Abstract: Methods and systems for embedding natural language sentences within a highly-dimensional vector space are provided. Additionally, various applications relating to natural language processing, are provided. Such applications include digital assistants and search engines, as well as systems for classifying, sorting, organizing, and/or pairing content that are associated with natural language objects. The sentence vector embeddings encode various semantic features of the sentence. Two separate language models, arranged in a serial architecture are employed to generate a sentence vector. The first language model generates token vectors for each of the tokens included in the sentence. The token vectors are employed as inputs to the second language model. The second language model generates the sentence vector for the sentence. A sentence vector embeds the semantic context of the corresponding natural language object within the vector space.

Abstract: A device implementing a system for expanded search includes a processor configured to identify plural words, and generate, for each word of the plural words, a word vector based on a proximity of the word relative to other words of the plural words, the word vector comprising plural dimensions. The processor is further configured to create a compressed word vector structure comprising clusters of subsets of the plural dimensions across the word vectors, each cluster including similar values of the respective dimensions, convert the word vectors to points on at least one plane, and partition the at least one plane into nested groupings of the points based on a threshold number of points per nested grouping. The processor is further configured to create a tree look-up structure of the nested groupings, and provide the compressed word vector structure and the tree look-up structure to a client device.

Abstract: A device implementing a system for expanded search includes a processor configured to identify plural words, and generate, for each word of the plural words, a word vector based on a proximity of the word relative to other words of the plural words, the word vector comprising plural dimensions. The processor is further configured to create a compressed word vector structure comprising clusters of subsets of the plural dimensions across the word vectors, each cluster including similar values of the respective dimensions, convert the word vectors to points on at least one plane, and partition the at least one plane into nested groupings of the points based on a threshold number of points per nested grouping. The processor is further configured to create a tree look-up structure of the nested groupings, and provide the compressed word vector structure and the tree look-up structure to a client device.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for enabling a user to quickly and easily filter and search a digital asset collection. The disclosed techniques allow for rapid recall of desired digital assets, linking assets into logical collections, and an overall improved user experience. The zero keyword/contextual keyword feature presents multimedia content icons and searchable keywords to allow a user to search the digital asset collection simply by tapping on one of these keywords. The top auto completion feature auto-completes suggestions in the search field based on various heuristics to ensure the method produces diverse and relevant results. The next keyword suggestion feature predicts a next search term based on learned properties about the digital asset collection. The semantical synonym and syntax synonym features expand an indexed vocabulary to allow broader searching.

Abstract: Systems and processes for operating an intelligent automated assistant to provide customized suggestions based on user-specific information. An example method includes, at an electronic device having one or more processors, obtaining impressions associated with at least one of the electronic device or additional electronic devices communicatively coupled to the electronic device; and determining one or more concepts based on the impressions. The method also includes generating, based on the one or more determined concepts, a representation of a collection of user-specific information; and providing one or more suggestions to a user based on the representation of the collection of user-specific information.