Abstract: A method for segmenting 3D digital model of jaw is provided. The method includes: obtaining a first 3D digital model of jaw; and segmenting the first 3D digital model of jaw using a trained deep artificial neural networks.

Abstract: According to one embodiment, a method, computer system, and computer program product for continuously ranking components in a live information is provided. The present embodiment may include receiving search feedback derived from interactions between users and information retrieval systems; assigning weights to each of the ranking components; adjusting the assigned weights based on search feedback; modifying the current set of ranking components based on the search feedback by adding new ranking components and deleting old ranking components; transmitting a query from the users to the current set of ranking components; aggregating ranking results from the transmitted query into a single ranking based on the weights; and transmitting the single ranking to the users.

Abstract: According to one embodiment, a method, computer system, and computer program product for continuously ranking components in a live information is provided. The present embodiment may include receiving search feedback derived from interactions between users and information retrieval systems; assigning weights to each of the ranking components; adjusting the assigned weights based on search feedback; modifying the current set of ranking components based on the search feedback by adding new ranking components and deleting old ranking components; transmitting a query from the users to the current set of ranking components; aggregating ranking results from the transmitted query into a single ranking based on the weights; and transmitting the single ranking to the users.

Abstract: Techniques for learning from highly-diverse datasets are provided. In one embodiment, the system is provided that comprises a memory that stores computer executable components. The system can comprise a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a neural network component that creates a neural network comprising a router that routes the neural network to a first layer of neurons that comprises a plurality of neurons. The computer executable components can comprise a training component that performs a plurality of successive training iterations on the neural network, a first iteration of the plurality of successive training iterations comprising both training the router to route among the plurality of neurons of the first layer of neurons, and training a first neuron of the plurality of neurons of the first layer of neurons to produce a given output from a given input.

Abstract: A method for knowledge base completion includes encoding a knowledge base comprising entities and relations between the entities into embeddings for the entities and embeddings for the relations. The embeddings for the entities are encoded based on a Graph Convolutional Network (GCN) with different weights for at least some different types of the relations, which GCN is called a Weighted GCN (WGCN). The method further includes decoding the embeddings by a convolutional network for relation prediction. The convolutional network is configured to apply one dimensional (1D) convolutional filters on the embeddings, which convolutional network is called Conv-TransE. The method further includes at least partially complete the knowledge base based on the relation prediction.

Abstract: Mechanisms are provided to implement a generative adversarial network (GAN) for natural language processing. With these mechanisms, a generator neural network of the GAN is configured to generate a bag-of-ngrams (BoN) output based on a noise vector input and a discriminator neural network of the GAN is configured to receive a BoN input, where the BoN input is either the BoN output from the generator neural network or a BoN input associated with an actual portion of natural language text. The mechanisms further configure the discriminator neural network of the GAN to output an indication of a probability as to whether the input BoN is from the actual portion of natural language text or is the BoN output of the generator neural network.

Abstract: Mechanisms are provided for implementing a Question Answering (QA) system utilizing a trained generator of a generative adversarial network (GAN) that generates a bag-of-ngrams (BoN) output representing unlabeled data for performing a natural language processing operation. The QA system obtains a plurality of candidate answers to a natural language question, where each candidate answer comprises one or more ngrams. For each candidate answer, a confidence score is generated based on a comparison of the one or more ngrams in the candidate answer to ngrams in the BoN output of the generator neural network of the GAN. A final answer to the input natural language question is selected from the plurality of candidate answers based on the confidence scores associated with the candidate answers, and is output.

Abstract: Software that generates an answer to an input question using a source document by performing the following operations: (i) receiving a question; (ii) generating a plurality of vectors including a first vector representation of a term in the question and a second vector representation of a term in a source document; (iii) providing each dimension of each of the first vector representation and the second vector representation into a respective input node of an artificial neural network; (iv) determining whether the source document is relevant to answering the question based, at least in part, on an output generated by the artificial neural network; and (v) in response to determining that the source document is relevant, generating an answer to the question utilizing the source document.

Abstract: Software that compares vector representations of question terms and passage terms in question answering systems by performing the following steps: (i) receiving a question; (ii) generating a plurality of vectors including a first vector representation of a term in the question and a second vector representation of a term in a set of natural language text; (iii) generating a similarity score representing an amount of similarity between the first vector representation and the second vector representation; and (iv) determining whether the set of natural language text is relevant to the question based, at least in part, on the generated similarity score.

Abstract: Software that compares vector representations of question terms and passage terms in question answering systems by performing the following steps: (i) receiving a question; (ii) generating a plurality of vectors including a first vector representation of a term in the question and a second vector representation of a term in a set of natural language text; (iii) generating a similarity score representing an amount of similarity between the first vector representation and the second vector representation; and (iv) determining whether the set of natural language text is relevant to the question based, at least in part, on the generated similarity score.

