U radu se istražuje toponimija na području sela Lovranska Draga i Visoče. Osnovni korpus toponima dobiven je njihovim ekscerpiranjem iz Popisa katastarskih čestica za katastarsku općinu Tuliševica, a ...pri istraživanju su iskorištene i mogućnosti nekoliko računalnih baza podataka kao korektivnih koraka za točnije ubiciranje toponima na terenu. Korpus je nadopunjen podacima dobivenim od informatora te ekscerpiranim iz literature i arhivskih izvora. Svi su toponimi popisani abecednim redom te obrađeni u zasebnim natuknicama. Nekoliko je razina obrade toponima: 1) geografska: svi su toponimi ubicirani te je pri njihovu opisu prvenstveno u obzir uzeta njihova smještenost u reljefu i eventualna ovisnost određenja o vrsti i naravi zemljišta koje imenuju; 2) povijesna: daje se osvrt na spomen pojedinih toponima u primarnim arhivskim izvorima (oporuke); 3) jezična: za neke je toponime ponuđeno kratko objašnjenje, posebice za one oblikovane u lokalnom idiomu, a svi su i akcentuirani. Radom se daje novi prilog dosad veoma slabo istraženoj toponimiji Lovranšćine.
ON THE ORIGIN OF THE TOPONYM RAJGRÓD
The article focuses on the etymological analysis of the place-name Pol. Rajgród ‘a town in Grajewo County, Podlaskie Voivodeship’. It has been stated that the ...stronghold known as the “Castle Hill” in Rajgród was founded by Yotvingians. Referring to the etymological analysis of the West Germanic forms of the analysed onym and its variants, such as Rongart 1360 etc., the author of the article presents a new hypothesis concerning the origin of Pol. Rajgród. In summing up the research results, one may conclude that the toponym Pol. Rajgród is to be ascribed to adapted place-names of Germanic origin, i. e. (1) CS Раи1253 ← top. *Rain / *Rein (/ *Reyn) ‘a frontier’ ↔ top. MLG †Reyne 1292, 1293, 1489; (2) Raygrod 1244, Reygrod 1429 etc. (↔ Rajgród) ← *Reingarden / *Reingart(h) ‘a frontier area’; (3) Rongart 1360, Rogors 1422 etc. ← sub. MLG ronne(bôm) ‘a border pole, i.e. a frontier, a march’.
Na temelju toponomastickih i leksikografskih podataka od 13. stoljeca naovamo, analizira se položaj
jezicnih inacica ‘jalša’ i ‘joha’ (u znacenju ‘Alnus’; ‘Alnus glutinosa’) u hrvatskom jezicnom ...prostoru.
Toponomastickim potvrdama utvrdeno je da je sve do 17. stoljeca kao standardna prevladavala inacica
‘jalša’, koja je slijedom krupnih migracijskih promjena, a u vezi s turskim osvajanjima, potisnuta na krajnji
sjever i zapad Hrvatske. Kao posljedica toga, ‘joha’ je sasvim istisnula ‘jalšu’ iz rjecnika jezicnog
standarda, te ‘jalša’ danas opstaje jedino kao dijalektizam i zastarjeli izraz. Ovaj je slucaj obraden kao
primjer jezicnog razvoja pod utjecajem migracija. Istovremeno, mada ima i primjera da se stari toponimi
prilagodavaju pritisku jezicnih promjena, toponomastika uglavnom uspijeva konzervirati izvorne oblike, te
kao takva postaje vrijedan jezicni spomenik.
U radu je obrađena toponimija dijela Splitske zagore - kraja u sjevernom zaleđu Splita. Toponimijska građa najvećim je dijelom prikupljena na području općine Lećevica i to terenskim istraživanjima, ...analizama katastarskih i topografskih karata te razgovorima s lokalnim stanovništvom.
Natural language texts, such as tweets and news, contain a vast amount of geospatial information, which can be extracted by first recognizing toponyms in texts (toponym recognition) and then ...identifying their geospatial representations (toponym disambiguation). This paper focuses on toponym disambiguation, which can be approached by toponym resolution and entity linking. Recently, many novel approaches, especially deep learning-based, have been proposed, such as CamCoder, GENRE, and BLINK. However, these approaches were not compared on the same and large datasets. Moreover, there is still a need and space to improve their robustness and generalizability further. To mitigate the two research gaps, in this paper, we propose a spatial clustering-based voting approach combining several individual approaches and compare a voting ensemble with 20 latest and commonly-used approaches based on 12 public datasets, including several highly challenging datasets (e.g., WikToR). They are in six types: tweets, historical documents, news, web pages, scientific articles, and Wikipedia articles, containing 98,300 toponyms. Experimental results show that the voting ensemble performs the best on all the datasets, achieving an average Accuracy@161km of 0.86, proving its generalizability and robustness. It also drastically improves the performance of resolving fine-grained places, i.e., POIs, natural features, and traffic ways. The detailed evaluation results can inform future methodological developments and guide the selection of proper approaches based on application needs.
•A spatial clustering-based voting approach is proposed to disambiguate toponyms.•The voting approach is compared with the 20 latest or commonly-used approaches.•12 datasets of six text types with 98,300 toponyms are used as benchmark data.•The voting approach is robust and general, performing best on every dataset.
