Swapna Somasundaran

20120078918, Mar 29, 2012

Aug 22, 2011

13/214291

Swapna Somasundaran - Lawrenceville NJ, US
Dingcheng Li - St. Paul MN, US
Amit Chakraborty - East Windsor NJ, US

Siemens Corporation - Iselin NJ

G06F 17/30

707748, 707E17009

For generating a word space, manual thresholding of word scores is used. Rather than requiring the user to select the threshold arbitrarily or review each word, the user is iteratively requested to indicate the relevance of a given word. Words with greater or lesser scores are labeled in the same way depending upon the response. For determining the relationship between named entities, Latent Dirichlet Allocation (LDA) is performed on text associated with the name entities rather than on an entire document. LDA for relationship mining may include context information and/or supervised learning.

20130066870, Mar 14, 2013

May 24, 2012

13/479363

Swapna Somasundaran - Lawrenceville NJ, US
Vinodkumar Prabhakaran - Bronx NY, US
Vinay Damodar Shet - Princeton NJ, US
Kateryna Tymoshenko - Trento, IT
Mathäus Dejori - NY NY, US

G06F 17/30

707737, 707E17091

A system generates medical knowledge base information by searching at least one repository of medical information to identify sentences including a received medical term. A data processor searches the identified sentences to identify sentences including a medical term different to the received term in response to a predetermined repository of medical terms and excludes sentences without a term different to the received term, to provide remaining multiple term sentences. The data processor groups different terms of individual sentences of the multiple term sentences to provide grouped terms, determines whether a medically valid relationship occurs between different terms of an individual group of terms of the grouped terms by using predetermined sentence structure and syntax rules and outputs data representing grouped terms having a medically valid relationship.

20130066903, Mar 14, 2013

May 24, 2012

13/479388

Kateryna Tymoshenko - Trento, IT
Swapna Somasundaran - Lawrenceville NJ, US
Vinay Damodar Shet - Princeton NJ, US

SIEMENS CORPORATOIN - Iselin NJ

G06F 17/30

707769, 707E17014

A system generates medical knowledge base information by using predetermined data source specific message syntax information in identifying first and second information received from first and second data sources respectively. The first and second information indicates at least one type of medical relationship between the received first and second medical terms. The system determines likelihood of existence of the at least one type of medical relationship indicated by a combination of the first and second information, in response to predetermined information indicating a number of occurrences of the at least one type of relationship in data of at least one of the first and second data source. The system outputs first and second medical terms and the at least one type of medical relationship in response to the determined likelihood of existence.

20150254229, Sep 10, 2015

Mar 6, 2015

14/640121

- Princeton NJ, US
Nitin Madnani - Princeton NJ, US
Jill Burstein - Princeton NJ, US
Swapna Somasundaran - Plainsboro NJ, US

G06F 17/27
G09B 7/02
G09B 5/06

Systems and methods are provided for a computer-implemented method of providing a score that measures an essay's usage of source material provided in at least one written text and an audio recording. Using one or more data processors, a determination is made of a list of n-grams present in a received essay. For each of a plurality of present n-grams, an n-gram weight is determined, where the n-gram weight is based on a number of appearances of that n-gram in the at least one written text and a number of appearances of that n-gram in the audio recording, and an n-gram sub-metric is determined based on the presence of the n-gram in the essay and the n-gram weight. A source usage metric is determined based on the n-gram sub-metrics for the plurality of present n-grams, and a scoring model is used to generate a score for the essay based on the source usage metric.

20150248397, Sep 3, 2015

Feb 27, 2015

14/633200

- Princeton NJ, US
Swapna Somasundaran - Plainsboro NJ, US
Martin Chodorow - New York NY, US

G06F 17/27
G06F 17/28

Systems and methods are provided for automatically generating a coherence score for a text using a scoring model. A lexical chain is identified within a text to be scored, where the lexical chain comprises a set of words spaced within the text. A discourse element is identified within the text, where the discourse element comprises a word within the text. A coherence metric is determined based on a relationship between the lexical chain and the discourse element. A coherence score is generated using a scoring model by providing the coherence metric to the scoring model.