graph, and manipulating the resulting graph to gain more insight into the information it contains. The extraction of relations and related entities is performed first by automatically inducting pattern and second by applying these induced patterns to unstructured text data. For each relation and entity, several features are extracted in order to build a graph whose nodes are entities and edges are relations.

Abstract: A mechanism is provided for determining a second document of a set of documents in a second language having the same textual content as a first document in a first language. A first histogram that is indicative of the textual content of the first document is generated. A second histogram is generated for each document of the set of documents. Each second histogram is indicative of the textual content of a document of the set of documents. Each second histogram is compared with the first histogram to determine at least one histogram from the plurality of second histograms which matches the first histogram. The second document is then identified as the document having the at least one histogram.

Abstract: A method for learning a process behavior model based on a process past instances and on one or more process attributes, and a method for detecting an anomalous process using the corresponding process behavior model.