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.