Abstract: A method of using a computing device using natural language software to respond to a Boolean question is presented. The method includes receiving, by a computing device, a question and at least one passage. The computing device classifies the question as a Boolean type question. The computing device generates evidence from the at least one passage to the Boolean question. The computing device generates a response to the Boolean question using the generated evidence. The computing device provides the generated evidence that supports the response to the Boolean question.

Abstract: In a computer-implemented method for improving a static analyzer output, a processor receives a labeled data set with labeled true vulnerabilities and labeled false vulnerabilities. A processor receives pretrained contextual embeddings from a contextual embeddings model. A processor maps the true vulnerabilities and the false vulnerabilities to the pretrained contextual embeddings model. A processor generates a fine-tuned model with classifications for true vulnerabilities.

Abstract: A method of using a computing device using natural language software to respond to a Boolean question is presented. The method includes receiving, by a computing device, a question and at least one passage. The computing device classifies the question as a Boolean type question. The computing device generates evidence from the at least one passage to the Boolean question. The computing device generates a response to the Boolean question using the generated evidence. The computing device provides the generated evidence that supports the response to the Boolean question.

Abstract: In a computer-implemented method for improving a static analyzer output, a processor receives a labeled data set with labeled true vulnerabilities and labeled false vulnerabilities. A processor receives pretrained contextual embeddings from a contextual embeddings model. A processor maps the true vulnerabilities and the false vulnerabilities to the pretrained contextual embeddings model. A processor generates a fine-tuned model with classifications for true vulnerabilities.

Abstract: Cascaded models may be applied to extract facts from a medical text. A first model may be applied to at least a portion of the medical text. The first model extracts at least one first medical fact. The at least one first medical fact is linked to at least first text in the at least a portion of the medical text. A second model may be applied to the first text. The second model extracts at least one second fact that is an attribute of the at least one first medical fact.

Abstract: Cascaded models may be applied to extract facts from a medical text. A first model may be applied to at least a portion of the medical text. The first model extracts at least one first medical fact. The at least one first medical fact is linked to at least first text in the at least a portion of the medical text. A second model may be applied to the first text. The second model extracts at least one second fact that is an attribute of the at least one first medical fact.

Abstract: Cascaded models may be applied to extract facts from a medical text. A first model may be applied to at least a portion of the medical text. The first model extracts at least one first medical fact. The at least one first medical fact is linked to at least first text in the at least a portion of the medical text. A second model may be applied to the first text. The second model extracts at least one second fact that is an attribute of the at least one first medical fact.

Abstract: A method, apparatus and article of manufacture for determining annotation quality, including obtaining N annotations on an artifact, wherein each annotation includes a feature and N annotations include three or more annotations including an annotation provided by a first human annotator, zero or more human annotations and zero or more imposter annotations, selectively displaying the N annotations to the first human annotator, wherein the annotation provided by the first human annotator is completely visible and each of the other N?1 annotations includes a feature hidden, and determining to annotation quality of one of the N?1 annotations based on input from the first human annotator regarding the displayed annotations, wherein ability of the first human annotator to identify imposter annotations or recognize that no imposter annotation exists is gated by the quality of the first human's annotation via hiding other annotations and requiring a probe based on the first human annotator's annotation.

Abstract: An improved method, apparatus, and computer instructions displaying search results. A query is received in a first language. A snippet in the document containing the word is selected in response to identifying a document in a second language in which the document contains a word matching a query word in the query. This snippet is translated into the first language.

Abstract: There is provided a method for compressing an index file in an information retrieval system that retrieves information from a plurality of documents. Each of the plurality of documents has features occurring therein. Each of the features has parameters corresponding thereto. Parameter values corresponding to the parameters of the features are mapped into a plurality of bins. Bin identifiers are stored in the index file. Each of the bin identifiers identifies a bin to which is assigned at least one individual parameter value corresponding to at least one individual parameter.

Abstract: An audio indexing system including, in addition to a speech recognition subsystem for converting the audio information into a textual form and an indexing subsystem for extracting the features to be used for searching and browsing, a statistical machine translation model, trained on a parallel or comparable corpus of automatically and by-hand transcribed data, for processing the output of the speech recognition system.

Abstract: There is provided a method for compressing an index file in an information retrieval system that retrieves information from a plurality of documents. Each of the plurality of documents has features occurring therein. Each of the features has parameters corresponding thereto. Parameter values corresponding to the parameters of the features are mapped into a plurality of bins. Bin identifiers are stored in the index file. Each of the bin identifiers identifies a bin to which is assigned at least one individual parameter value corresponding to at least one individual parameter.

Abstract: A method for retrieving information, in accordance with the present invention, includes the steps of providing an initial query in a first language, retrieving data in a second language in accordance with the initial query, formulating the query in the second language, retrieving data in a third language in accordance with the query formulated in the second language and outputting data retrieved in the third language in accordance with the initial query. A system is also provided in accordance with the invention.

Abstract: A system and method for translating a series of source words in a first language to a series of target words in a second language is provided. The system includes an input device for inputting the series of source words. A fertility hypothesis generator operatively coupled to the input device generates at least one fertility hypotheses for a fertility of a source word, based on the source word and a context of the source word. A sense hypothesis generator operatively coupled to the input device generates sense hypotheses for a translation of the source word, based on the source word and the context of the source word. A fertility model operatively coupled to the fertility hypothesis generator determines a probability of the fertility of the source word, based on the source word and the context of the source word.