Aspects of this disclosure provide a device, system, and method for analyzing text. In an embodiment, a system is configured to convert characters of the text into a numerical time series signal. The numerical time series signal includes a time series conversion of the characters in numerical format. The system is further configured to generate a waveform with extracted information from the numerical time series signal. The extracted information having features based on politeness in language, a quantifiable use of punctuations, a quantifiable use of conjunctions, use of idioms, or a combination thereof. The system is additionally configured to determine whether the text is written by a specific user based on an analysis of the waveform against a threshold.

A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

A method, system, and medium for automated analysis of business intelligence each: receive natural language input from a user; evaluate, via a natural language understanding processor that includes a parser and an interpreter, the natural language input to determine an intent of the user; determine the intent of the user and generate a query based on a context manager; send an identification of the failure to a failure analysis system for human intervened analysis and refinement of a natural language model used by the natural language understand processor; assess, via a context manager processor, to determine a user interest in one or more portions of results of the query, a scrolling of the user through the results of the query; and refine, based on the user interest in the one or more portions of the results of the query, an output of the results of the query.

A predictor interactive learning system of the present invention includes machine learning unit configured to perform machine learning of a predictor that outputs a predicted value indicating a likelihood of being a predetermined intrinsic expression, by using teacher data and teacher labels, an interest score calculation unit configured tip obtain an interest score according to statistical data of a corresponding word in a corpus including the predicted value of the predictor for each of words of the corpus, an interactive learning frame unit configured to extract the word serving as the teacher data used in next learning of the predictor according to the interest score, and a question-response unit configured to output a question of whether the extracted teacher data is an intrinsic expression of which the likelihood is predicted by the predictor, and to acquire a teacher label corresponding to the teacher data, as a response to the question, in which the machine learning unit performs machine learning of the predictor using teacher data extracted by a teacher word extraction unit and a teacher label acquired by an interaction unit.

The present disclosure provides a method and apparatus for acquiring a pre-trained model, an electronic device and a storage medium, and relates to the field of artificial intelligence, such as the natural language processing field, the deep learning field, or the like. The method may include: adding, in a process of training a pre-trained model using training sentences, a learning objective corresponding to syntactic information for a self-attention module in the pre-trained model; and training the pre-trained model according to the defined learning objective. The solution of the present disclosure may improve a performance of the pre-trained model, and reduce consumption of computing resources, or the like.

A document execution engine receives a training set of data including training documents that each include one or more passages associated with a passage type and a level of risk. The document execution engine trains a machine learned model based on the training set. The trained machine learned model, when applied to subsequently identified passages within documents in the document execution environment, can identify a passage with above threshold levels of risk (e.g., a high-risk passage) based on a passage type of the passage. The trained machine learned model can then provide for display the high-risk passage and a related passage of the same passage type from a second document within the document execution environment to the user via a document passage comparison interface. Differences between the passages can be highlighted, enabling a user to quickly compare and contrast the passages.

A method and an apparatus for constructing an object relationship network and an electronic device are provided by the present disclosure, relating to the field of artificial intelligence technologies, such as deep neural networks, deep learning, etc. A specific implementation solution is: extracting keywords in respective text contents corresponding to a plurality of objects to obtain keywords corresponding to respective objects; and according to the keywords corresponding to the objects, a similarity between the plurality of objects is determined; and then according to the similarity between the plurality of objects, an object relationship network between the plurality of objects is constructed. Since the object relationship network constructed by means of the similarity between the plurality of objects can accurately describe a closeness degree of a relationship between the objects, thus, the plurality of objects can be managed effectively by means of the constructed object relationship network.

A system and method of creating an entity relationship map includes receiving a stream of lexical matter associated with one or more categories (302) and identifying one or more tokens from the received lexical matter based on the one or more categories (304). A frequency of one or more of unique lexical token and recurring lexical token are determined (306) and one or more outliers based on a standard deviation range associated with the at least one category is eliminated (308). Sentences with the one or more recurring lexical tokens are selected (310) to find one or more lexical neighbors and the entity relationship map is created based on an association between the unique lexical tokens and the at least one lexical neighbor (312).

A system and method of creating an entity relationship map includes receiving a stream of lexical matter associated with one or more categories (302) and identifying one or more tokens from the received lexical matter based on the one or more categories (304). A frequency of one or more of unique lexical token and recurring lexical token are determined (306) and one or more outliers based on a standard deviation range associated with the at least one category is eliminated (308). Sentences with the one or more recurring lexical tokens are selected (310) to find one or more lexical neighbors and the entity relationship map is created based on an association between the unique lexical tokens and the at least one lexical neighbor (312).