One or more embodiments of the present specification relates to an online interview method and system. The method includes: establishing communication connection between an interviewing terminal and an interviewed terminal through a network; and acquiring communication information between the interviewing terminal and the interviewed terminal. The communication information includes one or more types of audio information, video information, and text information. The interviewing terminal includes one or more of a first host terminal, a second host terminal, and a text processing terminal. The first host terminal is configured to host an interview, and the first host terminal displays an interview outline and/or information of the interviewed terminal. The second host terminal is configured to host the interview and/or participate in the consultation of interview questions. The text processing terminal converts the audio information and/or the audio information in the video into corresponding text information.

Methods, systems, and computer programs are presented for displaying a customized news stream. One method includes an operation for identifying dwell times spent by users while accessing a first plurality of items, each item belonging to one media type from a plurality of media types (e.g., news articles, videos, slide shows, etc.). In addition, the method includes operations for determining statistical parameters for each media type based on the identified dwell times, and for detecting a news corpus having a second plurality of items. A priority for each item in the news corpus is determined based on the media type of the item, the corresponding statistical parameters for the media type of the item, and the profile of a user. The news stream is sent to the user for presentation on a display, the news stream being sorted based on the priority of the items in the news corpus.

Disclosed are system and methods for digitally capturing, labeling, and analyzing data representing shared experiences between a service provider and a customer. The shared experience data is used to identify, test, and implement value-added improvements, enhancements, and augmentations to the shared experience and to monitor and ensure the quality of customer service. The improvements can be implemented as customer service process modifications, precision learning and targeted coaching for agents rendering customer service, process compliance monitoring, and as knowledge curation for a knowledge bot software application that facilitates automation of tasks and provides a natural language interface for accessing historical knowledge bases and solutions.

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.

Exemplary embodiments provide systems, devices and methods for computer-based categorization and moderation of user-generated content for publication of the content in an online environment. Exemplary embodiments determine a likelihood that the user-generated content falls into a first selected category unsuitable for publication. The likelihood is compared to a first set of threshold values and then, in this embodiment, it is determined whether to electronically publish the content in the online environment based on the comparison.

An architecture for semantic search over encrypted data that improves upon existing encrypted data search techniques by providing a solution that is space-efficient on both the cloud and client sides, considers the semantic meaning of the user's query, and returns a list of documents accurately ranked by their similarity to the query. Different search schemes are presented based on S3C architecture (namely, FKSS, SKSS, and KSWF) that are fine-tuned for different types of datasets. The system requires only a single plaintext query to be entered and is easily portable to thin-clients, making it simple and quick for users to use. The system is also shown to be secure and resistant to attacks.

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.

Systems and methods for indexing geological features are disclosed. In one embodiment, a method for indexing geological features includes accessing a database storing a plurality of map objects that originate from documents. Each map object includes a map defined by a geographical boundary and a text caption. The method includes, for each map object, determining a plurality of geohashes within the geographical boundary, and includes, for each map object, comparing terms of the text caption with a list of geological keywords. For each map object, the method includes identifying one or more geological noun phrases within the text caption that match one or more geological noun phrases of the list. The method includes determining, for each geological noun phrase, one or more geohashes associated with the geological noun phrase and, for each geohash, determining a frequency that the geohash is associated with the geological noun phrase.

The present disclosure relates to extracting entities from a collection of digital content items based on text from within the digital content items. For example, the present disclosure describes a customizable entity extraction system that utilizes a number of models to extract entities, rank entities, and classify certain entities using a combination of rule-based and machine learning approaches. In one or more embodiments, a customizable entity extraction system applies a set of rules to unstructured text of a collection of digital content items to extract and classify a set of entities in connection with a specific domain of interest.

The disclosure is directed to systems, methods, and computer storage media, for, among other things, generating, training, and tuning lexicon-based classifier models. The models may be employed in various compliance enforcement applications and/or tasks. The tradeoff between the model's false positive error rate (FPR) and the model's false negative rate (FNR) may be “tuned” via a balance parameter supplied by the user. The classifier model may classify content (e.g., text records) as either belonging to a “positive” class or a “negative” class. The positive class may be associated with non-compliance, while the negative class may be associated with compliance (or vice-versa). In some embodiments, the classifier model may be a probabilistic probability model that provides a probability (or degree of belief) that the content is associated with the positive and/or negative class.