Abstract: A method is provided for role-oriented risk analysis in a contract. The method generates, using deep semantic association analysis, a report specifying a set of potential risks relating to explicit and hidden roles of contract parties. The generating step categorizes input statements of the contract into respective obligation/right pairs according to a deep semantic association distribution thereof. Each pair includes a respective obligation and a respective right. The generating step detects deep semantic differences between the respective pairs and a set of reference obligation/right pairs. The generating step identifies the explicit and hidden roles of the involved parties in the respective obligations/rights pairs according to domain-specific use scenarios and multidimensional local and global context clues in the contract. The generating step identifies the set of potential risks by applying a deep semantic role-oriented risk entailment model to the deep semantic differences.

Abstract: Embodiments of the present disclosure relate to attention-based neural language processing. In an embodiment, a method is disclosed. According to the method, a sentence graph is generated from a sentence containing words. The sentence graph comprises nodes representing words and edges connecting the nodes, at least one of the edges being constructed to indicate a syntactic relationship between words represented by nodes connected therebetween. Word representations for the words are determined based on the sentence graph by applying an attention mechanism on respective ones of the nodes and respective sets of neighbor nodes for the nodes. A set of neighbor nodes for a node has edges connected to the node. A sentence representation for the sentence is determined based on the word representations for use in a natural language processing task related to the sentence. In other embodiments, a system and a computer program product are disclosed.

Abstract: A computer-implemented method for implementing separated attention on like and dislike items for personalized ranking includes performing an element-wise product on a user embedding and a final like item embedding to generate a first vector. The method further includes performing an element-wise product on the user embedding and a final dislike item embedding to generate a second vector. The method further includes computing a probability that the user prefers the like item to the dislike item based on the first and second vectors, and generating one or more item recommendations including one or more electronic images for the user using the probability.

Abstract: The present invention relates to a method, computer system, and computer program product for data processing based on a response strategy. According to the method, a performance value of a server is determined in response to receiving at least one request to the server. A response strategy for the at least one request is determined based on the determined performance value. At least one response is provided to the at least one request according to the determined response strategy.

Abstract: Systems and methods for estimating battery-powered driving distance for a vehicle, including training a relative model for a battery using input historical battery temperature data and historical battery-external factors, and predicting a future battery temperature based on the relative model and one or more of current or future battery-external factors. A battery power capacity is determined using the predicted future battery temperature and input manufacturer specifications for the battery, and a remaining battery powered driving distance is calculated based on input vehicle power consumption data and the determined battery power capacity.

Abstract: Methods and systems for natural language processing include generating respective feature vectors, for each word in an input, based on sequences of input words of different respective lengths. The respective feature vectors for each word in the input are combined to form a combined vector for each word. A hidden state is determined for each word in the input based on the combined vector. The hidden states for all words in the input are combined to form a multi-resolution context vector. A natural language processing action is performed using the multi-resolution context vector.

Abstract: A computer-implemented method is presented for implementing multi-aspect sentiment analysis by collaborative attention allocation. The method includes extracting a sequence of word vectors from a sentence received from a data stream, feeding the sequence of word vectors to long short-term memory (LSTM) neural networks to generate a sequence of hidden states corresponding to the sequence of word vectors, generating a plurality of aspect embedding vectors for each aspect, employing an attention mechanism to determine attention weight vectors concurrently for all aspects, and outputting predicted sentiments for each aspect of the sentence to a user interface of a computing device.

Abstract: A data processing system identifies a first topic for a first table, identifies a second topic for a second table, collects at least one first table attribute comprising at least one row name for the first table, and collects at least one second table attribute comprising at least one row name for the second table. The at least one semantic vector for the first table is compared with the at least one semantic vector for the second table to identify as related at least one row of the first table and at least one row of the second table. The at least one row of the first table and the at least one row of the second table are provided to a communication device with an identification as related.

Abstract: Techniques for generating a personality trait model are described. According to an example, a system is provided that can generate text data and linguistic data, and apply psycholinguistic data to the text data and the linguistic data, resulting in updated text data and updated linguistic data. The system is further operable to combine the updated text data with the updated linguistic data to generate a personality trait model. In various embodiments, the personality trait model can be trained and updated as additional data is received from various inputs.

Abstract: Techniques are provided for training, by a system operatively coupled to a processor, an attention weighted recurrent neural network encoder-decoder (AWRNNED) using an iterative process based on one or more paragraphs of agent sentences from respective transcripts of one or more conversations between one or more agents and one or more customers, and based on one or more customer response sentences from the respective transcripts, and generating, by the system, one or more groups respectively comprising one or more agent sentences and one or more customer response sentences selected based on attention weights of the AWRNNED.

Abstract: A computer implemented method, computer system and computer program product are provided for aspect-based sentiment analysis. According to the method, at least one sentence and at least one aspect are received by one or more processing units. At least one opinion snippet of the at least one sentence is determined based on the at least one sentence and the at least one aspect, through a trained aspect-sentence fusion model, by one or more processing units. A sentiment prediction of the at least one opinion snippet is calculated by one or more processing units for the at least one aspect.

