Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. An improved approach is provided for identifying log files that have undergone a change in status that would require retrieve of its log data, by including a module directly into the operating system that allows the log collection component to be reactively notified of any changes to pertinent log files.

A document of written content may be obtained. The document may be a candidate for inclusion in a corpus. A first entity associated with the document may be identified. A first discrete entity associated with the first entity may be identified. The relationship associated with the first entity and the first discrete entity may be analyzed. Based on the analyzing, a likelihood that the document contains content that would be detrimental for inclusion in the corpus may be determined.

A non-transitory computer-readable recording medium stores a response processing program for causing a computer to execute processing including: receiving a question from a user input to a terminal; extracting, in a case where a plurality of pieces of answer candidate data that corresponds to the received question is retrieved, keywords or key phrases from the plurality of pieces of answer candidate data; classifying the extracted keywords or key phrases on the basis of words included in the keywords or key phrases; and outputting a classification result of the keywords or key phrases to the terminal in a state selectable by a user, together with a response text to the question.

The present disclosure discloses a data mining method, a data mining apparatus, an electronic device and a storage medium, relates to the technical field of the Internet, and in particular to the Internet big data processing technology. The method includes: acquiring a current article to be mined; obtaining information values required for each data identification strategy of multiple data identification strategies from the current article, and each data identification strategy is used for identifying a preset type of data; identifying a data type of the current article according to the information values required for each data identification strategy to obtain a data type identification result; and determining whether the current article belongs to any preset type of data according to the data type identification result.

Technology is disclosed for detecting, classifying, and/or enforcing policies on social networking activity. The technology can store a content policy, classify content posted to a social network, determine that the classification corresponds to the content policy, and apply an action specified by the content policy to the posted content.

Embodiments of the present disclosure relate generally to organizing electronic text documents. In particular, one or more embodiments comprise a content management system that improves the organization of electronic text documents by intelligently and accurately categorizing electronic text documents by topic. The content management system organizes electronic text documents based on one or more topics, without the need for a human reviewer to manually classify each electronic text document, and without the need for training a classification algorithm based on a set of manually classified electronic text documents. Further, the content management system identifies and suggests topics for electronic text documents that relate to new or emerging topics.

Provided are systems and methods for mapping customer feedback to one or more processes. In one example, the method may include receiving text content that includes feedback, mapping, via execution of a mapping algorithm, the text content to a plurality of processes based on a synonym graph, generating mapping values for the plurality of processes, where a mapping value for a process is generated based on distance within the synonym graph of a mapping between a word in the text content and a word identifying the process, and determining a process among the plurality of processes that is most correlated to the feedback based on the generated mapping values.

The disclosure provides a text classification model training method, a text classification method, an apparatus, an electronic device, and a computer-readable storage medium, and relates to artificial intelligence technology. The text classification model training method includes: performing machine translation on a plurality of first text samples in a first language to obtain a plurality of second text samples in a second language different from the first language; training a first text classification model for the second language based on a plurality of third text samples in the second language and corresponding class labels; performing confidence-based filtering on the plurality of second text samples by the trained first text classification model; and training a second text classification model for the second language based on the filtered second text samples.

Systems and methods are disclosed for a system to provide an interface that is dynamic and that provides selectable links in response to a query for products in an electronic marketplace, where the selectable links are titled with the query and portions of reviews for products associated with the query. The system is configured to select feedback for items purchased from an electronic marketplace. Descriptors from the feedback are generated. In response to a query for the one or more of the items in the electronic marketplace, a determination is made that portions of the descriptors provide detail responsive to the query. An interface is displayed including selectable links titled with the query in combination with the portions of the descriptors. In response to selection of one of the selectable links, a portion of the items are displayed.

An information processing apparatus includes a processor. The processor receives an input of a graph structure. The graph structure has nodes including text and edge. The processor assigns the nodes to one or more clusters. The processor partitions the text into words. The processor classifies the words into 1) a word representing a subject or target of an operation, 2) a word representing a content or state of the operation, and 3) other words. The processor extracts a frequent word by counting a frequency of occurrence of one or more words classified as the words representing the subject or target of the operation and extracts a frequent word by counting a frequency of occurrence of one or more words classified as the words representing the content or state of the operation, for the respective clusters.