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: A computer-implemented method of Named Entity Recognition (NER) includes receiving an input, identifying a plurality of candidate entities corresponding to the input, assigning word embeddings to the input at an embedding layer, capturing sequential context of the word embeddings in an encoding layer to obtain encoded word embeddings, constructing an entity relation graph using global coreference relations and local dependency relations to obtain a coreference graph and a dependency graph, fusing the encoded word embeddings, coreference graph, and dependency graph, via a graphical neural network (GNN), to obtain updated word embeddings, and decoding the updated word embeddings via a decoding layer to obtain enriched entity predictions.

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: 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: An oxygen reduction device in an ion source region of inductively coupled plasma is provided. The oxygen reduction device includes a torch and an inflation sleeve. An upper end of the inflation sleeve is sealed and sleeved outside the torch, and a lower end of the inflation sleeve and the torch are arranged at an interval to form an inflation gap. An inflation hole communicating with the inflation gap is formed in an outer side wall of the inflation sleeve. An outer side face of the lower end of the inflation sleeve is protruded outwards to form an annular gas guiding protrusion. The annular gas guiding protrusion is configured for being arranged opposite to a sampling cone base arranged below the torch, and a gas outlet gap is formed between the annular gas guiding protrusion and the sampling cone base.

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: 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: Methods and systems for performing semantic entity recognition. The method includes accessing a document stored in a memory and selecting from a general knowledge data repository, target domain information based on a specified target domain. The method also includes generating a plurality of weak annotators for the document based upon the selected target domain information and expert knowledge from a domain-specific expert knowledge data repository and applying the plurality of weak annotators to the document to generate a plurality of weak labels. The method further includes selecting at least one weak label from the plurality of weak labels as training data and training a semantic entity prediction model using the training data.

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: Methods and systems for performing semantic entity recognition. The method includes accessing a document stored in a memory and selecting from a general knowledge data repository, target domain information based on a specified target domain. The method also includes generating a plurality of weak annotators for the document based upon the selected target domain information and expert knowledge from a domain-specific expert knowledge data repository and applying the plurality of weak annotators to the document to generate a plurality of weak labels. The method further includes selecting at least one weak label from the plurality of weak labels as training data and training a semantic entity prediction model using the training data.

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.