# US 12,001,896 B2 · Unsupervised event extraction > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,001,896 B2 - Document ID: US12001896B2 - Status: Granted - Kind: B2 - Application number: 18/296,777 - Filed: 2023-04-06 - Published: 2023-08-03 - Granted: 2024-06-04 - Earliest claimed: 2020-05-21 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Rajarshi Haldar; Yu Deng; Lingfei Wu; Ruchi Mahindru; Shu Tao - Web page: https://patentgrep.com/patent/US12001896B2 ## CPC - G06F 9/542: {Event management; Broadcasting; Multicasting; Notifications} - G06F 40/30: Semantic analysis - G06F 16/35: Clustering; Classification - G06F 16/38: Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually - G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)} - G06F 40/205: Parsing - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 20/00: Machine learning - G06F 16/243: {Natural language query formulation} - G06N 3/045: Combinations of networks - G06N 3/047: Probabilistic or stochastic networks - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Computer-implemented techniques for unsupervised event extraction are provided. In one instance, a computer implemented method can include parsing, by a system operatively coupled to a processor, unstructured text comprising event information to identify candidate event components. The computer implemented method can further include employing, by the system, one or more unsupervised machine learning techniques to generate structured event information defining events represented in the unstructured text based on the candidate event components. ## Claims ### Claim 1 A system, comprising: a memory that stores computer executable components; a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a parsing component that identifies event components included in an unstructured text query using one or more unsupervised machine learning processes; and an event extraction component that generates structured event information defining one or more events represented in the unstructured text query based on the event components. ### Claim 2 (depends on 1) The system of claim 1, wherein the computer executable components further comprise: a query component that queries the structured event information against structured event schema for a corpus of unstructured text data and identifies one or more parts of the unstructured text data that are relevant to a query request based on a correspondence between the structured event information and reference structured event information included in the structured event schema and associated with the one or more parts. ### Claim 3 (depends on 2) The system of claim 2, wherein the query component further generates query result data for the unstructured text query identifying the one or more parts. ### Claim 4 (depends on 2) The system of claim 2, wherein the event extraction component generates the structured event schema for the corpus of unstructured text using the one or more unsupervised machine learning processes. ### Claim 5 (depends on 1) The system of claim 1, wherein the structured event information defines respective events of the one or more events using a trigger term, one or more argument terms, one or more roles of the arguments and an event type. ### Claim 6 (depends on 1) The system of claim 1, wherein the computer executable components further comprise: an event representation component that generates one or more event representations using graph embeddings based on the event components. ### Claim 7 (depends on 6) The system of claim 6, wherein the event components comprise one or more event trigger terms and one or more candidate event arguments respectively associated with the one or more event trigger terms, and wherein the event representations comprise an event representation for each of the one or more event trigger terms. ### Claim 8 (depends on 6) The system of claim 6, wherein the computer executable components further comprise: a clustering component that employs the one or more event representations to cluster the event components into one or more event types, and wherein the event extraction component generates the structured event information based on the one or more event types and the event components respectively grouped with the one or more event types. ### Claim 9 (depends on 8) The system of claim 8, wherein the event components comprise one or more event trigger terms and one or more event arguments respectively associated with the one or more event trigger terms, and wherein the computer executable components further comprise: a role labeling component that labels the one or more event arguments with one or more role attributes representative of one or more roles the one or more event arguments play with respect to the one or more event types, wherein the event extraction component generates the structured event information based on the one or more event types, the one or more event trigger terms respectively associated with the one or more event types, the one or more event arguments respectively associated with the one or more event trigger terms, and the one or more role attributes. ### Claim 10 (depends on 9) The system of claim 9, wherein the role labeling component employs one or more external knowledge bases to facilitate labeling the one or more event arguments with the one or more role attributes. ### Claim 11 (depends on 1) The system of claim 1, wherein the one or more unsupervised machine learning processes comprise abstract meaning representation parsing. ### Claim 12 A method, comprising: identifying, by a system operatively coupled to a processor, event components included in an unstructured text query using one or more unsupervised machine learning processes; and generating, by the system, structured event information defining one or more events represented in the unstructured text query based on the event components. ### Claim 13 (depends on 12) The method of claim 