A system, method, and computer program product for determining statement attributions. The system includes at least one processing component, at least one memory component, a feature extractor, a model generator, a model database, and an attribution selector. The method includes receiving a statement, generating at least one pattern that defines a grammatical feature of the statement, and generating a statement model from the at least one pattern. The method also includes determining a similarity value for the statement model and at least one reference model.

In some embodiments, techniques are provided for conducting similarity-based searches using DNA. In some embodiments, sets of features that represent stored data sets are encoded in DNA sequences such that a hybridization yield between a molecule having a given stored DNA sequence and a molecule having a reverse complement of a DNA sequence that encodes a set of features that represent a query data set reflects an amount of similarity between the set of features that represent the query data set and the set of features encoded in the given stored DNA sequence. In some embodiments, machine learning techniques are used to determine the DNA sequence encoding. In some embodiments, machine learning techniques are used to predict hybridization yields between DNA molecules.

Embodiments of the present disclosure provide for improved data sequence validity processing, for example to determine validity of sentences or other language within a particular language domain. Such improved processing is useful at least for arranging data sequences based on determined validity, and/or making determinations and/or performing actions based on the determined validity. A determined probability (e.g., transformed into the perplexity space) of each token appearing in a data sequence is used in any of a myriad of manners to perform such data sequence validity processing. Example embodiments provide for generating a perplexity value set for each data sequence in a plurality of data sequences, generating a probabilistic ranking set for the plurality of data sequences based on the perplexity value sets and at least one sequence ranking metric, and generating an arrangement of the plurality of data sequences based on the probabilistic ranking set.

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for information processing. In an information processing method, based on multiple weights corresponding to multiple words in text, a computing device determines a target object associated with the text among predetermined multiple objects, and also determines, among the multiple words, a set of key words with respect to the determination of the target object. Next, the computing device determines, among the set of key words, a set of target words related to a text topic of the text. Then, the computing device outputs the set of target words and an identifier of the target object in an associated manner. In this way, the credibility of the target object associated with the text that is determined by the information processing method is improved, thereby improving the user experience of the information processing method.

A schema-ontology is automatically constructed with reference to implicit sematic relationships of a database schema. An estimation of the meaning of the language input is determined based on the language input and the schema-ontology. The machine interpretation of the language input is generated based on the meaning and based on the estimation of the meaning including an estimation of an ambiguity of portions of the language input. A software action that is responsive to the machine interpretation of the language input is selected. The software action is implemented based on the machine interpretation of the language input.

A method, computer program, and computer system is provided for query matching of sentences based on co-attention scores. Two or more query inputs to a neural network are received. A correlation factor between the two or more query input is identified based on attention weights. A feature vector is generated based on the attention weights. A probability value corresponding to the two or more query inputs is determined based on the generated feature vector.

Method and apparatus for providing a personalized self-support service to a user of an online application coupled with an online community forum. Embodiments include obtaining a plurality of questions from the online community forum and obtaining historical user data. Embodiments further include identifying one or more part-of-speech words in the plurality of questions and generating a high-dimensional vector for each question of the plurality of questions based on a frequency of the one or more part-of-speech words. Embodiments further include identifying one or more user features of the plurality of users based on the historical user data and establishing, based on the historical user data, one or more statistical correlations between user features and part-of-speech words. Embodiments further include training a predictive model based on the one or more statistical correlations. Embodiments further include using the predictive model to predict to provide one or more relevant questions to the user.

Methods, systems, and programming for retrieving content items for a search are described herein. In a non-limiting embodiment, a query including a plurality of terms may be received. For each of the plurality of terms, a posting list of one or more content items may be obtained. The posting list may include a ranked list of term scores corresponding to the one or more content items, each of the term scores being indicative of a level of relevance of a corresponding content item to a term associated with the posting list. A list of relevant content items for the query may be determined based on the term scores in each posting list for the one or more content items identified with respect to each term. At least one of the relevant content items may be provided as a response to the query.

Embodiments of the present disclosure are directed to systems and methods for managing a database and performing database operations. An exemplary method in accordance with embodiments of this disclosure comprises: receiving a request to perform one or more database operations on a dataset comprising one or more data items; inputting the dataset into a statistical model, wherein the statistical model is configured to identify one or more storage locations associated with the one or more data items based on a similarity between one or more properties of the one or more data items; receiving the one or more storage locations associated with the one or more data items; updating the one or more data items based on the received one or more storage locations; and performing the one or more database operations on the one or more updated data items based on the one or more storage locations.

Systems and methods provide micro-credential accreditation. The systems and methods analyze, using one or more prediction models, received text submissions received from applicants via interaction with an applicant device. The prediction model(s) fit one or more micro-credentials to the received text submission, which may collectively or independently qualify the applicant for one or more accreditation credits. By processing the received text submission, the systems and methods allow for consistent and standard output of micro-credentials by the prediction model(s). Furthermore, the systems and methods provide for monitoring the prediction model output(s) to ensure ethical fairness across varying demographic groups of applicants.