Investigating text power in predicting semantic similarity Article Swipe
Zahra Yousefi
,
Hajar Sotudeh
,
Mahdieh Mirzabeigi
,
Seyed Mostafa Fakhrahmad
,
Alireza Nikseresht
,
Mehdi Mohammadi
·
YOU?
·
· 2019
· Open Access
·
YOU?
·
· 2019
· Open Access
·
This article presents an empirical evaluation to investigate the distributional semantic power of abstract, body and full-text, as different text levels, in predicting the semantic similarity using a collection of open access articles from PubMed. The semantic similarity is measured based on two criteria namely, linear MeSH terms intersection and hierarchical MeSH terms distance. As such, a random sample of 200 queries and 20000 documents are selected from a test collection built on CITREC open source code. Sim Pack Java Library is used to calculate the textual and semantic similarities. The nDCG value corresponding to two of the semantic similarity criteria is calculated at three precision points. Finally, the nDCG values are compared by using the Friedman test to determine the power of each text level in predicting the semantic similarity. The results showed the effectiveness of the text in representing the semantic similarity in such a way that texts with maximum textual similarity are also shown to be 77% and 67% semantically similar in terms of linear and hierarchical criteria, respectively. Furthermore, the text length is found to be more effective in representing the hierarchical semantic compared to the linear one. Based on the findings, it is concluded that when the subjects are homogenous in the tree of knowledge, abstracts provide effective semantic capabilities, while in heterogeneous milieus, full-texts processing or knowledge bases is needed to acquire IR effectiveness.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doaj.org/article/e5fc4231b48e46238c4c60ad0edef40f
- OA Status
- green
- Cited By
- 2
- References
- 14
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- OpenAlex ID
- https://openalex.org/W2910117565
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https://openalex.org/W2910117565Canonical identifier for this work in OpenAlex
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Investigating text power in predicting semantic similarityWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
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2019-01-01Full publication date if available
- Authors
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Zahra Yousefi, Hajar Sotudeh, Mahdieh Mirzabeigi, Seyed Mostafa Fakhrahmad, Alireza Nikseresht, Mehdi MohammadiList of authors in order
- Landing page
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https://doaj.org/article/e5fc4231b48e46238c4c60ad0edef40fPublisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://doaj.org/article/e5fc4231b48e46238c4c60ad0edef40fDirect OA link when available
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Semantic similarity, Computer science, Similarity (geometry), Information retrieval, Natural language processing, Artificial intelligence, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2021: 1, 2020: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.PubMed. | 34 |
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| abstract_inverted_index.article | 1 |
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| abstract_inverted_index.textual | 86, 152 |
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| abstract_inverted_index.Friedman | 116 |
| abstract_inverted_index.articles | 32 |
| abstract_inverted_index.compared | 112, 187 |
| abstract_inverted_index.criteria | 43, 100 |
| abstract_inverted_index.measured | 39 |
| abstract_inverted_index.milieus, | 218 |
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| abstract_inverted_index.subjects | 202 |
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| abstract_inverted_index.criteria, | 170 |
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| abstract_inverted_index.knowledge | 222 |
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| abstract_inverted_index.knowledge, | 209 |
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| abstract_inverted_index.investigate | 7 |
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| abstract_inverted_index.similarities. | 89 |
| abstract_inverted_index.distributional | 9 |
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| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.63799959 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |