ON THE EFFECT OF WORD POSITIONS IN GRAPH-BASED KEYWORD EXTRACTION
In this study, we focus on the effect of word positions in unsupervised, graph-based keyword extraction. To this aim, we discuss the performance of four node-weighting procedures, namely Word Position (WP), Word Position Bidirectional (WPB), Sentence Position (SP), and Sentence Position Bidirectiona...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
National Defense University Barbaros Naval Sciences and Engineering Institute Journal of Naval Science and Engineering
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/327c54751393413bbd6c9c650b41b4e6 |
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| Sumario: | In this study, we focus on the effect of word positions in unsupervised, graph-based keyword extraction. To this aim, we discuss the performance of four node-weighting procedures, namely Word Position (WP), Word Position Bidirectional (WPB), Sentence Position (SP), and Sentence Position Bidirectional (SPB). WP assigns higher weights to words that appear at the beginning of a text. WPB assigns higher weights to words that appear either at the beginning or end of a text. SP assigns higher weights to words that appear in the very first sentences of a text. SPB assigns higher weights to words that appear in sentences that are either close to the beginning or end of a text. Experiments conducted on six benchmark datasets show that WP and SP do not statistically differ. However, for datasets whose keywords appear early in the text WP performs better than SP with no statistical difference, while for datasets where keywords are evenly distributed in text SP statistically performs better than WP. |
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