| Autor: |
Mozhgan Saeidi, Kaveh Mahdaviani, Evangelos Milios, Norbert Zeh |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Intelligent Systems with Applications, Vol 19, Iss , Pp 200246- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2667-3053 |
| DOI: |
10.1016/j.iswa.2023.200246 |
| Popis: |
Wikification is a method to automatically enrich a text with links to Wikipedia as a knowledge base. One step in Wikification is detecting ambiguous mentions, and one other step is disambiguating those mentions.In this paper, we worked on the mention disambiguation problem. Some state-of-the-art disambiguation approaches have divided long input document text into non-overlapping windows. Later, for each ambiguous mention, they pick the most similar sense to the chosen meaning of the key-entity (a word that helps disambiguation other words of the text). Partitioning the input into disjoint windows means that the most appropriate key-entity to disambiguate a given mention may be in an adjacent window. The disjoint windows negatively affect the accuracy of these methods. This work presents CACW (Context-Aware Concept Wikifier), a knowledge-based approach to produce the correct meaning for ambiguous mentions in the document. CACW incorporates two algorithms; the first uses co-occurring mentions in consecutive windows to augment the available contextual information to find the correct sense. The second algorithm ranks senses based on their context relevancy. We also define a new metric for disambiguation to measure the coherence of the whole text document. Comparing our approach with state-of-the-art methods shows the effectiveness of our method in terms of text coherence in the English Wikification task. We observed between 10-20 percent improvement in the F1 measure compared to the state-of-the-art techniques. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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