Abstract: A method for automatically identifying a transactions table in a document includes obtaining a document object model of the document. The document object model includes a plurality of nodes and a plurality of edges, with each of the nodes corresponding to a respective element of the document. The method includes generating a plurality of hash values, with each of the plurality of hash values corresponding to a respective node of the plurality of nodes. The method includes determining the document includes a candidate table based on the plurality of hash values. The method includes generating a textual table based on the candidate table. The method includes analyzing one or more columns of the textual table to determine whether the textual table satisfies one or more criteria. The method includes determining the textual table is the transactions table based on determining the textual table satisfies the one or more criteria.

Abstract: Certain aspects of the disclosure provide a computer-implemented method for varying machine learning model output. The method includes receiving an input for a machine learning model system; selecting, based on one or more parameters associated with the input, a set of weights of a plurality of sets of weights; providing the input to a machine learning model associated with the set of weights; and obtaining output from the machine learning model based on the input.

Abstract: A method for automatically identifying a transactions table in a document includes obtaining a document object model of the document. The document object model includes a plurality of nodes and a plurality of edges, with each of the nodes corresponding to a respective element of the document. The method includes generating a plurality of hash values, with each of the plurality of hash values corresponding to a respective node of the plurality of nodes. The method includes determining the document includes a candidate table based on the plurality of hash values. The method includes generating a textual table based on the candidate table. The method includes analyzing one or more columns of the textual table to determine whether the textual table satisfies one or more criteria. The method includes determining the textual table is the transactions table based on determining the textual table satisfies the one or more criteria.

Abstract: An automatic technique is disclosed to enrich presented answers by highlighting relevant shopping recommendations. The shopping recommendations can either be highlighted within the answer itself, or as an auxiliary list of suggestions. A model is described for selecting phrases from the answer text (sequences of consecutive terms called noun phrases) that refer to potential products that likely represent relevant shopping recommendation in context of the question-answer pair. The noun phrases are then ranked in order of importance. The top-ranked noun phrases are used to search products to be displayed in association with the noun phrases. Clicking or tapping on a highlighted noun phrase launches a shopping-related flow, such as presenting a widget with product recommendations or running a search in a search engine.