Patent application EP19177419A (EP), filed 2019-05-29
Publication number: EP3745281A1
Title: PROVIDING MACHINE-LEARNING TRAINING DATA FOR A DIFFERENTIATOR MODULE TO IDENTIFY DOCUMENT PROPERTIES
Abstract: A computer (201) provides machine-learning training data (125) for a differentiator module (202) that is adapted to identify a document property (370). The computer (201) receives a first set (100∼0) of reference documents with sentences. The computer (201) uses a selector module (201-A) to select a second set (100∼2) of reference documents, the candidate set being a sub-set with documents that comply with both a topic condition and an argument condition. In application to each reference document of the second set (100∼2), a classifier module classifies the sentences into sentences either with a first sentence attribute (+) or a second sentence attribute (-). The computer provides training data (125) for the differentiator module (202) by providing the sentences with the first sentence attribute (+).
IPC codes: G06F  16/    55            A I
Claims: number: 1
text: Computer-implemented method (400) for providing machine-learning training data (125) for a differentiator module (202) that is adapted to identify a document property (370), the method (400) comprising:
number: 2
text: Method according to claim 1, wherein selecting (420) is performed for the topic condition (C1) with ranking the documents (110-n∼0) in the first set (100∼0) with respect to a given topic (t).
number: 3
text: Method according to claim 2, wherein selecting (420) is performed for the topic condition (C1) with ranking the documents (110-n∼0) in the first set (100∼0) by indexing with an Okapi BM25 tool.
number: 4
text: Method according to any of claims 1 to 3, wherein selecting (420) is performed for the property condition (C2) being a lexical condition.
number: 5
text: Method according to any of claims 1 to 4, wherein selecting (420) is performed for the property condition (C2) by comparing the keyword occurrence number (Key_n(t)) to a pre-defined match parameter (p).
number: 6
text: Method according to claim 5, wherein comparing uses a magnitude relation with the match parameter that (p) being a threshold value.
number: 7
text: Method according to any of the preceding claims, wherein classifying (430) the sentences (120-n-k∼2) into sentences with either a first sentence attribute (+) or a second sentence attribute (-) comprises to classify sentences with the first attribute (+) for sentences that belong to the topic and that comprise arguments.
number: 8
text: Method according to any of the preceding claims, wherein providing (440) the training data comprise to provide sentences with the second sentence attribute (-), wherein the share of the sentences with the second sentence attribute (-) is smaller than the share of the sentences with the first sentence attribute (+).
number: 9
text: Method according to any of the preceding claims, wherein in the selecting step (420), the property condition is a condition selected from the following: argument condition and operation mode condition.
number: 10
text: Computer-system (201) adapted to provide machine-learning training data (125) for a differentiator module (202) that is adapted to identify a document property (370), the computer-system (201) performing a method according any of claims 1-9.
number: 11
text: A computer program product that - when loaded into a memory of a computer and being executed by at least one processor of the computer - performs the steps of the computer-implemented method according to any of claims 1-9.
Office action: search_report, dated 2020-12-02
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ref_type: patent
severity: background
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ref_type: npl
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ref_type: npl
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Final disposition: pending (2020-12-02)