Systems and methods are described for training an artificial intelligence model to infer a log sourcetype of a log. For example, logs may have different log sourcetypes, and logs having the same log sourcetypes may have different messagetypes. The artificial intelligence model may be a machine learning model, and can be trained using training data that includes logs with known log sourcetypes. Each log can be tokenized, filtered, converted into a vector, and applied to a machine learning model as an input to perform the training. The machine learning model may output an inferred log sourcetype, which can be compared with the known log sourcetype to update model parameters to improve the machine learning model accuracy. The trained machine learning model may be trained to infer a log sourcetype of a log regardless of the messagetype of the log.

Systems and methods are described for training an artificial intelligence model to infer a log sourcetype of a log. For example, logs may have different log sourcetypes, and logs having the same log sourcetypes may have different messagetypes. The artificial intelligence model may be a machine learning model, and can be trained using training data that includes logs with known log sourcetypes. Each log can be tokenized, filtered, converted into a vector, and applied to a machine learning model as an input to perform the training. The machine learning model may output an inferred log sourcetype, which can be compared with the known log sourcetype to update model parameters to improve the machine learning model accuracy. The trained machine learning model may be trained to infer a log sourcetype of a log regardless of the messagetype of the log.

A method and apparatus for processing a natural language text, a device and a storage medium are provided. An implementation of the method includes: after obtaining a target sentence text to be processed, performing word segmentation on the target sentence text, to obtain a target fixed word slot corresponding to the target sentence text and candidate free word slots corresponding to the target sentence text; then performing, based on syntax rules of preset standard sentence patterns, sentence pattern matching on the target fixed word slot and the candidate free word slots, to obtain a target sentence pattern including the target fixed word slot and a target free word slot; and replacing the target free word slot in the target sentence pattern with a free word corresponding to the target free word slot in the target sentence text, to obtain a target sentence pattern including the free word.

A method and apparatus for processing a natural language text, a device and a storage medium are provided. An implementation of the method includes: after obtaining a target sentence text to be processed, performing word segmentation on the target sentence text, to obtain a target fixed word slot corresponding to the target sentence text and candidate free word slots corresponding to the target sentence text; then performing, based on syntax rules of preset standard sentence patterns, sentence pattern matching on the target fixed word slot and the candidate free word slots, to obtain a target sentence pattern including the target fixed word slot and a target free word slot; and replacing the target free word slot in the target sentence pattern with a free word corresponding to the target free word slot in the target sentence text, to obtain a target sentence pattern including the free word.

A system, method, apparatus, and computer program product that scans a document to locate potentially significant terms, such as loopholes, legal clauses, as well as other potential harmful language and identifies the significant term to the user for further review. The invention may also provide access resources such as legal resources for the user to utilize. The invention can also produce document scanning software for other professional and technical fields to assess and highlight significant terms. An artificial intelligence (AI) component parses the document and analyze the document to identify one or more significant terms within a corpus of the document, determine a credibility score for the document, and gather and annotate the document with supplemental content related to each of the one or more significant terms.

Provided is an information search system that enables a searcher to efficiently find information they want to know, the system including: a database (12) that stores a plurality of pieces of information that are text-searchable; a query sentence acceptance unit (26) that accepts a query sentence in a natural language format; an inputted search keyword extractor (44) that extracts an inputted search keyword from the query sentence; a retrieval executor (40) that executes retrieval processing from the database using the inputted search keyword, along with a keyword relevant to the inputted search keyword; and a keyword dictionary (30) in which words associated with categories are registered, wherein the retrieval executor acquires, from the keyword dictionary, words associated with one of the categories selected by a searcher, re-sorts information retrieved as a result of the retrieval processing, based on the acquired words, and displays the information to the searcher.

Embodiments of the present disclosure relate to a dialogue interaction method and apparatus, a device and a storage medium, and relate to the field of artificial intelligence technology. The method may include: determining a first semantic encoding of received user information according to a sentence tree; determining a second semantic encoding for responding to the user information from a dialogue tree according to the first semantic encoding, the sentence tree and the dialogue tree being trained and obtained through sentence node information and/or word node information in a logical brain map sample; and determining a target response sentence of the second semantic encoding from the sentence tree, to be used for a dialogue with a user.

Embodiments of the present disclosure relate to a dialogue interaction method and apparatus, a device and a storage medium, and relate to the field of artificial intelligence technology. The method may include: determining a first semantic encoding of received user information according to a sentence tree; determining a second semantic encoding for responding to the user information from a dialogue tree according to the first semantic encoding, the sentence tree and the dialogue tree being trained and obtained through sentence node information and/or word node information in a logical brain map sample; and determining a target response sentence of the second semantic encoding from the sentence tree, to be used for a dialogue with a user.

A character input device according to one or more embodiments may include: an output unit configured to output a first character string to an application program having a suggestion function; a detector configured to detect selection of a second character string that is presented in correspondence with the first character string by the application program; and a registration unit configured to register, in a dictionary database, the second character string that is detected, by the detector, to have been selected.

A character input device according to one or more embodiments may include: an output unit configured to output a first character string to an application program having a suggestion function; a detector configured to detect selection of a second character string that is presented in correspondence with the first character string by the application program; and a registration unit configured to register, in a dictionary database, the second character string that is detected, by the detector, to have been selected.