Abstract: A method for segmenting 3D digital model of jaw is provided. The method includes: obtaining a first 3D digital model of jaw; and segmenting the first 3D digital model of jaw using a trained deep artificial neural networks.

Abstract: According to one embodiment, a method, computer system, and computer program product for continuously ranking components in a live information is provided. The present embodiment may include receiving search feedback derived from interactions between users and information retrieval systems; assigning weights to each of the ranking components; adjusting the assigned weights based on search feedback; modifying the current set of ranking components based on the search feedback by adding new ranking components and deleting old ranking components; transmitting a query from the users to the current set of ranking components; aggregating ranking results from the transmitted query into a single ranking based on the weights; and transmitting the single ranking to the users.

Abstract: According to one embodiment, a method, computer system, and computer program product for continuously ranking components in a live information is provided. The present embodiment may include receiving search feedback derived from interactions between users and information retrieval systems; assigning weights to each of the ranking components; adjusting the assigned weights based on search feedback; modifying the current set of ranking components based on the search feedback by adding new ranking components and deleting old ranking components; transmitting a query from the users to the current set of ranking components; aggregating ranking results from the transmitted query into a single ranking based on the weights; and transmitting the single ranking to the users.

Abstract: A computer-aided translation system includes a processor configured to generate a suggestion pool of possible translations for each sentence in a document. A translation module configured to provide a best suggestion from the suggestion pool to a user for a sentence being translated and to provide an updated best suggestion from the updated suggestion pool to the user after the receipt of a user's translation prefix input. A pool update module configured to update the suggestion pool based on the user's input of a translation prefix.

Abstract: Software that trains an artificial neural network for generating vector representations for natural language text, by performing the following steps: (i) receiving, by one or more processors, a set of natural language text; (ii) generating, by one or more processors, a set of first metadata for the set of natural language text, where the first metadata is generated using supervised learning method(s); (iii) generating, by one or more processors, a set of second metadata for the set of natural language text, where the second metadata is generated using unsupervised learning method(s); and (iv) training, by one or more processors, an artificial neural network adapted to generate vector representations for natural language text, where the training is based, at least in part, on the received natural language text, the generated set of first metadata, and the generated set of second metadata.

Abstract: Methods and systems for computer-aided translation include receiving a document having one or more sentences to be translated; generating a suggestion pool of possible translations for each sentence in the document; providing a best suggestion from the suggestion pool to a user for a sentence being translated; updating the suggestion pool based on the user's input of a translation prefix; and providing an updated best suggestion from the updated suggestion pool to the user for the sentence being translated.

Abstract: A computer program that generates a vector representation of a set of natural language text in a natural language processing system by: (i) receiving a first set of natural language text and a set of information pertaining to the first set of natural language text, where the information includes a dependency parse tree including a root node and a plurality of nodes that depend from the root node, where the root node represents the first set of natural language text, and where the plurality of nodes that depend from the root node represent context features of the first set of natural language text; and (ii) generating, by the natural language processing system, a first vector representation of the first set of natural language text, wherein the generating includes adding vector representations for the context features represented by the plurality of nodes that depend from the root node.

Abstract: A computer program that uses structured information, such as syntactic and semantic information, as context for representing words and/or phrases as vectors, by performing the following steps: (i) receiving a first set of natural language text and a set of information pertaining to the first set of natural language text, where the information includes metadata and corresponding contextual information indicating a relationship between the metadata and the first set of natural language text; and (ii) generating a first vector representation for the first set of natural language text utilizing the metadata and its corresponding contextual information.

Abstract: A computer program that uses structured information, such as syntactic and semantic information, as context for representing words and/or phrases as vectors, by performing the following steps: (i) receiving a first set of natural language text and a set of information pertaining to the first set of natural language text, where the information includes metadata and corresponding contextual information indicating a relationship between the metadata and the first set of natural language text; and (ii) generating a first vector representation for the first set of natural language text utilizing the metadata and its corresponding contextual information.

Abstract: A computer program that generates a vector representation of a set of natural language text in a natural language processing system by: (i) receiving a first set of natural language text and a set of information pertaining to the first set of natural language text, where the information includes a dependency parse tree including a root node and a plurality of nodes that depend from the root node, where the root node represents the first set of natural language text, and where the plurality of nodes that depend from the root node represent context features of the first set of natural language text; and (ii) generating, by the natural language processing system, a first vector representation of the first set of natural language text, wherein the generating includes adding vector representations for the context features represented by the plurality of nodes that depend from the root node.