Abstract: A method and an apparatus for optimizing diagnostics of rotating equipment is provided. The apparatus includes a device for providing status information about status of the rotating equipment over a series of time windows whereby status can be derived from sensor features of at least one available sensor taking measurements during a predefinable time period, a device for using deep learning which combines provided historic sensor information with sequence of events data indicating warnings and/or alerts of the rotating equipment, whereby status information is supplemented with via deep learning predicted probabilities whether a warning and/or an alert has occurred within a time window, device for providing an amount of textual diagnostic knowledge cases, device for extracting semantic information on text features from the textual diagnostic knowledge cases, and device for combining status information and semantic information into a unified representation enabling optimization of the diagnostics.

Abstract: A method and system for automatically performing a discovery of topics within temporal ordered text document collections are provided. The method includes generating a bag of words vector for each text document collection using a predefined dictionary. The method also includes iteratively calculating, based on the generated bag of words vectors, for each text document collection, a hidden topic vector representing topics of the respective text document collection using a calculated hidden state vector memorizing a hidden state of all previous text document collections.

Abstract: Provided is a computer-implemented method for inter-sententially determining a semantic relationship between a first entity and a second entity in a natural language document, comprising at least the steps of: generating a first dependency parse tree, DPT, for a first origin sentence of the document which comprises the first entity, wherein each DPT comprises at least a root node; generating a second DPT for a second origin sentence of the document which mentions the second entity; linking the root nodes of the first DPT and the second DPT so as to create a chain of words, COW; determining for each word in the COW a subtree; generating for each word in the COW a subtree embedding vector cw which is based at least on word embedding vectors xw of the words of the subtree; generating a representation vector pw for each word in the COW; and classifying, using a recurrent neural network, the semantic relationship between the first entity and the second entity, based on the input representation vectors pw.

Abstract: Provided is a computer-implemented method for inter-sententially determining a semantic relationship between a first entity and a second entity in a natural language document, comprising at least the steps of: generating a first dependency parse tree, DPT, for a first origin sentence of the document which comprises the first entity, wherein each DPT comprises at least a root node; generating a second DPT for a second origin sentence of the document which mentions the second entity; linking the root nodes of the first DPT and the second DPT so as to create a chain of words, COW; determining for each word in the COW a subtree; generating for each word in the COW a subtree embedding vector cw which is based at least on word embedding vectors xw of the words of the subtree; generating a representation vector pw for each word in the COW; and classifying, using a recurrent neural network, the semantic relationship between the first entity and the second entity, based on the input representation vectors pw.

Abstract: A method and system for automatically performing a discovery of topics within temporal ordered text document collections are provided. The method includes generating a bag of words vector for each text document collection using a predefined dictionary. The method also includes iteratively calculating, based on the generated bag of words vectors, for each text document collection, a hidden topic vector representing topics of the respective text document collection using a calculated hidden state vector memorizing a hidden state of all previous text document collections.

Abstract: A Semantic Textual Similarity System comprising a first Long Short Term Memory, LSTM, branch adapted to be operative, to determine text similarity, on a first text corpus, the first text corpus comprising a plurality of first text elements; wherein each first text element has a first number of distinct subdivisions. The system also comprises a second LSTM branch adapted to be operative, to determine text similarity, on a second text corpus, the second text corpus comprising a plurality of second text elements, wherein each second text element has a second number of distinct subdivisions.

Abstract: Provided is a ticketing system adapted to retrieve a recommendation from a knowledge database in response to a received query, the ticketing system including a processor adapted to perform semantic similarity learning in textual description pairs by calculating similarity scores for similarities between the received query and tickets stored in the knowledge database of the ticketing system, wherein each textual description pair includes a textual description of the received query and a textual description of a ticket of a plurality of tickets stored in the knowledge database of the ticketing system, wherein the ticket having the maximum similarity score is identified and a solution of the identified ticket is output as the retrieved recommendation for the received query by the ticketing system.

Abstract: A device for relation extraction in a natural language sentence having n words is suggested, the device comprising: a recurrent neural network for joint entity and relation extractions of entities and relations between the entities in the sentence, and an entity-relation table for storing entity labels for the entities and relation labels for the relations, wherein both the entity labels and the relation labels are defined as instances of binary relationships between certain words wi and wj in the sentence, with i?[1, . . . , n], and j?[1, . . . , n], wherein each of the entity labels is a first binary relationship for i=j, and wherein each of the relation labels is a second binary relationship for i?j, wherein the recurrent neural network is configured to fill the cells of the entity-relation table and includes a forward neural network and a backward neural network.

Abstract: A Semantic Textual Similarity System comprising a first Long Short Term Memory, LSTM, branch adapted to be operative, to determine text similarity, on a first text corpus, the first text corpus comprising a plurality of first text elements; wherein each first text element has a first number of distinct subdivisions. The system also comprises a second LSTM branch adapted to be operative, to determine text similarity, on a second text corpus, the second text corpus comprising a plurality of second text elements, wherein each second text element has a second number of distinct subdivisions.

Abstract: A method and an apparatus for optimizing diagnostics of rotating equipment, in particular a gas turbine is provided.

Abstract: A device for relation extraction in a natural language sentence having n words is suggested, the device comprising: a recurrent neural network for joint entity and relation extractions of entities and relations between the entities in the sentence, and an entity-relation table for storing entity labels for the entities and relation labels for the relations, wherein both the entity labels and the relation labels are defined as instances of binary relationships between certain words wi and wj in the sentence, with i?[1, . . . , n], and j?[1, . . . , n], wherein each of the entity labels is a first binary relationship for i=j, and wherein each of the relation labels is a second binary relationship for i?j, wherein the recurrent neural network is configured to fill the cells of the entity-relation table and includes a forward neural network and a backward neural network.

Abstract: Answering machines and voice mailbox systems are normally provided in modern communications systems. When a communications subscriber is temporarily unreachable at the location of their fixed network telephone, incoming calls can be accepted by an answering machine and voice messages can be recorded. The drawback of this procedure is that messages containing important information cannot be promptly conveyed to the communications subscriber. During incoming voice messages, their content is evaluated with the aid of a speech recognition system. As a result, content-based prioritization of voice messages is made possible in a communications system and the receiver is notified of the incoming voice messages according to the prioritization.

Abstract: A voice-based classification method authenticates a user for a service which is reserved for a predetermined user group and is provided via a communication link in response to a request received from the user over a first communication link in an access control unit for access to the service. Over a second communication link a voice connection between the user and a speech processing unit is established. In the speech processing unit the voice of the user is recorded using a speech sample and at least a first criterion is checked, with the first criterion being fulfilled if the user is assigned to the predetermined user group. An age and or gender classification method may be used, with the predetermined user group being specified by its age and its gender. The service may be Internet-based, for example a chat room, in which persons of a specific age group interact.