| Autor: |
Nazar Rogelio, Balvet Antonio, Ferraro Gabriela, Marín Rafael, Renau Irene |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Journal of Intelligent Systems, Vol 30, Iss 1, Pp 376-394 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2191-026X |
| DOI: |
10.1515/jisys-2020-0044 |
| Popis: |
In this paper we present the problem of a noisy lexical taxonomy and suggest two tasks as potential remedies. The first task is to identify and eliminate incorrect hypernymy links, and the second is to repopulate the taxonomy with new relations. The first task consists of revising the entire taxonomy and returning a Boolean for each assertion of hypernymy between two nouns (e.g. brie is a kind of cheese). The second task consists of recursively producing a chain of hypernyms for a given noun, until the most general node in the taxonomy is reached (e.g. brie → cheese → food → etc.). In order to achieve these goals, we implemented a hybrid hypernym-detection algorithm that incorporates various intuitions, such as syntagmatic, paradigmatic and morphological association measures as well as lexical patterns. We evaluate these algorithms individually and collectively and report findings in Spanish, English and French. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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