Embodiments relate to automatically providing textual context for source strings in a source language that are to be translated by a human translator to target strings in a target language. The source strings are compared against a dictionary of reference strings in the source language. For each source string, one or more of the reference strings that are most relevant, similar, etc., are selected. When a human translator is to translate the source strings, the selected reference strings are presented; each source string has one or more similar/related strings displayable in association therewith. For a given source string, the human translator can use the associated reference strings as a form of context to help estimate the intended meaning of the given source string when translating the given source string to a target string in the target language.

A natural language processing (“NLP”) manager is provided that manages NLP model training. An unlabeled corpus of multilingual documents is provided that span a plurality of target languages. A multilingual embedding is trained on the corpus of multilingual documents as input training data, the multilingual embedding being generalized across the target languages by modifying the input training data and/or transforming multilingual dictionaries into constraints in an underlying optimization problem. An NLP model is trained on training data for a first language of the target languages, using word embeddings of the trained multilingual embedding as features. The trained NLP model is applied for data from a second of the target languages, the first and second languages being different.

Computer system, methods, mobile app, and media to guarantee the quality of a language translation of content using a computer network of translators and reviewers communicating in real-time. The accuracy, and hence quality, of the translation is ensured by incorporating “real-time” quality checks comprising randomly inserted errors into an original human translation and evaluating if a reviewer detects the errors. By using a plurality of reviewers for grading each translation, while detecting and eliminating reviewers that are not competent, the quality of a translation is guaranteed. The level of quality is also controlled by increasing (higher quality) and decreasing (lower quality) the type and amount of errors to be detected by the reviewers. Therefore, a computerized system is able to guarantee the quality of a translation into any language and without knowing that language, or the translators who translate it, or the reviewers who grade the translators.

An information processing device according to an embodiment includes a keyword extracting unit, a tag generating unit and a UI control unit. The keyword extracting unit extracts a keyword from time-series texts within a time range set by a user. The tag generating unit generates a tag corresponding to a time period from a first appearing time until a last appearing time of a same keyword appearing plural times within a duration set according to the time range. The UI control unit creates a UI screen including a first display area in which a time axis corresponding to the time range is displayed and a second display area in which the tag is displayed while causing the tag to correspond to the time period on the time axis, and resets, by selecting the tag, a time period of the selected tag in the time range to update the UI screen.

Systems and methods may provide proactive news analytics based on integrated prediction statements. Users may extract and collect conditional statements from research reports. A compiled list based on the processing/linking of statements for signal generation may then be created. Similarly, a list of counter statements that are assigned a conflict rating that specifies how much agreement/disagreement on a specific topic exists for the counter statement and the statement itself may be created. The custom library may have semantic capabilities to justify conditional statements in order to capture meaning and identify supporting news related to the statement. When a relevant event is detected that relates to a conditional statement, expected conclusions are linked, and customized indexes are calculated to allow for analysis of the relevant event.

An information processing apparatus includes an input unit, a display unit, a designation unit and an update unit. The input unit inputs a plurality of parts configuring one character. The display unit displays the plurality of parts. The designation unit designates one or more parts selected from the plurality of parts by a user. The update unit updates a display on the display unit such that the designated one or more parts are deleted from the one character and the other parts are remained displayed on the display unit.

Deep learning is used to identify specific, potential entertainment risks to an enterprise while such risks before the enterprise commits large sums of money to a project. The system involves mining and using existing classifications of data (e.g., from a database of previously successful book and film franchises) to train one or more deep learning algorithms, and then examining a proposed entertainment document with the trained algorithm, to generate a scored output that will enable enterprise personnel to be alerted to risks and take action in time to prevent the risks from resulting in harm to the enterprise.

Deep learning is used to identify specific, potential risks of missed diagnosis for a patient and reporting the risk to healthcare provider. The system involves mining and using existing electronic health records for specific medical diagnosis to train one or more deep learning algorithms, and then examining the internal electronic health record of the patient with the trained algorithm, to generate a scored output that will enable a healthcare provider to be alerted to potential risks of a missed diagnosis.

A non-transitory computer readable medium stores a program causing a computer to execute a process for displaying text. The process includes displaying in association with each other a text region extracted from image information and including an image of a text, an original text that is obtained by performing character recognition on the image of the text included in the text region, and a translation text into which the original text is translated.

Systems and methods for communicating in a network using share signals in accordance with various embodiments of the present disclosure are provided. In one embodiment, a method for communicating in a network may include receiving, from a first client device, a share signal including first image data captured by a camera of a first audio/video (A/V) recording and communication device and a command to share the first image data with a network of users; processing the share signal by comparing the first image data to second image data captured by a camera of a second A/V recording and communication device; and generating and transmitting an alert to a second client device associated with the second A/V recording and communication device when comparison of the first image data with the second image data indicates a person of interest is depicted in both the first image data and the second image data.