Nada Lavrač
Biographic Data
| ID | 7502091 |
|---|---|
| NAME | Nada Lavrač |
| GIVEN NAMES | Nada |
| FAMILY NAME | Lavrač |
| SIGNATURE | LAVRAČ N |
| AFFILIATIONS | Jožef Stefan Institute |
| ORCID | 0000-0002-9995-7093 |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
TermEnsembler
This paper describes TermEnsembler, a bilingual term extraction and alignment system utilizing a novel ensemble learning approach to bilingual term alignment. In the proposed system, the processing starts with monolingual term extraction from a language industry standard file type containing aligned English and Slovenian texts. The two separate term lists are then automatically aligned using an ensemble of seven bilingual alignment methods, which…
Detecting contrast patterns in newspaper articles by combining discourse analysis and text mining
Text mining aims at constructing classification models and finding interesting patterns in large text collections. This paper investigates the utility of applying these techniques to media analysis, more specifically to support discourse analysis of news reports about the 2007 Kenyan elections and post-election crisis in local (Kenyan) and Western (British and US) newspapers. It illustrates how text mining methods can assist discourse analysis by…
No prominent works on this page.
TermEnsembler
This paper describes TermEnsembler, a bilingual term extraction and alignment system utilizing a novel ensemble learning approach to bilingual term alignment. In the proposed system, the processing starts with monolingual term extraction from a language industry standard file type containing aligned English and Slovenian texts. The two separate term lists are then automatically aligned using an ensemble of seven bilingual alignment methods, which…
Detecting contrast patterns in newspaper articles by combining discourse analysis and text mining
Text mining aims at constructing classification models and finding interesting patterns in large text collections. This paper investigates the utility of applying these techniques to media analysis, more specifically to support discourse analysis of news reports about the 2007 Kenyan elections and post-election crisis in local (Kenyan) and Western (British and US) newspapers. It illustrates how text mining methods can assist discourse analysis by…
Artificial Intelligence (2 works) · Computer Science (2 works) · Topic Modeling (2 works) · Authorship Attribution and Profiling (1 work) · Biomedical Text Mining and Ontologies (1 work) · Content analysis (1 work) · Contrast (vision (1 work) · Discourse analysis (1 work) · Ethnic group (1 work) · Focus (optics (1 work)