US 2020/0027566 A1Published application

Identification of co-located artifacts in cognitively analyzed corpora

AssigneeInternational Business Machines Corporation

InventorsBrendan BULL, Paul Lewis FELT, Andrew HICKS

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Abstract

Techniques for cognitive corpora analysis are provided. Vector representations are generated by processing documents in a corpus using a passage encoder. One or more concepts are identified in the documents by processing the documents with the passage encoder, where the concepts are assigned respective importance scores by the passage encoder. Further, a selection of a document is received, and a sub-corpus of documents is generated by computing a similarity measure between the vector representation of the first document and the vector representation of at least one other document in the corpus. An overall importance score is generated for a first concept, with respect to the generated sub-corpus, by identifying a respective importance score of the first concept in at least two respective documents in the sub-corpus, and aggregating the respective importance scores. Finally, an indication of the generated overall importance score is provided.

Claims20

Dependent claims

A method comprising:

generating a plurality of vector representations by processing a plurality of documents in a corpus using a passage encoder, wherein the plurality of vector representations correspond to respective documents of the plurality of documents;
identifying one or more concepts in the plurality of documents by processing the plurality of documents with the passage encoder, wherein the one or more concepts are assigned respective importance scores by the passage encoder;
receiving a selection of a first document in the plurality of documents;
generating a sub-corpus of documents from the corpus by computing a similarity measure between the vector representation of the first document and the vector representation of at least one other document in the plurality of documents in the corpus;
and
generating an overall importance score for a first concept of a first plurality of concepts, with respect to the generated sub-corpus, by:
identifying a respective importance score of the first concept in at least two other documents in the sub-corpus;
and
aggregating the respective importance scores;
and
providing an indication of the generated overall importance score.

8 more elements

A computer program product comprising:

a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation comprising:
generating a plurality of vector representations by processing a plurality of documents in a corpus using a passage encoder, wherein the plurality of vector representations correspond to respective documents of the plurality of documents;
identifying one or more concepts in the plurality of documents by processing the plurality of documents with the passage encoder, wherein the one or more concepts are assigned respective importance scores by the passage encoder;
receiving a selection of a first document in the plurality of documents;
generating a sub-corpus of documents from the corpus by computing a similarity measure between the vector representation of the first document and the vector representation of at least one other document in the plurality of documents in the corpus;
and
generating an overall importance score for a first concept of a first plurality of concepts, with respect to the generated sub-corpus, by:
identifying a respective importance score of the first concept in at least two other documents in the sub-corpus;
and
aggregating the respective importance scores;
and
providing an indication of the generated overall importance score.

9 more elements

A system comprising:

one or more computer processors;
and
a memory containing a program which when executed by the one or more computer processors performs an operation, the operation comprising:
generating a plurality of vector representations by processing a plurality of documents in a corpus using a passage encoder, wherein the plurality of vector representations correspond to respective documents of the plurality of documents;
identifying one or more concepts in the plurality of documents by processing the plurality of documents with the passage encoder, wherein the one or more concepts are assigned respective importance scores by the passage encoder;
receiving a selection of a first document in the plurality of documents;
generating a sub-corpus of documents from the corpus by computing a similarity measure between the vector representation of the first document and the vector representation of at least one other document in the plurality of documents in the corpus;
and
generating an overall importance score for a first concept of a first plurality of concepts, with respect to the generated sub-corpus, by:
identifying a respective importance score of the first concept in at least two other documents in the sub-corpus;
and
aggregating the respective importance scores;
and
providing an indication of the generated overall importance score.

11 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/041,091Filed 20 Jul 2018
Published: 23 Jan 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

G16H 70/20
ICT specially adapted for the handling or processing of medical references: relating to practices or guidelines
G06N 3/0442
Recurrent networks, e.g. Hopfield networks: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
G06N 3/0455
Combinations of networks: auto-encoder networks; Encoder-decoder networks
G06N 20/00
Machine learning