Napredna pretraga

Pregled bibliografske jedinice broj: 926296

Keyword Extraction from Parallel Abstracts of Scientific Publications


Beliga, Slobodan; Kitanović, Olivera; Stanković, Ranka; Martinčić-Ipšić, Sanda
Keyword Extraction from Parallel Abstracts of Scientific Publications // Semantic Keyword-Based Search on Structured Data Sources. Third International KEYSTONE Conference, IKC 2017, Gdańsk, Poland, September 11-12, 2017, Revised Selected Papers and COST Action IC1302 Reports. / Szymański, Julian ; Velegrakis, Yannis (ur.).
Cham: Springer International Publishing, 2018. str. 44-55 doi:10.1007/978-3-319-74497-1_5


Naslov
Keyword Extraction from Parallel Abstracts of Scientific Publications

Autori
Beliga, Slobodan ; Kitanović, Olivera ; Stanković, Ranka ; Martinčić-Ipšić, Sanda

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni

Knjiga
Semantic Keyword-Based Search on Structured Data Sources. Third International KEYSTONE Conference, IKC 2017, Gdańsk, Poland, September 11-12, 2017, Revised Selected Papers and COST Action IC1302 Reports.

Urednik/ci
Szymański, Julian ; Velegrakis, Yannis

Izdavač
Springer International Publishing

Grad
Cham

Godina
2018

Raspon stranica
44-55

ISBN
978-3-319-74496-4

Ključne riječi
Graph-based keyword extraction ; Bilingual keyword extraction ; SBKE method ; Parallel abstracts

Sažetak
In this paper, we study the keyword extraction from parallel abstracts of scientific publication in the Serbian and English languages. The keywords are extracted by a selectivity-based keyword extraction method. The method is based on the structural and statistical properties of text represented as a complex network. The constructed parallel corpus of scientific abstracts with annotated keywords allows a better comparison of the performance of the method across languages since we have the controlled experimental environment and data. The achieved keyword extraction results measured with an F1 score are 49.57% for English and 46.73% for the Serbian language, if we disregard keywords that are not present in the abstracts. In case that we evaluate against the whole keyword set, the F1 scores are 40.08% and 45.71% respectively. This work shows that SBKE can be easily ported to new a language, domain and type of text in the sense of its structure. Still, there are drawbacks – the method can extract only the words that appear in the text.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove
Sveučilište u Rijeci - Odjel za informatiku

Časopis indeksira:


  • Scopus


Citati