Abstract: Disclosed herein relates to a method that analyzes the sentiment of user feedback for a genealogical system and identifies key phrases that may relate to novel themes in the user feedback. Sentiment analysis and novel theme prediction systems, methods, and computer-program products are described. Sentiment analysis of user feedback may include dividing user-generated unstructured text files into sections. The method classifies each section to an aspect of the genealogical system from a predetermined list of aspects monitored by the genealogical system. The method inputs the text belonging to the classified section to a supervised machine learning model and determines a sentiment associated with the classified section. In other embodiments, a method generates embedding vectors representing survey responses from users of a genealogical system. The method extracts a subset of survey responses having embedding vectors grouped into one cluster. The method extracts key phrases that may indicate a novel theme.

Abstract: Systems and methods for importing documents are described. An input image is received and preprocessed. OCR and/or page segmentation and chapter detection are performed. Special-case processing is performed for lists, tables, free text, and other categories. Anaphora analysis, stemming, lemmatization, and relationship detection are performed. A genealogical tree is generated, augmented, or merged based on the extracted entities and relationships.

Abstract: Hybrid machine-learning systems and methods can be used to perform automatic keyphrase extraction from input text, such as historical records. For example, a computer-implemented method for extracting keyphrases from input text can include receiving input text having a plurality of words and identifying a set of candidate phrases from the plurality of words and a score for each of the candidate phrases using one or more unsupervised machine-learning models. The method can also include identifying named entities from the set of candidate phrases using one or more supervised machine-learning models and determining an updated set of scores for at least some of the candidate phrases within the set based on the named entities identified using the supervised machine-learning model. The method can also include identifying a keyphrase from the set of candidate phrases based on the updated set of scores.

Abstract: A search system performs a federated search across multiple databases and generates a ranked combined list of found genealogical records. The system receives a user query with one or more specified characteristics. The system may determine expanded characteristics derived from the specified characteristics. The system searches the various databases with the characteristics retrieving records according to the characteristics. The system combines the retrieved records and ranks them using a machine learning model. The machine learning model is configured to assign a weight to the records returned from each of the genealogical databases based on the characteristics specified in the user query. The machine learning model may be trained by any combination of one or more of: a Nelder-Mead method, a coordinate ascent method, and a simulated annealing method. The ranked combined results are provided in response to the user query.

Abstract: Described herein are systems, methods, and other techniques for extracting one or more keyphrases from an input text. The input text may include a plurality of words. A plurality of token-level attention matrices may be generated using a transformer-based machine learning model. The plurality of token-level attention matrices may be converted into a plurality of word-level attention matrices. A set of candidate phrases may be identified from the plurality of words based on the plurality of word-level attention matrices. The one or more keyphrases may be selected from the set of candidate phrases.

Abstract: Described herein are systems, methods, and other techniques for evaluating a classifier model. The classifier model may be provided with a set of elements to be classified into N classes. Classification results may be obtained from the classifier model. N class-specific precisions and N class-specific recalls for the N classes may be computed based on the classification results. N class-specific precision weights and N class-specific recall weights corresponding to the N classes may be obtained. A weighted f-measure may be computed by weighting the N class-specific precisions with the N class-specific precision weights and weighting the N class-specific recalls with the N class-specific recall weights.

Abstract: A search system performs a federated search across multiple databases and generates a ranked combined list of found genealogical records. The system receives a user query with one or more specified characteristics. The system may determine expanded characteristics derived from the specified characteristics. The system searches the various databases with the characteristics retrieving records according to the characteristics. The system combines the retrieved records and ranks them using a machine learning model. The machine learning model is configured to assign a weight to the records returned from each of the genealogical databases based on the characteristics specified in the user query. The machine learning model may be trained by any combination of one or more of: a Nelder-Mead method, a coordinate ascent method, and a simulated annealing method. The ranked combined results are provided in response to the user query.