Automatic lot classification

A system, comprising:

• a processor;
• and
• a memory device storing instructions which, when executed by the processor, causes the system to perform operations comprising:
• receiving an input comprising a string indicating a plurality of items are included in a single listing for publication by a publication system;
• parsing the string to identify a token that comprises a quantity word or a digit, the token being formed by:
• identifying the quantity word in the string;
• converting the identified quantity word into digit form;
• normalizing the string to lowercase characters;
• splitting the normalized string into a series of substrings using a sequence of delimiters;
• and
• splitting the series of substrings by separating a character from an adjacent character based on a difference between the character and the adjacent character;
• determining, by a machine learning model, a probability that the token is indicative of a quantity of the plurality of items;
• classifying the single listing as a lot listing corresponding to the plurality of items based at least in part on the probability that the token is indicative of the quantity of the plurality of items included in the lot listing;
• and
• causing display of the single listing as the lot listing based at least in part on the classifying.

A non-transitory computer-readable medium comprising instructions which, when read by a machine, cause the machine to perform operations comprising:

• receiving an input comprising a string indicating a plurality of items are included in a single listing for publication by a publication system;
• parsing the string to identify a token that comprises a quantity word or a digit, the token being formed by:
• identifying the quantity word in the string;
• converting the identified quantity word into digit form;
• normalizing the string to lowercase characters;
• splitting the normalized string into a series of substrings using a sequence of delimiters;
• and
• splitting the series of substrings by separating a character from an adjacent character based on a difference between the character and the adjacent character;
• determining, by a machine learning model, a probability that the token is indicative of a quantity of the plurality of items;
• classifying the single listing as a lot listing corresponding to the plurality of items based at least in part on the probability that the token is indicative of the quantity of the plurality of items included in the lot listing;
• and
• causing display of the single listing as the lot listing based at least in part on the classifying.