Abstract: Various embodiments of the present disclosure provide model-based domain-aware autocomplete techniques for generating autocomplete suggestions in a complex search domain. Example embodiments are configured to generate, using a domain-aware autocomplete model, a label for an autocomplete suggestion based on a set of keywords within an autocomplete suggestion training dataset associated with a target domain source. Example embodiments are also configured to generate, using a weak-labeling model, an updated label for the autocomplete suggestion by decorrelating the set of keywords from the label. Example embodiments are also configured to generate, using a sentence classification model, a category for the autocomplete suggestion based on the updated label. Example embodiments are also configured to, using the domain-aware autocomplete model, generate a suggestion-category pair (SCP) based on the autocomplete suggestion and the category for the autocomplete suggestion.

Abstract: Various embodiments of the present disclosure provide an interactive map-based visualization system related to multi-channel search for search domains to improve upon traditional search resolutions within such domains. The techniques may include receiving a user interface request that comprises (i) character-level text input related to a search query via a user interface of a user device and (ii) filter metadata for a user identifier associated with the user interface request, generating a set of query result data objects for the user interface request by correlating the character-level text input to at least one domain knowledge profile, and generating a set of filtered query result data objects for the user interface request by filtering the set of query result data objects using the filter metadata. In some examples, the techniques may include initiating a rendering of a set of selectable graphical element options that are correlated to a real-time map visualization.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for ranking entities provided in response to a search query by identifying one or more categorical identifiers based on a semantic similarity between a query embedding and a plurality of categorical description embeddings, generating a predicted distance preference for the search query based on a location associated with a querying user, identifying one or more entities based on the location associated with the querying user, the predicted distance preference, and entity activity data entries comprising a plurality of categorical descriptions matching the identified one or more categorical identifiers.

Abstract: Various embodiments of the present disclosure provide computer interpretation techniques for implementing a query resolution process to improve upon traditional search resolutions within a search domain. The techniques may include receiving a plurality of interaction data objects comprising a plurality of assessment codes and a plurality of intervention codes. The techniques may include generating a frequency distribution comprising a plurality of code pairs based on a plurality of cooccurrences of the plurality of assessment codes and the plurality of intervention codes within the plurality of interaction data objects. The techniques may include generating, using the frequency distribution, a cross-code dataset comprising one or more mapped code pairs from the plurality of code pairs based on a threshold cooccurrence value. The techniques may include initiating the performance of a query resolution operation for a search query based on the cross-code dataset.

Abstract: Various embodiments of the present disclosure provide model-based domain-aware autocomplete techniques for generating autocomplete suggestions in a complex search domain. Example embodiments are configured to generate, using a domain-aware autocomplete model, a label for an autocomplete suggestion based on a set of keywords within an autocomplete suggestion training dataset associated with a target domain source. Example embodiments are also configured to generate, using a weak-labeling model, an updated label for the autocomplete suggestion by decorrelating the set of keywords from the label. Example embodiments are also configured to generate, using a sentence classification model, a category for the autocomplete suggestion based on the updated label. Example embodiments are also configured to, using the domain-aware autocomplete model, generate a suggestion-category pair (SCP) based on the autocomplete suggestion and the category for the autocomplete suggestion.

Abstract: Various embodiments of the present disclosure provide query processing techniques for resolving queries in a complex search domain to improve upon traditional search resolutions within such domains. The techniques may include generating a keyword and an embedding representation for an agnostic search query. The keyword representation may be compared against source text attributes within one or more domain channels to generate a plurality of keyword similarity scores between the search query and features within a search domain. The embedding representation may be compared against source embedding attributes within the one or more domain channels to generate a plurality of embedding similarity scores between the search query and the features within the search domain. The keyword and embedding similarity scores may be aggregated to generate aggregated similarity scores for identifying an intermediate query resolution for the search query. The intermediate query resolution may be leveraged to resolve the query.

Abstract: Various embodiments of the present disclosure provide model-based domain-aware autocomplete techniques for generating autocomplete suggestions in a complex search domain. Example embodiments are configured to generate, using a domain-aware autocomplete model, a label for an autocomplete suggestion based on a set of keywords within an autocomplete suggestion training dataset associated with a target domain source. Example embodiments are also configured to generate, using a weak-labeling model, an updated label for the autocomplete suggestion by decorrelating the set of keywords from the label. Example embodiments are also configured to generate, using a sentence classification model, a category for the autocomplete suggestion based on the updated label. Example embodiments are also configured to, using the domain-aware autocomplete model, generate a suggestion-category pair (SCP) based on the autocomplete suggestion and the category for the autocomplete suggestion.

Abstract: Various embodiments of the present disclosure provide computer interpretation techniques for implementing a query resolution process to improve upon traditional search resolutions within a search domain. The techniques may include generating a plurality of interaction embeddings for a plurality of textual descriptions corresponding to a plurality of interaction codes identified within an interaction dataset and a plurality of taxonomy embeddings for a plurality of taxonomy categories identified within a taxonomy dataset. The techniques may include generating similarity scores for a plurality of description-category pairs based on a comparison between the plurality of interaction embeddings and the plurality of taxonomy embeddings.

Abstract: A system and method for predicting risk of violence for an individual (primarily school violence, but not limited to school violence) performs the following steps: (a) receiving responses to questions from an individual; (b) extracting by a computerized annotator words or phrases from the questions and responses; (c) assigning by the annotator extracted word(s) or phrase(s) to at least one of a plurality of pre-defined categories; and (d) automatically identifying and scoring words or phrases that could be classified into the pre-defined categories by a trained machine-learning engine to produce a score reflecting relative risk of violence by the individual. The pre-defined categories include: expression of violent acts or thoughts of the individual; expression of negative feelings, thoughts or acts of others; expression of negative feelings, thoughts or acts of the individual; expression of family discord or tragedies; and expression of protective factors.

Abstract: A system and method for predicting risk of violence for an individual (primarily school violence, but not limited to school violence) performs the following steps: (a) receiving responses to questions from an individual; (b) extracting by a computerized annotator words or phrases from the questions and responses; (c) assigning by the annotator extracted word(s) or phrase(s) to at least one of a plurality of pre-defined categories; and (d) automatically identifying and scoring words or phrases that could be classified into the pre-defined categories by a trained machine-learning engine to produce a score reflecting relative risk of violence by the individual. The pre-defined categories include: expression of violent acts or thoughts of the individual; expression of negative feelings, thoughts or acts of others; expression of negative feelings, thoughts or acts of the individual; expression of family discord or tragedies; and expression of protective factors.

Abstract: A system and method for predicting risk of violence for an individual (primarily school violence, but not limited to school violence) performs the following steps: (a) receiving responses to questions from an individual; (b) extracting by a computerized annotator words or phrases from the questions and responses; (c) assigning by the annotator extracted word(s) or phrase(s) to at least one of a plurality of pre-defined categories; and (d) automatically identifying and scoring words or phrases that could be classified into the pre-defined categories by a trained machine-learning engine to produce a score reflecting relative risk of violence by the individual. The pre-defined categories include: expression of violent acts or thoughts of the individual; expression of negative feelings, thoughts or acts of others; expression of negative feelings, thoughts or acts of the individual; expression of family discord or tragedies; and expression of protective factors.