A centralized system may collect and aggregate assessments from multiple websites. An aggregate score may be calculated for the resource that cumulatively considers assessments from a plurality of different websites from which assessments are received from users. Text descriptions associated with each of the assessments may be provided to a machine-learning system that uses a trained model to assign identifiers to the assessments as they are received. These identifiers may include common words or text that are descriptive of different facets of user experiences related to receiving and using the resource. After selecting one or more identifiers, assessments associated with that identifier may be included or excluded from the display. Additionally, the overall aggregate score for the resource may be recalculated by removing components of that score that are based on assessments with identifiers that have been selected for exclusion.

The present disclosure relates generally to Lightweight Directory Access Protocol (LDAP), and more particularly, to techniques for improving query performance on an LDAP server. One particular technique includes receiving a LDAP query having search criteria, identifying one or more search filters within the search criteria, determining candidate indices based on the identified one or more search filters, evaluating the candidate indices based on statistics collected for the candidate indices, selecting one or more indices from the candidate indices based on the evaluating, and executing the LDAP query on an LDAP directory using the selected one or more indices.

Computer-implemented techniques for enhanced tagging of natural language queries that are initially segmented and tagged by a named entity recognition system. By doing so, enhanced tagging of a natural language query that represents a deeper understanding of the query is provided. The enhanced tagging improves the operation of search engines that use the enhanced tags by enabling the search engine to identify and return more relevant search results in answers to natural language queries.

A computer or computer retrieval system implemented method for inputting and processing file feature determination information by network terminal users. It includes providing terminal users with the items of the files according to query, determining the input feature word(s) according to the prescribed operation modes and the prescribed modes on the web page on which the item sequence(s) being located or a web page linked by that web page directly. Retrieval system can process the input information to create or improve a retrieval method or database used by users which can include different feature words or classification results, therefore the search efficiency would be greatly improved

Examples associated with reading difficulty level based resource recommendation are disclosed. One example may involve instructions stored on a computer readable medium. The instructions, when executed on a computer, may cause the computer to obtain a set of candidate resources related to a source document. The candidate resources may be obtained based on content extracted from the source document. The instructions may also cause the computer to identify reading difficulty levels of members of the set of candidate resources. The instructions may also cause the computer to recommend a selected candidate resource to a user. The selected candidate resource may be recommended based on subject matter similarity between the selected candidate resource and the source document. The selected candidate resource may also be recommended based on reading difficulty level similarity between the selected candidate resource and the source document.

The disclosed apparatus, systems, and methods relate to a location query mechanism that can efficiently determine whether a target entity is located within a region of interest (ROI). At a high level, the location query mechanism can be configured to represent a ROI using one or more polygons. The location query mechanism can, in turn, divide (e.g., tessellate) the one or more polygons into sub-polygons. Subsequently, the location query mechanism can use the sub-polygons to build an index system that can efficiently determine whether a particular location is within any of the sub-polygons. Therefore, when a computing device queries whether a particular location is within the region of interest, the location query mechanism can use the index system to determine whether the particular location is within any of the sub-polygons.

Methods and systems for generating and using a conversation summary model. The method comprises receiving at least one training dataset. The at least one training dataset comprises data samples, each data sample comprising a text comprising text segments. The text is labelled with a conversation summary comprising any of the text segments which summarize the text. The at least one training dataset includes a dataset from a specific source. Using the at least one training dataset and the pre-trained model, the method further comprises generating the conversation summary model by fine-tuning the pre-trained model. The generated conversation summary model may be used to generate conversation summaries for chat conversations.

A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

A system to calculate a reconfigured confidence score is configured to receive a text, a plurality of labels, and a plurality of confidence scores from a plurality of models and assign a weightage to the inputs received from the plurality of models. The system is configured to select a first text with a first label and retrieve a second text, a third text, and a second label. The system is further configured to generate a first, second and third output confidence score for the first text, second text and third text, and corresponding labels. The system compares the plurality of output confidence scores and generates an output which comprises of the first text, the first label, and a final confidence score, wherein the final confidence score is one among the first, second and third output confidence scores.

One example method includes collecting annotated unstructured documents that each include a table with words whose respective column indices are known, using the documents to train a model to detect a table header in a given document, identifying, by the model, a region of a document that corresponds to a table header in a new document that is not part of the training data, using an algorithm to perform a segmentation process on the table header that identifies column boundaries in the table header, and to use the identified column boundaries to preliminarily assign a respective column index to each word in the table header. Finally, a graph neural network model is run on a graph that includes the words in the table, and running the graph neural network generates a refined prediction of a respective column index for each of the words in the table of the new document.