Abstract:
The disclosure is directed at a method and system for improved search functionality. By using natural language processing (NLP), individual words in a search term may be parsed and then individually stored. The relationships between the individual words of the search term may also be stored such that when the search is repeated, the previous results may be retrieved and, if not, a more accurate search may be performed.
Abstract:
A task assistant executed by a processing device receives a copy of a first correspondence sent by a first user in a first communication thread, wherein the first correspondence is addressed to a second user and the copy is sent to an automated task assistant. The task assistant determines that the first correspondence includes a request and a due date and monitors additional correspondence in the first communication thread to determine whether the request has been satisfied by the due date. Responsive to determining that the request has not been satisfied, the task assistant provides a notification with a reminder of the request to a client device associated with the second user in advance of the due date.
Abstract:
Analyzing textual data is disclosed, including by: receiving textual data; determining that the textual data is a candidate for analogy analysis based at least in part on at least a portion of the textual data matching an analogical question template; extracting a source substantive from the textual data; using the source substantive to determine a target substantive from a word vector model that is trained on a set of training data; and generating an answer including the target substantive based at least in part on an analogical answer template corresponding to the analogical question template.
Abstract:
A computerized method of automated patient chart review includes receiving a selection of a particular patient, automatically parsing at least one document of a patient's medical record having structured data and natural language data, automatically generating a list of variables from the patient's medical record, automatically generating a list of important variables from the list of variables associated with a specific clinical event from the structured data and natural language data. Predictive modeling and artificial intelligence are used to analyze the patient data, reviewer actions, and reviewer feedback data.
Abstract:
A method and a language processing and knowledge building system (LPKBS) for processing textual data, receives textual data and a language object; segments the textual data into sentences and each sentence into words; generates a list of one or more natural language phrase objects (NLPOs) for each word by identifying vocabulary classes and vocabulary class features for each word based on vocabulary class feature differentiators; creates sentence phrase lists, each including a combination of one NLPO selected per word from each list of NLPOs; groups two or more NLPOs in each sentence phrase list based on word to word association rules, the vocabulary classes, the vocabulary class features, and a position of each NLPO; replaces each such group of NLPOs with a consolidated NLPO; maps each segmented sentence to a sentence type; identifies a semantic item for each mapped NLPO; and identifies and stores associated attributes and relations.
Abstract:
An offline semantic processor of a resource-constrained voice-enabled device such as a mobile device utilizes an offline grammar model with reduced resource requirements to parse voice-based queries received by the device. The offline grammar model may be generated from a larger and more comprehensive grammar model used by an online voice-based query processor, and the generation of the offline grammar model may be based upon query usage data collected from one or more users to enable a subset of more popular voice-based queries from the online grammar model to be incorporated into the offline grammar model. In addition, such a device may collect query usage data and upload such data to an online service to enable an updated offline grammar model to be generated and downloaded back to the device and thereby enable a dynamic update of the offline grammar model to be performed.
Abstract:
Systems, apparatuses, and methods for generating a parser training set and ultimately a correct treebank for a corpus of text, based on using an existing parser that was trained on a different corpus. Also disclosed are systems, apparatuses, and methods for improving the operation of a parser in the case of using a less familiar set of training data than is typically used to train conventional parsers. This can be used to generate a more effective and accurate parser for a new corpus (and hence more accurate parse trees) with significantly less effort than would be required if it was necessary to generate a standard size training set.
Abstract:
La présente invention concerne le domaine de la compréhension sémantique par ordinateur. Plus précisément elle concerne un procédé d'analyse sémantique d'un texte en langage naturel par des moyens de traitement de données, en vue de sa classification.