A method for issuing control signals in response to sentiment. In some embodiments, the method includes: assigning, to each of a plurality of comments, a respective topic vector of length k; determining whether a largest element of the topic vector of a first comment of the plurality of comments exceeds a weight threshold; in response to the determining that the largest element exceeds the weight threshold, classifying the first comment into a first topic, of k topics, the first topic corresponding to the position, in the topic vector, of the largest element of the topic vector; calculating a first sentiment score; calculating an average sentiment score, based in part on the first sentiment score; determining whether the average sentiment score meets a criterion; and in response to the determining that the criterion is met, generating a control signal, the control signal including a message related to the first topic.

An information processing apparatus (1S) includes: a creation unit (232) configured to create support information for supporting at least one of construction and maintenance of a database on the basis of first information including a representative question and an answer sentence associated with the representative question and stored in the database stored in a storage unit and second information indicating a history of reception with respect to a user; and a processing unit (233) configured to execute processing of performing at least one of construction and maintenance of the database on the basis of input information input to the support information created by the creation unit (232).

A method is disclosed. The method includes receiving a text phrase from a user and parsing the text phrase using a natural language parser to generate distinct words. A vector of values relating to the distinct words is generated and compared to vectors in a graph database which relate to learned communities of language. Based on the comparing, a most similar community in the learned communities of language is determined and an action set action set associated with the most similar community is queried. A response to the text phrase from the user is then generated based on the action set and provided to the user.

A computing system using machine learning and natural language processing techniques to map assessment text into a latent feature space are disclosed herein. The latent feature space includes a set of impact categories and allows for assessment comparison and determination of deficiencies in assessments. The computing system inputs a portion of an assessment into a machine learning model to determine what impact category in the latent feature space that the portion maps to. Based on mapping an assessment to the set of impact categories, the computing system generates a group of scores that includes a score for each impact category. The computing system compares the scores with other scores to determine how the assessment can be improved.

Methods and systems are described herein for receiving a document (e.g., a technical paper), identifying sections of the document, and inputting each section into a corresponding machine learning model that has been trained using previously reviewed documents to process text and other data within a specific section. The machine learning model may output a score indicating the degree of compliance to a particular standard of a particular document. In addition, various recommendations for content to be added to the document may be identified and provided to the user along with the score for each section.

Systems, methods, and computer program products for search platforms for unstructured interaction summaries. An application executing on a processor may receive a query comprising a term. The application may generate, based on an embedding vector and the term, an expanded query comprising a plurality of additional terms. The application may generate, based on a term frequency inverse document frequency model, a vector for the expanded query and generate an entity vector for the query. The application may generate a combined vector for the query based on the entity vector and the vector for the expanded query. The application may compute, based on the combined vector for the query and a feature matrix of a corpus, a respective cosine similarity score for a plurality of results in the corpus. The application may return one or more of the plurality of results as responsive to the query based on the similarity scores.

Techniques regarding similarity based negative sample analysis are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a similarity component that can determine similarity metrics for respective entities based on a vector space model. The respective entities can be represented by a dataset. Also, the computer executable components can comprise a sampling component that can perform a negative sampling analysis on the dataset based on the similarity metrics.

A topic-processing system processes topics in a set of documents in a two-stage manner. In the first stage, the system recognizes candidate topics in the set of documents using a machine-trained named-entity recognition (NER) model, to produce original NER information. In a second stage, the system aggregates the original NER information over the set of documents, to produce aggregated information. The system then ranks the candidate topics in the set of candidate topics based on the aggregated information using a machine-trained classification model, to produce a set of ranked topics. The system then selects a set of final topics from the set of ranked topics, e.g., by excluding ranked topics having scores below a prescribed threshold value. A production system presents supplemental information regarding selected final topics, where those final topics are identified by the topic-processing system.

The present invention relates to data processing system and method in supply chain application. The data processing system includes clustering of received supply chain data after normalization, tokenization and vectorization through graph-based analysis.

In an approach to generating question answer pairs, one or more computer processors receive a corpus of text. One or more computer processors extract one or more key concepts from the corpus of text. Based on the one or more key concepts, one or more computer processors generate one or more questions associated with the key concepts, where the one or more key concepts are answers to the one or more generated questions. One or more computer processors display the one or more generated questions and the answers to the one or more generated questions.