Entity linking and filtering using efficient search tree and machine learning representations
Methods, systems, and computer-readable storage media for a ML system that reduces a number of target items from consideration as potential matches to a query item using token embeddings and a search tree.
1 . A computer-implemented method for matching a query item to one or more target items using machine learning (ML) models, the method being executed by one or more processors and comprising:
prior to matching a query item to one or more target items of a superset of target items during inference, providing a set of target items from the superset of target items by:
for a first query item token of the query item:
identifying, within a search space, at least one target item token that is similar to the first query item token, and
associating the first query item token with a revised search space within a tracker, the tracker comprising an array data structure that is initialized with a set of null values, associating the first query item token with the revised search space within the tracker comprises replacing a null value with a search space index indicating where the first query item token was found in the search space, and wherein the revised search space is provided in a queue of search spaces, a length of the queue being defined by a window parameter;
determining, based on the length between the items being within a window size of the window parameter, a set of matched item tokens indicating one of a match and a partial match between a query item token and a target item token;
defining the set of target items from the set of matched item tokens; and
executing inference to match the query item to one or more target items in the set of target items to provide inference results, the inference results indicating a match between the query item and at least one target item in the set of target items.
2 . The method of claim 1 , wherein the search space is defined within a search tree comprising a set of nodes, each node representative of a respective target item token in a set of target item tokens.
3 . The method of claim 1 , wherein the revised search space comprises a search sub-space of the search space.
4 . The method of claim 1 , further comprising determining that no target item tokens represented in the revised search space is similar to a second query item token, and in response, comparing a second query item token embedding to target item token embeddings of target items tokens included within an alternative search space present in the queue.
5 . The method of claim 4 , further comprising, for the second query item token comparing a second query item token embedding to target item token embeddings of target items tokens included within the revised search space.
6 . The method of claim 1 , wherein a first query item token embedding is determined by a first ML model, and the at least one target item token that is similar to the first query item token is identified by comparing the first query item token embedding to target item token embeddings of target items tokens included within the search space.
7 . The method of claim 6 , wherein, during a training phase, the first ML model is fine-tuned based on sets of perturbations, each set of perturbations corresponding to an item token.
8 . The method of claim 1 , wherein executing inference to match the query item to one or more target items in the set of target items to provide inference results comprises processing the query item and target items of the set of target items through a second ML model that outputs the inference results.
9 . The method of claim 1 , wherein a number of target items in the set of target items being less than a number of target items in the superset of target items.