12, further comprising: querying, by the system, the structured event information against structured event schema for a corpus of unstructured text data; and identifying, by the system, one or more parts of the unstructured text data that are relevant to a query request based on a correspondence between the structured event information and reference structured event information included in the structured event schema and associated with the one or more parts. ### Claim 14 (depends on 13) The method of claim 13, further comprising: generating, by the system, query result data for the unstructured text query identifying the one or more parts. ### Claim 15 (depends on 12) The method of claim 12, further comprising: generating, by the system, the structured event schema for a corpus of unstructured text using the one or more unsupervised machine learning processes. ### Claim 16 (depends on 12) The method of claim 12, wherein the structured event information defines respective events of the one or more events using a trigger term, one or more argument terms, one or more roles of the arguments and an event type. ### Claim 17 (depends on 12) The method of claim 12, further comprising: generating, by the system, one or more event representations using graph embeddings based on the event components; employing, by the system, the one or more event representations to cluster the event components into one or more event types; and generating, by the system, the structured event information based on the one or more event types and the event components respectively grouped with the one or more event types. ### Claim 18 (depends on 17) The method of claim 17, wherein the event components comprise one or more event trigger terms and one or more event arguments respectively associated with the one or more event trigger terms, and wherein the method further comprises: labeling, by the system, the one or more event arguments with one or more role attributes representative of one or more roles the one or more event arguments play with respect to the one or more event types; and generating, by the system, the structured event information based on the one or more event types, the one or more event trigger terms respectively associated with the one or more event types, the one or more event arguments respectively associated with the one or more event trigger terms, and the one or more role attributes. ### Claim 19 A computer program product for unsupervised event extraction, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing component to cause the processing component to: identify event components included in an unstructured text query using one or more unsupervised machine learning processes; and generate structured event information defining one or more events represented in the unstructured text query based on the event components. ### Claim 20 (depends on 19) The computer program product of claim 19, the program instructions further executable by the processing component to cause the processing component to: query the structured event information against structured event schema for a corpus of unstructured text data; and identify one or more parts of the unstructured text data that are relevant to a query request based on a correspondence between the structured event information and reference structured event information included in the structured event schema and associated with the one or more parts. ### Claim 21 A system, comprising: a memory that stores computer executable components; a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: an event extraction component that generates structured event schema defining different events represented in a corpus of unstructured text data using one or more unsupervised machine learning processes; and a query component that receives an unstructured text query and employs the structured event schema to identify one or more parts of the corpus of unstructured text data that are relevant to the unstructured text query based on a correspondence between reference structured event data associated with the one or more parts as defined in the structured event schema and structured event data generated for the unstructured text query by the event extraction component using the one or more unsupervised machine learning processes. ### Claim 22 (depends on 21) The system of claim 21, wherein the event extraction component generates the structured event schema based on candidate event components extracted from the corpus of unstructured text data using at least one unsupervised machine learning process of the one or more unsupervised machine learning processes. ### Claim 23 (depends on 21) The system of claim 21, wherein the structured event schema defines respective events of the different events using a trigger term, one or more argument terms, one or more roles of the arguments and an event type. ### Claim 24 A method, comprising: generating, by a system operatively coupled to a processor, structured event schema defining different events represented in a corpus of unstructured text data using one or more unsupervised machine learning processes; receiving, by the system, unstructured text query; converting, by the system, the unstructured text query into structured event data using the one or more unsupervised machine learning processes; and identifying, by the system, one or more parts of the corpus of unstructured text data that are relevant to the unstructured text query based on a correspondence between reference structured event data associated with the one or more parts as defined in the structured event schema and the structured event data. ### Claim 25 (depends on 24) The method of claim 24, wherein the generating comprises generating the structured event schema based on candidate event components extracted from the corpus of unstructured text data using at least one unsupervised machine learning process of the one or more unsupervised machine learning processes.