Stephen G Soderland

8209164, Jun 26, 2012

Nov 21, 2007

11/944100

Oren Etzioni - Seattle WA, US
Kobi Reiter - Seattle WA, US
Marcus Sammer - Seattle WA, US
Michael Schmitz - Seattle WA, US
Stephen Soderland - Bainbridge Island WA, US

University of Washington - Seattle WA

G06F 17/27
G06F 17/28

704 2, 704 9

A translation graph is created using a plurality of reference sources that include translations between a plurality of different languages. Each entry in a source is used to create a wordsense entry, and each new word in a source is used to create a wordnode entry. A pair of wordnode and wordsense entries corresponds to a translation. In addition, a probability is determined for each wordsense entry and is decreased for each translation entry that includes more than a predefined number of translations into the same language. Bilingual translation entries are removed if subsumed by a multilingual translation entry. Triangulation is employed to identify pairs of common wordsense translations between a first, second, and third language. Translations not found in reference sources can also be inferred from the data comprising the translation graph. The translation graph can then be used for searches of a data collection in different languages.

20120271622, Oct 25, 2012

Jun 25, 2012

13/532742

Oren Etzioni - Seattle WA, US
Kobi Reiter - Seattle WA, US
Marcus Sammer - Seattle WA, US
Michael Schmitz - Seattle WA, US
Stephen Soderland - Bainbridge Island WA, US

UNIVERSITY OF WASHINGTON - Seattle WA

G06F 17/20

704 8

A translation graph is created using a plurality of reference sources that include translations between a plurality of different languages. Each entry in a source is used to create a wordsense entry, and each new word in a source is used to create a wordnode entry. A pair of wordnode and wordsense entries corresponds to a translation. In addition, a probability is determined for each wordsense entry and is decreased for each translation entry that includes more than a predefined number of translations into the same language. Bilingual translation entries are removed if subsumed by a multilingual translation entry. Triangulation is employed to identify pairs of common wordsense translations between a first, second, and third language. Translations not found in reference sources can also be inferred from the data comprising the translation graph. The translation graph can then be used for searches of a data collection in different languages.

20140297264, Oct 2, 2014

Nov 18, 2013

14/083342

- Seattle WA, US
Robert E. Bart - Bellevue WA, US
Mausum - Seattle WA, US
Michael D. Schmitz - Langley WA, US
Stephen G. Soderland - Bainbridge Island WA, US

G06F 17/27

704 9

Open Information Extraction (IE) systems extract relational tuples from text, without requiring a pre-specified vocabulary, by identifying relation phrases and associated arguments in arbitrary sentences. However, state-of-the-art Open IE systems such as RVand share two important weaknesses—(1) they extract only relations that are mediated by verbs, and (2) they ignore context, thus extracting tuples that are not asserted as factual. This paper presents , a substantially improved Open IE system that addresses both these limitations. First, achieves high yield by extracting relations mediated by nouns, adjectives, and more. Second, a context-analysis step increases precision by including contextual information from the sentence in the extractions. obtains 2.7 times the area under precision-yield curve (AUC) compared to RVand 1.9 times the AUC of .