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Pregled bibliografske jedinice broj: 1082198

ConGraCNet 0.3: Corpus-based graph syntactic- semantic relations analysis


Perak, Benedikt; Ban Kirigin, Tajana
ConGraCNet 0.3: Corpus-based graph syntactic- semantic relations analysis // Book of Abstracts LAP 2020
Dubrovnik, 2020. str. 53-54 (predavanje, međunarodna recenzija, prošireni sažetak, znanstveni)


CROSBI ID: 1082198 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
ConGraCNet 0.3: Corpus-based graph syntactic- semantic relations analysis

Autori
Perak, Benedikt ; Ban Kirigin, Tajana

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, prošireni sažetak, znanstveni

Izvornik
Book of Abstracts LAP 2020 / - Dubrovnik, 2020, 53-54

Skup
Logic and Applications, FORMALS 2020

Mjesto i datum
Dubrovnik, Hrvatska, 21.09.2020. - 25.09.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Natural Language Processing ; Conceptual Similarity ; Word Sense Induction ; Corpus

Sažetak
The paper will demonstrate the ConGraCNet application for distinguishing word senses and identifying semantically related lexemes in a corpus by using the syntactic-semantic patterns of language usage. This unsupervised tagged corpus graph analysis method is based on the construction grammar approach to syntactic dependencies. ConGraCNet relies explicitly on the coordinated [x and|or y] and [x is_a y] syntactic grammatical relations between the lexical co- occurrences for the construction of the network representation. For a given source lexeme in a corpus, the method yields associated communities of collocation lexemes that represent the sense structure and different meanings based on the context of its usage. By projecting semantic value to a coordinated syntactical relation [x and|or y], we can filter the lexical collocates with high conceptual similarity from a corpus and construct clustered lexical networks that reveal ambiguous referential meanings of a source lexeme. The members of a cluster are processed with an iterative graph function that finds best candidates for abstracted class label using [x is_a y] syntactic-semantic construction. We will explain the impact of the modulation of the linguistic and graph parameters, exemplify the application of the procedure on several lexemes in different languages and corpora and present the implementation of the WordNet external knowledge databases for further refinement of the results.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti, Filologija



POVEZANOST RADA


Projekti:
HRZZ-UIP-2017-05-9219 - Formalno rasuđivanje i semantike (FORMALS) (Perkov, Tin, HRZZ - 2017-05) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-human-18-243 - Jezično izražavanje emocija: Razvoj računalnih metoda identifikacije i ontološkog modeliranja komunikacije psiholoških stanja u hrvatskom jeziku (EmoCNet) (EmoCNet) (Perak, Benedikt, NadSve - UNIRI Sredstva potpore znanstvenim istraživanjima) ( CroRIS)

Ustanove:
Filozofski fakultet, Rijeka,
Sveučilište u Rijeci, Fakultet za matematiku

Profili:

Avatar Url Tajana Ban Kirigin (autor)

Avatar Url Benedikt Perak (autor)

Poveznice na cjeloviti tekst rada:

drive.google.com imft.ftn.uns.ac.rs

Citiraj ovu publikaciju:

Perak, Benedikt; Ban Kirigin, Tajana
ConGraCNet 0.3: Corpus-based graph syntactic- semantic relations analysis // Book of Abstracts LAP 2020
Dubrovnik, 2020. str. 53-54 (predavanje, međunarodna recenzija, prošireni sažetak, znanstveni)
Perak, B. & Ban Kirigin, T. (2020) ConGraCNet 0.3: Corpus-based graph syntactic- semantic relations analysis. U: Book of Abstracts LAP 2020.
@article{article, author = {Perak, Benedikt and Ban Kirigin, Tajana}, year = {2020}, pages = {53-54}, keywords = {Natural Language Processing, Conceptual Similarity, Word Sense Induction, Corpus}, title = {ConGraCNet 0.3: Corpus-based graph syntactic- semantic relations analysis}, keyword = {Natural Language Processing, Conceptual Similarity, Word Sense Induction, Corpus}, publisherplace = {Dubrovnik, Hrvatska} }
@article{article, author = {Perak, Benedikt and Ban Kirigin, Tajana}, year = {2020}, pages = {53-54}, keywords = {Natural Language Processing, Conceptual Similarity, Word Sense Induction, Corpus}, title = {ConGraCNet 0.3: Corpus-based graph syntactic- semantic relations analysis}, keyword = {Natural Language Processing, Conceptual Similarity, Word Sense Induction, Corpus}, publisherplace = {Dubrovnik, Hrvatska} }




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