Abstract: Methods and apparatus to facilitate annotation projects to extract structured information from free-form text using NLP techniques. Annotators explore text documents via automated preannotation functions, flexibly formulate annotation schemes and guidelines, annotate text, and adjust annotation labels, schemes and guidelines in real-time as a project evolves. NLP models are readily trained on iterative annotations of sample documents by domain experts in an active learning workflow. Trained models are then employed to automatically annotate a larger body of documents in a project dataset. Experts in a variety of domains can readily develop an annotation project for a specific use-case or business question. In one example, documents relating to the health care domain are effectively annotated and employed to train sophisticated NLP models that provide valuable insights regarding many facets of health care.

Abstract: Methods and apparatus to facilitate annotation projects to extract structured information from free-form text using NLP techniques. Annotators explore text documents via automated preannotation functions, flexibly formulate annotation schemes and guidelines, annotate text, and adjust annotation labels, schemes and guidelines in real-time as a project evolves. NLP models are readily trained on iterative annotations of sample documents by domain experts in an active learning workflow. Trained models are then employed to automatically annotate a larger body of documents in a project dataset. Experts in a variety of domains can readily develop an annotation project for a specific use-case or business question. In one example, documents relating to the health care domain are effectively annotated and employed to train sophisticated NLP models that provide valuable insights regarding many facets of health care.

Abstract: Methods and apparatus to facilitate annotation projects to extract structured information from free-form text using NLP techniques. Annotators explore text documents via automated preannotation functions, flexibly formulate annotation schemes and guidelines, annotate text, and adjust annotation labels, schemes and guidelines in real-time as a project evolves. NLP models are readily trained on iterative annotations of sample documents by domain experts in an active learning workflow. Trained models are then employed to automatically annotate a larger body of documents in a project dataset. Experts in a variety of domains can readily develop an annotation project for a specific use-case or business question. In one example, documents relating to the health care domain are effectively annotated and employed to train sophisticated NLP models that provide valuable insights regarding many facets of health care.

Abstract: Fenestration unit frames with integral mull posts and methods of making the same are described herein.

Abstract: A system eliminates alignment processing and performs TTS functionality using a new neural architecture. The neural architecture includes an encoder and a decoder. The encoder receives an input and encodes it into vectors. The encoder applies a sequence of transformations to the input and generates a vector representing the entire sentence. The decoder takes the encoding and outputs an audio file, which can include compressed audio frames.

Abstract: In various example embodiments, a system and methods are presented for generation and manipulation of predictive models within a user interface. The system and methods receive a view query with object data and time data and generate a user interface having a first graphical representation of a set of historical data responsive to the view query. The systems and methods generate a predictive model based on the set of historical data and generate a second graphical representation for the predictive model. The systems and methods generate and monitor a movable pivot element to automatically modify the predictive model and second graphical representation upon a change in position of the pivot element.

Abstract: Systems and methods to facilitate discounted volume purchasing of a product. A server system searches the internet to discover product information about a product. The server system generates a pack listing of product type, purchase quantity, and price per unit product based on the discovered product information, and posts the pack listing on a web site of the server system. The server system further facilitates the activation of the pack listing by a user of the web site to generate a non-public pack. The server system also facilitates the commitment of a supplier to the non-public pack and the transformation of the non-public pack to a public pack. The server system further facilitates the filling of the public pack via the web site and various aspects of purchasing via the pack once the pack is filled by the requisite committed purchase quantity.