公开(公告)号：US11416684B2

公开(公告)日：2022-08-16

申请号：US16784145

申请日：2020-02-06

Applicant: Adobe Inc.

Inventor： Paridhi Maheshwari ,  Harsh Deshpande ,  Diviya Singh ,  Natwar Modani ,  Srinivas Saurab Sirpurkar

IPC: G06F40/137 ,  G06F40/237 ,  G06F40/279 ,  G06F40/30 ,  G06F40/216 ,  G06V30/416

Abstract: Techniques are described for intelligently identifying concept labels for a set of multiple documents where the identified concept labels are representative of and semantically relevant to the information contained by the set of documents. The technique includes extracting semantic units (e.g., paragraphs) from the set of documents and determining concept labels applicable to the semantic units based on relevance scores computed for the concept labels. The technique includes determining an initial set of concept labels for the set of documents based on the applicable concept labels. The technique further includes obtaining a reference hierarchy associated with the reference set of concept labels and determining a final set of concept labels for the set of documents using a reference hierarchy, the initial set of concept labels, and the relevance scores. The technique includes outputting information identifying the final set of concept labels for the set of documents.

公开(公告)号：US20210248322A1

公开(公告)日：2021-08-12

申请号：US16784000

申请日：2020-02-06

Applicant: Adobe Inc.

Inventor： Natwar Modani ,  Srinivas Saurab Sirpurkar ,  Paridhi Maheshwari ,  Harsh Deshpande ,  Diviya Singh

IPC: G06F40/30 ,  G06F40/216 ,  G06K9/00 ,  G06F9/30

Abstract: A technique for intelligently identifying concept labels for a text fragment where the identified concept labels are representative of and semantically relevant to the information contained by the text fragment is provided. The technique includes determining, using a knowledge base storing information for a reference set of concept labels, a first subset of concept labels that are relevant to the information contained by the text fragment. The technique includes ordering the first subset of concept labels according to their relevance scores and performing dependency analysis on the ordered list of concept labels. Based on the dependency analysis, the technique includes identifying concept labels for a text fragment that are more independent (e.g., more distinct and non-overlapping) of each other, representative of and semantically relevant to the information represented by the text fragment.

公开(公告)号：US11354513B2

公开(公告)日：2022-06-07

申请号：US16784000

申请日：2020-02-06

Applicant: Adobe Inc.

Inventor： Natwar Modani ,  Srinivas Saurab Sirpurkar ,  Paridhi Maheshwari ,  Harsh Deshpande ,  Diviya Singh

IPC: G06F40/279 ,  G06F40/131 ,  G06F40/30 ,  G06F9/30 ,  G06F40/216 ,  G06V30/416

Abstract: A technique for intelligently identifying concept labels for a text fragment where the identified concept labels are representative of and semantically relevant to the information contained by the text fragment is provided. The technique includes determining, using a knowledge base storing information for a reference set of concept labels, a first subset of concept labels that are relevant to the information contained by the text fragment. The technique includes ordering the first subset of concept labels according to their relevance scores and performing dependency analysis on the ordered list of concept labels. Based on the dependency analysis, the technique includes identifying concept labels for a text fragment that are more independent (e.g., more distinct and non-overlapping) of each other, representative of and semantically relevant to the information represented by the text fragment.

公开(公告)号：US20210248323A1

公开(公告)日：2021-08-12

申请号：US16784145

申请日：2020-02-06

Applicant: Adobe Inc.

Inventor： Paridhi Maheshwari ,  Harsh Deshpande ,  Diviya Singh ,  Natwar Modani ,  Srinivas Saurab Sirpurkar

IPC: G06F40/30 ,  G06K9/00 ,  G06F40/216

Abstract: Techniques are described for intelligently identifying concept labels for a set of multiple documents where the identified concept labels are representative of and semantically relevant to the information contained by the set of documents. The technique includes extracting semantic units (e.g., paragraphs) from the set of documents and determining concept labels applicable to the semantic units based on relevance scores computed for the concept labels. The technique includes determining an initial set of concept labels for the set of documents based on the applicable concept labels. The technique further includes obtaining a reference hierarchy associated with the reference set of concept labels and determining a final set of concept labels for the set of documents using a reference hierarchy, the initial set of concept labels, and the relevance scores. The technique includes outputting information identifying the final set of concept labels for the set of documents.