Abstract: A method, a device and a computer program for conformity determination of cross-regional affairs. The method comprises obtaining characteristics from a description of an affair at least crossing a local region and a non-local region. The method further comprises generating a multi-level constraint based on the characteristics from a knowledge base, and the knowledge base includes regulations for cross-regional affairs, and the multi-level constraint includes a local constraint associated with the local region and a non-local constraint associated with the local region and the non-local region. Moreover, the method also comprises determining conformity of the affair to the multi-level constraint. The method can determine the conformity of the cross-regional affairs automatically, thereby reducing the consumption of human resources and the inconformity risk in the cross-regional affairs.

Abstract: Techniques for generating a personality trait model are described. According to an example, a system is provided that can generate text data and linguistic data, and apply psycholinguistic data to the text data and the linguistic data, resulting in updated text data and updated linguistic data. The system is further operable to combine the updated text data with the updated linguistic data to generate a personality trait model. In various embodiments, the personality trait model can be trained and updated as additional data is received from various inputs.

Abstract: A method, a device and a computer program product for managing a shared vehicle are proposed. In the method, first information about a user is obtained. The first information indicates a speed of at least one shared vehicle used by the user. A target vehicle is determined from the at least one shared vehicle based on the first information. Second information about the target vehicle is determined. The second information indicates respective speeds of the target vehicle moving at a plurality of time intervals. An abnormal state of the target vehicle is detected based on the second information. The abnormal state indicates that a malfunction occurs in the target vehicle.

Abstract: A method is provided for clause analysis in a legal domain. The method builds a coherence graph from a set of labeled training documents by (a) creating entity nodes from and of a same type as entities extracted from the set of labeled training documents, (b) creating clause nodes from labeled clauses in the set of labeled training documents, (c) forming bi-directional edges (i) between each of the clause nodes and the entity nodes belonging thereto, (ii) among parent-child clause nodes from among the clause nodes, and (iii) among same-level sibling clause nodes from among the clause nodes. The method merges nodes, from among the entity and clause nodes, that have a same semantic meaning. The method weights the bi-directional edges using a coherence metric. The method identifies a clause structure of a new document by matching the new document against the coherence graph using a node-covering algorithm.

Abstract: Techniques are provided for training, by a system operatively coupled to a processor, an attention weighted recurrent neural network encoder-decoder (AWRNNED) using an iterative process based on one or more paragraphs of agent sentences from respective transcripts of one or more conversations between one or more agents and one or more customers, and based on one or more customer response sentences from the respective transcripts, and generating, by the system, one or more groups respectively comprising one or more agent sentences and one or more customer response sentences selected based on attention weights of the AWRNNED.

Abstract: A computer-implemented method, computer program product, and computer processing system are provided for computing a trajectory-based Point of Interest recommendation. The method includes generating, by a processor device, a set of embeddings. Each of the embeddings in the set relates to a respective different trajectory contextual element of a user trajectory. The method further includes computing, by the processor device based on the set of embeddings, an activity representation that includes a set of POI candidate embeddings. The method also includes composing, by the processor device, a stop embedding based on the activity representation and the embeddings in the set and corresponding to a given stop in the user trajectory. The method additionally includes computing, by the processor device, the trajectory-based POI recommendation using an attention-based, user-specific, multi-stop trajectory, Recurrent Neural Network (RNN) model applied to the stop embedding.

Abstract: Embodiments of the present disclosure relate to generation of sentence representation. In an embodiment, a method is disclosed. According to the method, a sentence graph is generated from a sentence containing words, the sentence graph comprising nodes representing the words and edges connecting the nodes to indicate relationships between the words. Word representations for the plurality of words are determined based on the sentence graph by applying a graph convolution operation on respective sets of neighbor nodes for respective ones of the nodes, a set of neighbor nodes for a node having edges connected with the node. A sentence representation for the sentence is determined based on the word representations for use in a natural language processing task related to the sentence. In other embodiments, a system and a computer program product are disclosed.

Abstract: Embodiments of the present disclosure relate to attention-based neural language processing. In an embodiment, a method is disclosed. According to the method, a sentence graph is generated from a sentence containing words. The sentence graph comprises nodes representing words and edges connecting the nodes, at least one of the edges being constructed to indicate a syntactic relationship between words represented by nodes connected therebetween. Word representations for the words are determined based on the sentence graph by applying an attention mechanism on respective ones of the nodes and respective sets of neighbor nodes for the nodes. A set of neighbor nodes for a node has edges connected to the node. A sentence representation for the sentence is determined based on the word representations for use in a natural language processing task related to the sentence. In other embodiments, a system and a computer program product are disclosed.

Abstract: A method, system, and computer program product, include receiving a first input at a first element among a plurality of elements associated with at least one electronic document, determining a second element associated with the first element from the plurality of elements based on predetermined relations of the plurality of elements, and causing a view to be displayed together with an electronic document including the first element, the view at least including the second element.