Marshall–Olkin power-law distributions in length-frequency of entities
Entities involve important concepts with concrete meanings and play important roles in numerous linguistic tasks. Entities have different forms in different linguistic tasks and researchers treat those different forms as different concepts. In this paper, we are curious to know whether there are som...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/171190 |
| _version_ | 1811685981453025280 |
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| author | Zhong, Xiaoshi Yu, Xiang Cambria, Erik Rajapakse, Jagath Chandana |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Zhong, Xiaoshi Yu, Xiang Cambria, Erik Rajapakse, Jagath Chandana |
| author_sort | Zhong, Xiaoshi |
| collection | NTU |
| description | Entities involve important concepts with concrete meanings and play important roles in numerous linguistic tasks. Entities have different forms in different linguistic tasks and researchers treat those different forms as different concepts. In this paper, we are curious to know whether there are some common characteristics that connect those different forms of entities. Specifically, we investigate the underlying distributions of entities from different types and different languages, trying to figure out some common characteristics behind those diverse entities. After analyzing twelve datasets about different types of entities and eighteen datasets about entities in different languages, we find that while these entities are dramatically diverse from each other in many aspects, their length-frequencies can be well characterized by a family of Marshall–Olkin power-law (MOPL) distributions. We conduct experiments on those thirty datasets about entities in different types and different languages, and experimental results demonstrate that MOPL models characterize the length-frequencies of entities much better than two state-of-the-art power-law models and an alternative log-normal model. Experimental results also demonstrate that MOPL models are scalable to the length-frequency of entities in large-scale real-world datasets. |
| first_indexed | 2024-10-01T04:53:09Z |
| format | Journal Article |
| id | ntu-10356/171190 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:53:09Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1711902023-10-17T02:16:42Z Marshall–Olkin power-law distributions in length-frequency of entities Zhong, Xiaoshi Yu, Xiang Cambria, Erik Rajapakse, Jagath Chandana School of Computer Science and Engineering Engineering::Computer science and engineering Entities Length-Frequency of Entities Entities involve important concepts with concrete meanings and play important roles in numerous linguistic tasks. Entities have different forms in different linguistic tasks and researchers treat those different forms as different concepts. In this paper, we are curious to know whether there are some common characteristics that connect those different forms of entities. Specifically, we investigate the underlying distributions of entities from different types and different languages, trying to figure out some common characteristics behind those diverse entities. After analyzing twelve datasets about different types of entities and eighteen datasets about entities in different languages, we find that while these entities are dramatically diverse from each other in many aspects, their length-frequencies can be well characterized by a family of Marshall–Olkin power-law (MOPL) distributions. We conduct experiments on those thirty datasets about entities in different types and different languages, and experimental results demonstrate that MOPL models characterize the length-frequencies of entities much better than two state-of-the-art power-law models and an alternative log-normal model. Experimental results also demonstrate that MOPL models are scalable to the length-frequency of entities in large-scale real-world datasets. Agency for Science, Technology and Research (A*STAR) This research is supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A18A2b0046). 2023-10-17T02:16:41Z 2023-10-17T02:16:41Z 2023 Journal Article Zhong, X., Yu, X., Cambria, E. & Rajapakse, J. C. (2023). Marshall–Olkin power-law distributions in length-frequency of entities. Knowledge-Based Systems, 279, 110942-. https://dx.doi.org/10.1016/j.knosys.2023.110942 0950-7051 https://hdl.handle.net/10356/171190 10.1016/j.knosys.2023.110942 2-s2.0-85171334963 279 110942 en A18A2b0046 Knowledge-Based Systems © 2023 Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Entities Length-Frequency of Entities Zhong, Xiaoshi Yu, Xiang Cambria, Erik Rajapakse, Jagath Chandana Marshall–Olkin power-law distributions in length-frequency of entities |
| title | Marshall–Olkin power-law distributions in length-frequency of entities |
| title_full | Marshall–Olkin power-law distributions in length-frequency of entities |
| title_fullStr | Marshall–Olkin power-law distributions in length-frequency of entities |
| title_full_unstemmed | Marshall–Olkin power-law distributions in length-frequency of entities |
| title_short | Marshall–Olkin power-law distributions in length-frequency of entities |
| title_sort | marshall olkin power law distributions in length frequency of entities |
| topic | Engineering::Computer science and engineering Entities Length-Frequency of Entities |
| url | https://hdl.handle.net/10356/171190 |
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