| CPC G06N 3/08 (2013.01) [G06F 16/93 (2019.01); G06N 3/045 (2023.01); G06N 3/063 (2013.01)] | 18 Claims |
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1. A computer-implemented method for tagging sequences of items, comprising:
obtaining an original sequence of items from at least one source of original information;
obtaining supplemental information pertaining to the original sequence of items from a search system, the search system including matching logic that maps the original sequence of items to the supplemental information;
appending the supplemental information to the original sequence of items, with a separator token therebetween, to produce a supplemented sequence of items;
mapping the supplemented sequence of items into hidden state information using an encoder machine-trained model;
processing the hidden state information with a particular post-processing machine-trained model, to produce a tagged output sequence of items; and
providing output information that is based on the output sequence of items,
the encoder machine-trained model and the particular post-processing machine-trained model having been trained in a prior training process,
the prior training process including:
obtaining plural sets of training examples, the plural sets of training examples being generated based on plural respective data sets;
selecting a training example from a chosen set of training examples, the training example including: a supplemented sequence of items that includes an original sequence of items having text combined with supplemental information obtained from the search system; and labels that identify respective entity classes of the items in the original sequence of items of the training example,
the supplemental information associated with the training example being obtained by: obtaining search results generated by the search system for the original sequence of items having text in the training example, the search results including a set of matching-document digests that describe documents that match the original sequence of items having text, as determined by the search system; and selecting one or more supplemental items from the search results;
mapping the supplemented sequence of items of the training example into hidden state information using the encoder machine-trained model;
processing the hidden state information of the training example with a selected post-processing machine-trained model, to produce a tagged output sequence of items for the training example, each particular item in the tagged output sequence of items for the training example having a tag that identifies a class of entity to which the particular item pertains, the selected post-processing machine-trained model being selected from among plural post-processing machine-trained models, the plural post-processing machine-trained models being trained using the plural respective sets of training examples;
adjusting weights of the encoder machine-trained model and the selected post-processing machine-trained model based on a comparison between tags in the tagged output sequence of items associated with the training example and the labels of the training example; and
repeating said training process until a training objective is achieved.
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