Systems and methods for k-anonymizing a corpus of documents using linguistic similarities and embeddings distances between words. For instance, a word pair is selected based on linguistic similarity (e.g., belonging to the same part of speech) and small embeddings distance. For the selected word pair, a plurality of words is retrieved, also based on linguistic similarity to, and embeddings distances from, the selected word pair. Out of the plurality of words, a third word is identified that has a closer linguistic similarity to the word pair and also has smaller embeddings distances from the word pair. Each word in the word pair is then replaced by the third word. The process is repeated until k-anonymity is achieved.

Systems and methods for k-anonymizing a corpus of documents using linguistic similarities and embeddings distances between words. For instance, a word pair is selected based on linguistic similarity (e.g., belonging to the same part of speech) and small embeddings distance. For the selected word pair, a plurality of words is retrieved, also based on linguistic similarity to, and embeddings distances from, the selected word pair. Out of the plurality of words, a third word is identified that has a closer linguistic similarity to the word pair and also has smaller embeddings distances from the word pair. Each word in the word pair is then replaced by the third word. The process is repeated until k-anonymity is achieved.

Disclosed are a translation method, a classification model training method, a device and a storage medium, which relate to the field of computer technologies, particularly to the field of artificial intelligence such as natural language processing and deep learning. The translation method includes: obtaining a current processing unit of a source language text based on a segmented word in the source language text; determining a classification result of the current processing unit with a classification model; and in response to determining that the classification result is the current processing unit being translatable separately, translating the current processing unit to obtain translation result in a target language corresponding to the current processing unit.

Disclosed are a translation method, a classification model training method, a device and a storage medium, which relate to the field of computer technologies, particularly to the field of artificial intelligence such as natural language processing and deep learning. The translation method includes: obtaining a current processing unit of a source language text based on a segmented word in the source language text; determining a classification result of the current processing unit with a classification model; and in response to determining that the classification result is the current processing unit being translatable separately, translating the current processing unit to obtain translation result in a target language corresponding to the current processing unit.

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.

Methods to reduce agent effort and improve customer experience quality through artificial intelligence. The Agent Assist tool provides contact centers with an innovative tool designed to reduce agent effort, improve quality and reduce costs by minimizing search and data entry tasks The Agent Assist tool is natively built and fully unified within the agent interface while keeping all data internally protected from third-party sharing.

Methods to reduce agent effort and improve customer experience quality through artificial intelligence. The Agent Assist tool provides contact centers with an innovative tool designed to reduce agent effort, improve quality and reduce costs by minimizing search and data entry tasks The Agent Assist tool is natively built and fully unified within the agent interface while keeping all data internally protected from third-party sharing.

According to one embodiment, an information processing apparatus includes a processor. The processor generates a template, regarding a recording data sheet including a plurality of items, for one or more of the items that can be specified, with reference to an input order of input target items selected from the items. The processor performs a speech recognition on an utterance of a user and generate a speech recognition result. The processor determines an input target range relating to one more items specified by the utterance of the user among the items based on the template and the speech recognition result.

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.

Systems and methods for natural language processing are described. One or more embodiments of the present disclosure receive a span of text comprising an offensive span and a non-offensive span, generate a contextualized word embedding for each of a plurality of words of the span of text, generate a refined vector representation for each of the plurality of words based on the corresponding contextualized word embedding using a refinement network trained for offensive text recognition, generate label information for each of the plurality of words based on the corresponding refined vector representation, wherein the label information indicates whether each of the plurality of words includes offensive text, and transmit an indication of a location of the offensive span based on the label information.