International Business Machines Corporation

Google Inc.

Abhirut Gupta

Srikanth Govindaraj Tamilselvam

William Graham O'Keeffe

John P Bufe, Iii

Manish Kumar

Abhay Sharma

Aravindan Raghuveer

Nitin Raut

Systems and methods for generating phonetic spelling variations of a given word based on locale-specific pronunciations. A phoneme-letter density model may be configured to identify a phoneme sequence corresponding to an input word, and to identify all character sequences that may correspond to an input phoneme sequence and their respective probabilities. The phoneme-phoneme error model may be configured to identify locale-specific alternative phoneme sequences that may correspond to a given phoneme sequence, and their respective probabilities. Using these two models, a processing system may be configured to generate, for a given input word, a list of alternative character sequences that may correspond to the input word based on locale-specific pronunciations, and/or a probability distribution representing how likely each alternative character sequence is to correspond to the input word.

Systems and methods for generating locale-specific phonetic spelling variations

A technique for instance-specific feature-based cross-document sentiment aggregation includes analyzing input data to determine an entity referenced in the input data. One or more salient features of the entity are identified. Multiple documents that reference the entity are located. The salient features in each of the multiple documents are identified. Respective sentiment values are assigned to each of the salient features identified in the multiple documents. Respective sub-graphs are created for each of the multiple documents. The sub-graphs specify the sentiment values assigned to each of the salient features identified in an associated one of the multiple documents. The assigned sentiment values for each of the salient features are aggregated based on the sub-graphs. Finally, output data that is associated with the input data is generated. The output data provides an indication of the aggregated sentiment value for each of the salient features.

Techniques for instance-specific feature-based cross-document sentiment aggregation

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Bengaluru, India

Abhirut Gupta