This application discloses a text mining method based on artificial intelligence performed by a computer device. This application includes: obtaining domain candidate term features corresponding to domain candidate terms; obtaining term quality scores corresponding to the domain candidate terms according to the domain candidate term features; determining a new term from the domain candidate terms according to the term quality scores corresponding to the domain candidate terms; obtaining an associated text according to the new term; and determining a domain seed term as a domain new term in response to determining according to the associated text that the domain seed term satisfies a domain new term mining condition. By this application, new terms can be automatically selected from domain candidate terms based on a machine learning algorithm, thereby reducing manpower costs and well adapting to the rapid emergence of special new terms in the Internet era.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and/or computing entities for displaying content found in technical documentation via an IETM viewer. In accordance with one embodiment, a method is provided comprising identifying textual content describing a part for the item, each instance of the part having a unique serial number, and obtaining a first serial number for the part using an application programming interface (API). The first serial number is predicted to belong to the first instance of the part. The method further comprises obtaining a plurality of other serial numbers for the part and displaying the textual content with indications of the first serial number and the plurality of other serial numbers for the part. The method further comprises, responsive to receiving user input indicating a selected serial number, causing the selected serial number to be associated with the particular object of the item.

The present disclosure provides a method and an apparatus of verifying information based on a voice interaction, a device, and a computer storage medium, and relates to a field of artificial intelligence technology. The present disclosure is implemented to include: acquiring a text of a voice response of a user to a voice inquiry, wherein the voice inquiry is provided for verifying information with the user; and inputting each character of the text of the voice response and a phonetic information associated with the each character to a pre-trained semantic analysis model so as to obtain an user intention information and/or an information of an object to be verified output by the pre-trained semantic analysis model, wherein the user intention information includes a confirmation, a denial, an answer, or a question.

A learning apparatus according to an embodiment has a feature generation means configured to take a search query, a first document related to the search query, and a second document that is not related to the search query as input, and generate a feature of the search query, a feature of the first document, and a feature of the second document, by using model parameters of a neural network, and an update means configured to take the feature of the search query, the feature of the first document, and the feature of the second document as input, and update the model parameters by using an error function including a cost function that is a differentiable approximation function of an L0 norm.

This invention relates to a computer-implemented method of (and a system for) searching clustered data (106), the clustered data (106) representing a multi-dimensional feature space and the method comprising the steps of: obtaining data representing a query feature vector (202) comprising a predetermined number of numerical feature values, projecting the query feature vector (202) into the clustered data (106) and obtaining a number of potential matches (203) determined to be within a pre-determined dimensional range of the query feature vector (202), determining data representing one or more score values for each of the potential matches (203), updating or re-calibrating the query feature vector (202), resulting in a modified query feature vector, in response to the determined one or more score values, projecting the modified query feature vector into the clustered data (106) and obtaining a number of potential matches (203) in response thereto, and repeating the steps of determining data representing one or more score values, updating or re-calibrating the query feature vector (202), and projecting the modified query feature vector into the clustered data (106) and obtaining a number of potential matches (203) in response thereto, until the obtained number of potential matches (203) are satisfactory according to one or more predetermined criteria and then providing the satisfactory potential matches (203) as a search result (206).

Systems, methods, devices, instructions, and other examples are described for natural language processing. One example includes accessing natural language processing general encoder data, where the encoder data is generated from a general-domain dataset that is not domain specific. A domain specific dataset is accessed and filtered encoder data using a subset of the encoder data is generated. The filtered encoder data is trained using the domain specific dataset to generate distilled encoder data, and tuning values for the distilled encoder data are generated to configure task outputs associated with the domain specific dataset.

A system for predicting and prescribing actions for impacting policymaking outcomes may include at least one processor configured to access first information scraped from the Internet to identify, for a particular pending policy, information about a plurality of policymakers slated to make a determination on the pending policy. The processor may parse the scraped first information to determine an initial prediction relating to an outcome of the pending policy. The processor may access second information to identify an action likely to change at least one of the initial prediction and the propensity of at least one policymaker, to thereby generate a subsequent prediction corresponding to an increase in a likelihood of achieving the desired outcome. The processor may display to the system user a recommendation to take the action in order to increase the likelihood of achieving the desired outcome.

Systems, methods, and computer-readable media are provided for determining a connection degree between users. Upon receiving user data, a report line hierarchy tree is updated, the hierarchy tree including user reporting information and relationships within an organization received from user profile data. A report line feature code is determined for each user by a feature code generator based on the report line hierarchy tree and stored in a data store. A location hierarchy tree is generated based on physical location information from the user profile data. A location feature code is determined for each user based on the location hierarchy tree and is also stored in the data store. To determine a connection degree between one or more users the feature codes including either or both of the report line feature codes and the location feature codes of the users are retrieved from the data store and compared.

A request is received from a user to search a document for at least one search term. The document is analyzed to determine contextual meanings associated with the at least one search term. An ordered set of contexts of the at least one search term is constructed. An option of disambiguation is then prompted to the user to select a context of the ordered set of contexts. Search results are presented to the user associated with the selected context of the at least one search term.

In the field of government engagement management, for users of an employee desktop web client, it is now possible, within the web client application, to search and read articles and/or knowledge content that has been authored to external locations. Due to this integration to external, third-party applications, content and/or articles can be displayed to an agent on the employee desktop web client graphical user interface. Agents can enter free text into a specific search field and review the results in summary form, and then select an article in HTML format to progress the current interaction with the client. An additional feature extending from this capability is to add an amount of coding to external knowledge content websites that are owned and/or operated by the owner of the system such that when the website is viewed through the third-party integration module, a button or icon appears within the website that when selected takes the agent to an appropriate form. This button or icon does not appear when the website is viewed outside of the system. This functionality adds value to the agent experience and enables the agent to provide an improved service to the end client. Results may be filtered by the search engine as well. Moreover, this system and method improves the operation of the computer in that the computer running such a system in the past was not able to integrate in such a fashion in a web client format. This system and method also enable an agent to handle calls with the web client more efficiently and allows agents on the web client to automatically classify.