A system, a method, and a computer program product for generation of keywords for a solution note for resolving an issue associated with a computing component. A dataset for training a keyword data model is received. The dataset includes a plurality of variables associated with one or more values. The keyword data model is configured for determination, as a function of one or more variables in the plurality of variables, of one or more keywords in a plurality of keywords associated with a computing solution in a plurality of computing solutions for resolving a problem with an operation of a computing component in a plurality of computing components. The keyword data model is trained using the received dataset and the keyword data model is applied to one or more variables in the received dataset to generate one or more keywords. One or more keywords associated with the computing solution is generated.

Examples herein involve detection of entities in unstructured data. Terms are extracted from unstructured data. Entities scores for the terms are calculated using information from a name probability source, a known entity database, and historical context information. The entity scores indicate a probability that the respective terms refer to entities. The presence of detected entities are indicated based on the entity scores.

Described herein is a mechanism for utilizing a neural network to identify and rank search results. A machine learning model is trained by converting training data comprising query-document entries into query term-document entries. The query term-document entries are utilized to train the machine learning model. A set of query terms are identified. The query terms can be derived from a query history. The trained machine learning model is used to calculate document ranking scores for the query terms and the resultant scores are stored in a pre-calculated term-document index. A query to search the document index is broken down into its constituent terms and an aggregate document ranking score is calculated from a weighted sum of the document ranking scores corresponding to the individual query terms. Because the term-document index can be pre-calculated, it can be downloaded to provide deep learning search capabilities in a computationally limited environment.

A terminal device includes a first communication device, a display device, and a first control device. The first control device functions as a display controller. The display controller allows the display device to display a first massage in a message display area of the display device. When the first message is not a high-importance message and the display controller then receives through the first communication device a second message sent from the information processing apparatus, the display controller allows the display device to execute first processing of scrolling a displayed image in the message display area and displaying the second message in the message display area. When the first message is a high-importance message and the display controller then receives the second message through the first communication device, the display controller allows the display device to execute second processing different from the first processing.

A system includes at least one processor to perform natural language processing on text from at least one document and assign the at least one document to at least one subjective wellbeing dimension by comparing the text from the at least one document with a subjective wellbeing dimension filter for each subjective wellbeing dimension, insert the at least one document into at least one bin, each bin associated with a particular subjective wellbeing dimension, and analyze each document in each bin associated with the particular subjective wellbeing dimension to determine a score for each subjective wellbeing dimension and an overall score that is based on each score for each subjective wellbeing dimension.

An information processing apparatus according to an embodiment includes an aligner that aligns, with reference to a reference data set that is a sequential data set, another sequential data set; and a target data extractor that extracts a portion of the another sequential data set corresponding to the reference data set as a target data set.

The present disclosure discloses a text classification system based on feature selection and a method thereof in the technical field of natural language processing and short text classification, comprising: acquiring a text classification data set; dividing the text classification data set into a training text set and a test text set, and then pre-processing the training text set and the test text set; extracting feature entries from the pre-processed training text set through improved chi-square statistics to form feature subsets; using TF-IWF algorithm to give the weight to the extracted feature entries; based on the weighted feature entries, establishing a short text classification model based on a support vector machine; and classifying the pre-processed test text set by the short text classification model. The present disclosure solves the problem that the short text content is sparse to some extent, thereby improving the performance of short text classification.

A system and method for providing transactional data privacy while maintaining data usability, including the use of different obfuscation functions for different data types to securely obfuscate the data, in real-time, while maintaining its statistical characteristics. In accordance with an embodiment, the system comprises an obfuscation process that captures data while it is being received in the form of data changes at a first or source system, selects one or more obfuscation techniques to be used with the data according to the type of data captured, and obfuscates the data, using the selected one or more obfuscation techniques, to create an obfuscated data, for use in generating a trail file containing the obfuscated data, or applying the data changes to a target or second system.

A word extraction unit 11 that analyzes m texts to extract n words, a vector computation unit 12 that converts each of the m texts into a q-dimension vector and each of the n words into a q-dimension vector, thereby computing m text vectors including q axis components and n word vectors including q axis components, and an index value computation unit 13 that takes each of inner products of the m text vectors and the n word vectors, thereby computing a similarity index value reflecting a relationship between the m texts and the n words are included, and it is possible to obtain a similarity index value representing which word contributes to which text and to what extent as an inner product value by calculating an inner product of a text vector computed from a text and a word vector computed from a word included in the text.

The present specification provides a method and apparatus for determining feature words and a server. The method includes: obtaining text data; extracting a first feature word from the text data; updating a word segmentation library based on the first feature word to obtain an updated word segmentation library, the word segmentation library including a plurality of predetermined feature words for representing predetermined attribute types; and extracting a second feature word from the text data based on the updated word segmentation library and the predetermined feature words.