Paypal, Inc.

Dhamodharan Lakshmipathy

Rushik Navinbhai Upadhyay

Nandhini Ramesh

Aditya Kaulagi

Techniques are disclosed relating to implementing a machine learning approach to cross-language translation and search. In certain embodiments, a method may include receiving a plurality of characters of a first language that are unsegmented and grouping the plurality of character into multiple groups. The method also includes determining a set of word tokens based on one or more transliterations of the multiple groups and one or more translations of the multiple groups to a second language. Further, the method includes generating one or more word token solution sets by querying an index file using the one or more word tokens. The method also includes determining whether the index file references an entity name corresponding to the plurality of characters of the first language based on comparing the one or more token solution sets with the index file.

Machine learning approach to cross-language translation and search

A method may include determining that input text data includes a first keyword from a first set of keywords. The method also includes determining a similarity between the input text data and a first stored text string that has previously been identified as a false positive match for the first keyword, and based on the similarity, generating a first false positive score corresponding to the input text data. Further, the method includes determining a number of keywords, from a second set of keywords, that are included in the input text data, and based on the number of keywords, generating a second false positive score corresponding to the input text data. The method also includes calculating a final false positive score corresponding to the input text data based on the first false positive score and the second false positive score.

Identifying false positives between matched words

There are provided systems and methods for intelligent analysis of digital symbols for message content determination. A user may engage in messaging with another user, such as using a messaging platform that may include electronic transaction processing through a service provider. The service provider may have compliance rules, such as to avoid illegal, fraudulent, or otherwise noncompliant electronic transaction processing. In this regard, messaging between users may be analyzed to detect noncompliant messaging and activities by users on the service provider's platforms. The messages may include graphical images or icons, which may be converted to text so that plain text of the message may be analyzed. This may be done through a mapping of images-to-text, as well as an artificial intelligence model train to convert images into text. Thereafter, the plain text may be analyzed to determine if the message violates any compliance rules.

Intelligent analysis of digital symbols for message content determination

Techniques are disclosed relating to scoring partial matches between words. In certain embodiments, a method may include receiving a request to determine a similarity between an input text data and a stored text data. The method also includes determining, based on comparing one or more words included in the input text data with one or more words included in the stored text data, a set of word pairs and a set of unpaired words. Further, in response to determining that the set of unpaired words passes elimination criteria, the method includes calculating a base similarity score between the input text data and the stored text data based on the set of word pairs. The method also includes determining a scoring penalty based on the set of unpaired words and generating a final similarity score between the input text data and the stored text data by modifying the base similarity score based on the scoring penalty.

Algorithm for scoring partial matches between words

Techniques are disclosed relating to determining phonetic similarity using machine learning. The techniques include accessing training data that includes a first set of words of a native language and a second set of words corresponding to verified transliterations of the first set of words from the native language to a target language. Further, they include generating a set of new transliterations of the first set of words from the native language to the target language and storing comparison information based on a comparison between words from the second set of words and word from the set of new transliterations of the first set of words. Finally, a similarity score is determined between a first word of the target language and a second word of the target language based on the comparison information.

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San Jose, CA, United States of America

Dhamodharan Lakshmipathy