Patent application EP20305634A (EP), filed 2020-06-10
Publication number: EP3923206A1
Title: A METHOD AND SYSTEM FOR FINE-GRAINED INFORMATION EXTRACTION AND ANNOTATION USING SEMANTIC ATTRIBUTE PATHS
Abstract: [0001]    An annotation assistant 20 for semi-automatic creation of annotated machine learning training data is disclosed. The annotation assistant 20 comprises a graphical annotation interface 22 for displaying a sequence of tokens to be annotated and for allowing a span selection 224 by a user, and an automatic span tagger 24. The automatic span tagger 24 comprises an artificial neural network 242 configured to assign class probabilities to the tokens of the sequence of tokens, the class probabilities for a token corresponding to probabilities for the token to fall under respective classes of a predefined ontology. The automatic span 24 tagger further comprises a path-scoring algorithm 244 for computing scores for semantic attribute paths of the span selection 224 based on the class probabilities, wherein the semantic attribute paths correspond to paths in the predefined ontology. The graphical annotation interface 22 is further configured for displaying the scored semantic attribute paths, and for receiving a selection among the scored semantic attribute paths from the user.
IPC codes: G06N   3/    08            A I
Claims: number: 1
text: A method (300) of pre-training an artificial neural network for use in fine-grained information extraction, the method comprising:
number: 2
text: The method (300) of claim 1, further comprising optimizing the number (k) of least similar vocabulary tokens.
number: 3
text: The method (300) of claim 1 or claim 2, wherein the predefined ontology contains a first number of top classes and a second number of semantic classes.
number: 4
text: An annotation method (400) for semi-automatic creation of annotated machine learning training data employing an artificial neural network pre-trained according to the method of one of claims 1 to 3, the annotation method comprising:
number: 5
text: The annotation method (400) of claim 4, further comprising re-training (414) the artificial neural network employing the stored machine learning training data.
number: 6
text: The annotation method (400) of claim 4 or claim 5, wherein computing (406) the scores for semantic attribute paths of the span selection is based on the class probabilities for the tokens of the span selection and on the class probabilities of tokens surrounding the tokens of the span selection.
number: 7
text: The annotation method (400) of one of claims 4 to 6, wherein receiving (410) the user selection of a semantic attribute path comprises receiving a user input indicating acceptance of a semantic attribute path ranked highest in the list of semantic attribute paths or receiving a selection of an alternative semantic attribute path from the list of semantic attribute paths.
number: 8
text: The annotation method (400) of claim 7, wherein receiving the user input indicating acceptance of the semantic attribute path ranked highest comprises receiving another span selection.
number: 9
text: The annotation method (400) of one of claims 4 to 8, wherein the sequence of tokens corresponds to a user generated comment.
number: 10
text: The annotation method (400) of claim 9, wherein the user generated comment relates to a point of interest.
number: 11
text: The annotation method (400) of claim 9, wherein the user generated comment relates to a retailer.
number: 12
text: An annotation assistant (20) for semi-automatic creation of annotated machine learning training data, the annotation assistant (20) comprising:
number: 13
text: The annotation assistant (20) of claim 12, wherein the graphical annotation interface comprises a train button to initiate re-training of the artificial neural network employing the stored machine learning training data.
number: 14
text: The annotation assistant (20) of claim 12 or claim 13, wherein the path-scoring algorithm (244) is configured to compute the scored semantic attribute paths for the span selection based on the class probabilities by employing the class probabilities of the tokens in the span selection and the class probabilities of tokens surrounding the tokens of the span selection as optimal paths in the predefined ontology.
number: 15
text: The annotation assistant (20) of one of claims 12 to 14, wherein the artificial neural network (242) is a multi-label multi-class classification neural network trained for classifying spans of texts into the classes.
Office action: search_report, dated 2021-12-15
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Final disposition: pending (2021-12-15)