Abstract: A method includes receiving query data, receiving item data, initializing the query data as at least one natural language query token, and initializing the item data as at least one natural language item token. The method also includes generating a knowledge graph for the item based on the at least one natural language item token, flattening the knowledge graph for the item to generate a knowledge graph string, mapping at least one token associated with the knowledge graph string and the at least one natural language query token to an embedding vector using a matrix of parameters, and providing, to a machine learning model, the embedding vector. The method also includes receiving, from the machine learning model, a recommendation and a natural language explanation of the recommendation, and providing, to a user at a display, the recommendation and the natural language explanation of the recommendation.

Abstract: A plurality of weak label augmenters of different paradigms are integrated into a framework using robust training and negative instance filtering. A first of the augmenters extracts first weak labels from unlabeled data, a second of the augmenters extracts second weak labels from the unlabeled data. The robust training is used with an objective to downweight the probability of entities belonging to the wrong category. The first and second weak labels are filtered using an instance filter to update a high-precision training set shared by the plurality of augmenters. The plurality of augmenters are iteratively retrained using the updated high-precision training set to improve recognition performance over iterations.

Abstract: A system and method are disclosed for training a NER model configured to perform an NER task. The system and method advantageously utilize a label-word relation matrix to incorporate label semantic information into the attended text embedding. The system and method augment and enhance the design of the label-word relation matrix derived from label embeddings, which brings multiple benefits. In addition to the enhanced label-word relation matrix, the system and method further incorporate a novel training strategy that fits with the label embedding technique. With these improvements upon conventional NER systems, the system and method are effective for both open-domain and closed domain NER tasks.

Abstract: Disclosed are systems and methods for a computerized framework that provides a document structure parsing system for requirement engineering documents, where the logical structure of the text is not available, and is to be rebuilt based on the raw textual content. The framework approaches the build of the logical structure according to two phases. The first phase involves creating a list of list of text snippets from the raw text, where sequence labeling is adopted to re-segment and merge initially segmented text snippets. The second phase involves the framework executing computerized techniques including embedding adaptation approach, a hierarchy structure rebuilt algorithm, and a requirement text selection strategy to rebuild the hierarchy structure.

Abstract: A computer-implemented system and method relate to natural language processing. A candidate is selected based on a query. The candidate includes a question and an answer to that question. The candidate is classified as belonging to a class. The class indicates that the candidate is relevant to the query. The class is selected from among a group of classes, which includes at least that class and another class. The another class indicates that the candidate has the answer to the query. Upon classifying the candidate as belonging to the class, a machine learning system is configured to generate a relevant response for the query upon receiving the query and the question as input data. The relevant response provides information associated with a keyword of the question that is not contained in the query.

Abstract: Provided is a wireless Bluetooth earphone, which belongs to the field of headphone technology. The wireless Bluetooth earphone comprises a housing, a circuit board, a battery, a connection ring, and a connection terminal. The housing comprises a main housing and a battery housing. The circuit board is arranged in the main housing. The connection ring has a first end and a second end. The first end is connected to one end of the main housing, that is, the first end is connected to one end of the rear housing arranged with an opening. The second end is detachably connected to the battery housing, and the battery is arranged in the battery housing. The connection terminal is arranged in the connection ring. One end of the connection terminal is electrically connected to the circuit board, and the other end of the connection terminal is electrically connected to the battery.

Abstract: Provided is a wireless Bluetooth earphone, which belongs to the field of headphone technology. The wireless Bluetooth earphone comprises a housing, a circuit board, a battery, a connection ring, and a connection terminal. The housing comprises a main housing and a battery housing. The circuit board is arranged in the main housing. The connection ring has a first end and a second end. The first end is connected to one end of the main housing, that is, the first end is connected to one end of the rear housing arranged with an opening. The second end is detachably connected to the battery housing, and the battery is arranged in the battery housing. The connection terminal is arranged in the connection ring. One end of the connection terminal is electrically connected to the circuit board, and the other end of the connection terminal is electrically connected to the battery.

Abstract: The systems and methods described herein are directed to a Co-Augmentation framework that may learn new rules and labels simultaneously from unlabeled data with a small set of seed rules and a few manually labeled training data. The augmented rules and labels are further used to train supervised neural network models. Specifically, the systems and methods described herein include two major components: a rule augmenter, and a label augmenter. The rule augmenter is directed to learning new rules, which can be used to obtain weak labels from unlabeled data. The label augmenter is directed to learning new labels from unlabeled data. The Co-Augmentation framework is an iterative learning process which generates and refines a high precision set. At each iteration, both the rule augmenter and label augmenter will contribute new and more accurate labels to the high precision set, which is in turn used to train both the rule augmenter and label augmenter.

Abstract: An automatic terminology linking system includes a candidate generator configured to identify candidate nodes for each terminology that is to be linked to a node of the knowledge base. A pseudo-candidate generator is configured to identify pseudo-candidate nodes for candidate-less terminologies. A candidate scorer is configured to respectively score the candidate nodes and the pseudo-candidate nodes by collective inference using occurrence statistics and co-occurrence statistics for these nodes. The pseudo-candidate generator is configured to identify knowledge base nodes that are semantically-related to candidate-less terminology as the pseudo-candidate nodes for the candidate-less terminology.

Abstract: This application discloses a method of displaying information in a program interface of an application performed by a computer device. The method includes: displaying a virtual keyboard control and an extension bar control in the program interface; in response to an input operation in the virtual keyboard control, displaying at least one character string in the extension bar control, the at least one character string being determined according to the input operation in the virtual keyboard control; and in response to a select operation on a target string among the at least one character string in the extension bar control, displaying a function interface of applying a target function to the target string. This embodiment allows a user to quickly switch between function interfaces when using an application, thereby reducing operation steps of the user and improving human-computer interaction efficiency.

