Abstract: Grammatical element prediction is used in the context of machine translation. Features from both the source language and the target language sentences (or other text fragments) are used in predicting the grammatical elements.

Abstract: Grammatical element prediction is used to predict grammatical elements in text fragments (such as phrases or sentences). In one embodiment, a statistical model, using syntax features, is used to predict grammatical elements.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A method of training language model parameters trains discriminative model parameters in the language model based on a performance measure having discrete values.

Abstract: A method and apparatus for generating a score for a system that generates text is provided. The method and apparatus identify errors in the text generated by the system and identify errors in a second text generated by a second system. The number of errors that are generated by the system but not generated by the second system is divided by the number of errors that are generated by the second system but not by the system to generate the score.

Abstract: Embodiments of the present invention provide a method and apparatus for segmenting text by providing orthographic and inflectional variations to a syntactic parser. Under the present invention, possible segments are first identified in the sequence of characters. At least two of the identified segments overlap each other. For at least one of the segments, an alternative sequence of characters is identified. In some cases, this alternative sequence is formed through inflectional morphology, which identifies a different lexical form for a word identified by the segment. In some cases, the alternative sequence represents an orthographic variant of a word identified by the segment. The identified segments and the alternative segments are then passed to a syntactic analyzer, which produces one or more syntactic parses. The segments found in the resulting parses represent the segmentation of the input sequence of characters.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A system is utilized for determining a relationship between first and second textual inputs. The system identifies constituents in the first textual input, having predetermined characteristics indicative of usefulness in determining the relationship. The relationship is then determined based on the constituents identified. The constituents can be eliminated from the first textual input, weighted in the first textual input, or simply annotated in one of a variety of ways.

Abstract: A word breaking facility operates to identify words within a Japanese text string. The word breaking facility performs morphological processing to identify postfix bound morphemes and prefix bound morphemes. The word breaking facility also performs opheme matching to identify likely stem characters. A scoring heuristic is applied to determine an optimal analysis that includes a postfix analysis, a stem analysis, and a prefix analysis. The morphological analyses are stored in an efficient compressed format to minimize the amount of memory they occupy and maximize the analysis speed. The morphological analyses of postfixes, stems, and prefixes is performed in a right-to-left fashion. The word breaking facility may be used in applications that demand identity of selection granularity, autosummarization applications, content indexing applications, and natural language processing applications.

Abstract: A word breaking facility operates to identify words within a Japanese text string. The word breaking facility performs morphological processing to identify postfix bound morphemes and prefix bound morphemes. The word breaking facility also performs opheme matching to identify likely stem characters. A scoring heuristic is applied to determine an optimal analysis that includes a postfix analysis, a stem analysis, and a prefix analysis. The morphological analyses are stored in an efficient compressed format to minimize the amount of memory they occupy and maximize the analysis speed. The morphological analyses of postfixes, stems, and prefixes is performed in a right-to-left fashion. The word breaking facility may be used in applications that demand identity of selection granularity, autosummarization applications, content indexing applications, and natural language processing applications.