A method includes translating a source to generate a translated source, extracting a set of terms from one of the source and the translated source comprising at least a first term and a second teim related to the first term, comparing the extracted set of terms with at least one translation pair, and determining a correct translation based on the comparison.

The present invention is a system and method for generating narrative text utilizing data input from one or more data sources to drive the creation of a narrative text output. Narrative text is generated in accordance with sets of data that provide the scope of text to be generated. A narrative text editor permits automatic generation of narrative text automatically using pre-defined scope for the generated text, or under the guidance of scope input by a user. Generated text retains links to the origin structure and scope used in creation of the narrative text permitting quick troubleshooting of issues in the narrative text generation and rapid review and updating under the guidance of established rule sets or system users.

A system for detecting an original language of a translated document retrieves the translated document, and identifies a language of the retrieved document. The system calculates a language model for the language of the retrieved document (LM(RD)). The system calculates a distinct vector as a difference between LM(RD) and a common language model for the language of the retrieved document (LMT(RD)). The system obtains pair vectors for language model pairs associated with the language of the retrieved document, and calculates a vector distance between the distinct vector and each pair vector (or between the (LM(RD)) and each pair vector). The system identifies a given pair vector within a threshold vector distance, and calculates the confidence score. The system then identifies the original language corresponding to the given pair vector as the original language of the retrieved document, and retrieves an original document in the original language of the retrieved document.

Disclosed herein are systems and methods to incorporate human knowledge when developing and using statistical models for natural language understanding. The disclosed systems and methods embrace a data-driven approach to natural language understanding which progresses seamlessly along the continuum of availability of annotated collected data, from when there is no available annotated collected data to when there is any amount of annotated collected data.

Approaches for translating a transliterated search query are provided. An approach includes receiving a search query containing a transliterated word. The approach also includes determining a source language corresponding to the transliterated word. The approach further includes converting the transliterated word to a word in the source language. The approach additionally includes translating the word in the source language to a word in a target language. The approach also includes performing a search using the word in the target language.

A computer-implemented method for presenting statistical analysis in a natural language textual output comprising: receiving data to be analyzed by the processor; processing the data according to at least one of a plurality of pre-established statistical analysis types, thereby providing processed data; interpreting the processed data by analyzing the processed data to provide a pre-determined natural language text, thereby providing interpreted data; and generating a natural language textual output for the interpreted data according to at least one pre-established rule for converting the interpreted data to a natural language textual output.

Mechanisms are provided for performing context based synonym filtering for natural language processing. Content is parsed into one or more conceptual units, wherein each conceptual unit comprises a portion of text of the content that is associated with a single concept. For each conceptual unit, a term in the conceptual unit is identified that has a synonym to be utilized during natural language processing of the content. A first measure of relatedness of the term to at least one other term in the conceptual unit is determined. A second measure of relatedness of the synonym of the term to the at least one other term in the conceptual unit is determined. A determination whether or not to utilize the synonym when performing natural language processing on the conceptual unit is made based on the first and second measures of relatedness and natural language processing on the content is performed accordingly.

The present disclosure provides a voiceprint authentication method and a voiceprint authentication apparatus. The method includes: displaying a first character string to a user, in which the first character string includes a predilection character preset by the user, and the predilection character is displayed as a symbol corresponding to the predilection character in the first character string; obtaining a speech of the first character string read by the user; obtaining a first voiceprint identity vector of the speech of the first character string; comparing the first voiceprint identity vector with a second voiceprint identity vector registered by the user to determine a result of a voiceprint authentication.

Technology is provided for improving digital assistant performance by generating and presenting suggestions to users for completing a task or a session. To generate the suggestions, a machine learned language prediction model is trained with features extracted from multiple sources, such as log data and session context. When input is received from a user, the trained machine learned language prediction model is used to determine the most likely suggestion to present to the user to lead to successful task completion. In generating the suggestion, intermediate suggestion data, such as a domain, intent, and/or slot, is generated for the suggestion. From the generated intermediate suggestion data for the suggestion, a surface form of the suggestion is generated that can be presented to the user. The resulting suggestion and related context may further be used to continue training the machine learned language prediction model.

Aspects of the present invention provide a more universal, easy, natural, and vendor-agnostic interface to configure, manage, and/or monitor devices in networks. In embodiments, a user-friendly natural language interface, such as a chat or messaging interface, may be used to “live chat” with one or more devices. In embodiments, a natural language input from a user intended for a target device is received and converted into one or more properly formed commands that are target-specific for the target device and may be executed by the target device. In embodiments, results from the execution of the one or more commands may be appropriately formatted for presentation to the user.