Extracting the Component Composition Data of Inventions from Russian Patents using Dependency Tree Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AP85HG2W3" target="_blank" >RIV/00216208:11320/23:P85HG2W3 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162852443&doi=10.1109%2fICIEAM57311.2023.10139170&partnerID=40&md5=1ba465dae2c137c40b44d92621dc4334" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162852443&doi=10.1109%2fICIEAM57311.2023.10139170&partnerID=40&md5=1ba465dae2c137c40b44d92621dc4334</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/icieam57311.2023.10139170" target="_blank" >10.1109/icieam57311.2023.10139170</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Extracting the Component Composition Data of Inventions from Russian Patents using Dependency Tree Analysis

Popis výsledku v původním jazyce

"The paper presents a methodology for extracting device components and relationships between them from the Russian-language patent claims. Information about the components of the device is the most useful and important part. It can be used in various tasks of patent analysis. The objective of this study is to evaluate the the quality of data extraction using dependency tree analysis for Russian language. The dependency tree for a sentence is the result of syntactic parsing by natural language processing tools. There are several parsers were chosen for comparison: UdPipe, Stanza, DeepPavlov, spaCy and Trankit. The output data are presented in the form of SAO structures (Subject-Action-Object). The quality of data extraction has been evaluated using precision, recall and F1 metrics. For this purpose, 20 patent claims with 252 SAO structures were manually marked. Under the current methodological constraints, on the test dataset, at best we are able to extract 81% of the SAO structures according to the recall metric with a non-strict data evaluation, i.e. without considering the completeness of a noun phrases. The F1-measure is lower and ranges from 48% to 69% depending on evaluation type. The current level of parsers efficiency in the investigated area is summarized. The results can be useful for developing efficient approaches to extracting structured data from patent arrays. © 2023 IEEE."

Název v anglickém jazyce

Extracting the Component Composition Data of Inventions from Russian Patents using Dependency Tree Analysis

Popis výsledku anglicky

"The paper presents a methodology for extracting device components and relationships between them from the Russian-language patent claims. Information about the components of the device is the most useful and important part. It can be used in various tasks of patent analysis. The objective of this study is to evaluate the the quality of data extraction using dependency tree analysis for Russian language. The dependency tree for a sentence is the result of syntactic parsing by natural language processing tools. There are several parsers were chosen for comparison: UdPipe, Stanza, DeepPavlov, spaCy and Trankit. The output data are presented in the form of SAO structures (Subject-Action-Object). The quality of data extraction has been evaluated using precision, recall and F1 metrics. For this purpose, 20 patent claims with 252 SAO structures were manually marked. Under the current methodological constraints, on the test dataset, at best we are able to extract 81% of the SAO structures according to the recall metric with a non-strict data evaluation, i.e. without considering the completeness of a noun phrases. The F1-measure is lower and ranges from 48% to 69% depending on evaluation type. The current level of parsers efficiency in the investigated area is summarized. The results can be useful for developing efficient approaches to extracting structured data from patent arrays. © 2023 IEEE."

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

"Proc. - Int. Conf. Ind. Eng., Appl. Manuf., ICIEAM"

ISBN

978-166547595-2

ISSN

—

e-ISSN

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Počet stran výsledku

5

Strana od-do

1030-1034

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

—

Místo konání akce

Cham

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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