Abstract: Disclosed are systems and methods for a computerized framework that provides a document structure parsing system for requirement engineering documents, where the logical structure of the text is not available, and is to be rebuilt based on the raw textual content. The framework approaches the build of the logical structure according to two phases. The first phase involves creating a list of list of text snippets from the raw text, where sequence labeling is adopted to re-segment and merge initially segmented text snippets. The second phase involves the framework executing computerized techniques including embedding adaptation approach, a hierarchy structure rebuilt algorithm, and a requirement text selection strategy to rebuild the hierarchy structure.

Abstract: The present disclosure discloses an electric-machine shaft, wherein the electric-machine shaft is provided with a hollow structure in an axial direction at least one end, and the hollow structure is, at a position close to the end, provided with a coolant pumping mechanism having a spiral groove, so that the coolant is able to enter the hollow structure by an attractive force generated by rotation of the electric-machine shaft; the spiral groove is provided on an inner surface of an annular member, the annular member is fixedly mounted to the hollow structure, or the spiral groove is directly provided on an inner surface of the hollow structure; and the electric-machine shaft is provided with a plurality of groups of coolant channels in an axial direction, the coolant channels are in communication with the hollow structure, and when the electric-machine shaft is rotating, oil liquid inside the hollow structure is thrown out by the coolant channels, to cool components inside an electric-machine housing.

Abstract: A method and system for detecting and localizing a target audio event in an audio clip is disclosed. The method and system use utilizes a hierarchical approach in which a dilated convolutional neural network to detect the presence of the target audio event anywhere in an audio clip based on high level audio features. If the target audio event is detected somewhere in the audio clip, the method and system further utilizes a robust audio vector representation that encodes the inherent state of the audio as well as a learned relationship between state of the audio and the particular target audio event that was detected in the audio clip. A bi-directional long short term memory classifier is used to model long term dependencies and determine the boundaries in time of the target audio event within the audio clip based on the audio vector representations.

Abstract: A method of automatically generating a terminology definition knowledge base (KB) includes mapping each word in a word sequence to a real value dense vector using dense vector representations. The word sequence is then processed using a Convolutional Neural Network (CNN) model to identify whether the word sequence includes a terminology definition and to label the word sequence with a label indicating whether a terminology definition exists within the word sequence. The word sequence is then processed using a Conditional Random Field (CRF) model to identify boundaries of the terminology definition in the word sequence. The terminology definition is then extracted and added to the terminology definition KB.

Abstract: Systems and methods for weakly-supervised training a machine-learning model to perform named-entity recognition. All possible entity candidates and all possible rule candidates are automatically identified in an input data set of unlabeled text. An initial training of the machine-learning model is performed using labels assigned to entity candidates by a set of seeding rules as a first set of training data. The trained machine-learning model is then applied to the unlabeled text and a subset of rules from the rule candidates is identified that produces labels that most accurately match the labels assigned by the trained machine-learning model. The machine-learning model is then retrained using the labels assigned by the identified subset of rules as the second set of training data. This process is iteratively repeated to further refine and improve the performance of the machine-learning model for named-entity recognition.

Abstract: This application provides an article recommendation method and apparatus, a computer device, and a storage medium.

Abstract: A server includes a controller in communication with the server and configured to receive a query related to an issue with a vehicle, retrieve information related to the query from a database that includes structured data including data related to vehicle issues and a solution to the vehicle issues, determine whether the database includes structured data including data related to the solution responsive to the query, and output information including the solution to the issue when the determination identifies that the structured data including the solution in the database or retrieve information from a website when the determination identifies that the structured data including the solution is not found in the database, wherein the controller is further configured to retrieve the information from the website by conducting a keyword search for a first set of results and filtering the information from the website utilizing an autoencoder.

Abstract: A system for automatically labeling data using conceptual descriptions. In one example, the system includes an electronic processor configured to generate unlabeled training data examples from one or more natural language documents and, for each of a plurality of categories, determine one or more concepts associated with a conceptual description of the category and generate a weak annotator for each of the one or more concepts. The electronic processor is also configured to apply each weak annotator to each training data example and, when a training data example satisfies a weak annotator, output a category associated with the weak annotator. For each training data example, the electronic processor determines a probabilistic distribution of the plurality of categories. For each training data example, the electronic processor labels the training data example with a category having the highest value in the probabilistic distribution determined for the training data example.

Abstract: Systems and methods for training a machine-learning model for named-entity recognition. A rule graph is constructed including a plurality of nodes each corresponding to a different labeling rule of a set of labeling rules (including a set of seeding rules of known labeling accuracy and a plurality of candidate rules of unknown labeling accuracy). The nodes are coupled to other nodes based on which rules exhibit the highest sematic similarity. A labeling accuracy metric is estimated for each candidate rule by propagating a labeling confidence metric through the rule graph from the seeding rules to each candidate rule. A subset of labeling rules is then identified by ranking the rules by their labeling confidence metric. The identified subset of labeling rules is applied to unlabeled data to generate a set of weakly labeled named entities and the machine-learning model is trained based on the set of weakly labeled named entities.

Abstract: A method of detecting occupancy in an area includes obtaining, with a processor, an audio sample from an audio sensor and determining, with the processor, feature functional values of a set of selected feature functionals from the audio sample. The determining of the feature functional values includes extracting features in the set of selected feature functionals from the audio sample, and determining the feature functional values of the set of selected features from the extracted features. The method further includes determining, with the processor, occupancy in the area using a classifier based on the determined feature functional values.