Abstract: Embodiments of the present disclosure relate generally to semantic indexing to improve search results of a large corpus. Some embodiments identify one or more closest matches between (i) a search semantic vector that corresponds to a search query, the search semantic vector based on a first machine-learned model that projects the search query into a semantic vector space, and (ii) a plurality of publication vectors corresponding to respective publications in the publication corpus, the plurality of publication vectors based on a second machine-learned model that projects the plurality of publication vectors into the semantic vector space.

Abstract: Embodiments of the present disclosure relate generally to index trimming to improve search results of a large corpus. Some embodiments, prior to receiving, from a user device, a search query of one or more keywords searching for a relevant set of publications in a publication corpus, trim candidate publications from a plurality of candidate publications to generate a trimmed plurality of candidate publications.

Abstract: Embodiments of the present disclosure relate generally to semantic indexing to improve search results of a large corpus. Some embodiments, with at least one of the keywords of the search query encoded by a semantic vector in a semantic vector space, identify a plurality of candidate publications in the publication corpus, the plurality of candidate publications encoded by a cluster of a plurality of semantic vectors in the semantic vector space, the identifying based on proximity in the semantic vector space between the at least one of the keywords of the search query and keywords in the plurality of candidate publications, the proximity based on a first machine-learned model that projects the at least one keyword in the search query and the keywords in the plurality of candidate publications into the semantic vector space.

Abstract: Embodiments of the present disclosure relate generally to index trimming to improve search results of a large corpus. Some embodiments, prior to receiving, from a user device, a search query of one or more keywords searching for a relevant set of publications in a publication corpus, trim candidate publications from a plurality of candidate publications to generate a trimmed plurality of candidate publications.

Abstract: Embodiments of the present disclosure relate generally to semantic indexing to improve search results of a large corpus. Some embodiments identify one or more closest matches between (i) a search semantic vector that corresponds to a search query, the search semantic vector based on a first machine-learned model that projects the search query into a semantic vector space, and (ii) a plurality of publication vectors corresponding to respective publications in the publication corpus, the plurality of publication vectors based on a second machine-learned model that projects the plurality of publication vectors into the semantic vector space.

Abstract: Embodiments of the present disclosure relate generally to indexing with multiple algorithms to improve search results of a large corpus.

Abstract: Embodiments of the present disclosure relate generally to semantic indexing to improve search results of a large corpus. Some embodiments, with at least one of the keywords of the search query encoded by a semantic vector in a semantic vector space, identify a plurality of candidate publications in the publication corpus, the plurality of candidate publications encoded by a cluster of a plurality of semantic vectors in the semantic vector space, the identifying based on proximity in the semantic vector space between the at least one of the keywords of the search query and keywords in the plurality of candidate publications, the proximity based on a first machine-learned model that projects the at least one keyword in the search query and the keywords in the plurality of candidate publications into the semantic vector space.