Triplètoile: Extraction of Knowledge from Microblogging Text
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914925014679552 |
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| author | Zavarella, Vanni Consoli, Sergio Recupero, Diego Reforgiato Fenu, Gianni Angioni, Simone Buscaldi, Davide Dessì, Danilo Osborne, Francesco |
| author_facet | Zavarella, Vanni Consoli, Sergio Recupero, Diego Reforgiato Fenu, Gianni Angioni, Simone Buscaldi, Davide Dessì, Danilo Osborne, Francesco |
| contents | Numerous methods and pipelines have recently emerged for the automatic extraction of knowledge graphs from documents such as scientific publications and patents. However, adapting these methods to incorporate alternative text sources like micro-blogging posts and news has proven challenging as they struggle to model open-domain entities and relations, typically found in these sources. In this paper, we propose an enhanced information extraction pipeline tailored to the extraction of a knowledge graph comprising open-domain entities from micro-blogging posts on social media platforms. Our pipeline leverages dependency parsing and classifies entity relations in an unsupervised manner through hierarchical clustering over word embeddings. We provide a use case on extracting semantic triples from a corpus of 100 thousand tweets about digital transformation and publicly release the generated knowledge graph. On the same dataset, we conduct two experimental evaluations, showing that the system produces triples with precision over 95% and outperforms similar pipelines of around 5% in terms of precision, while generating a comparatively higher number of triples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14908 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Triplètoile: Extraction of Knowledge from Microblogging Text Zavarella, Vanni Consoli, Sergio Recupero, Diego Reforgiato Fenu, Gianni Angioni, Simone Buscaldi, Davide Dessì, Danilo Osborne, Francesco Information Retrieval Computational Engineering, Finance, and Science Computation and Language 68T01, 68T50 I.2.7; I.2.1 Numerous methods and pipelines have recently emerged for the automatic extraction of knowledge graphs from documents such as scientific publications and patents. However, adapting these methods to incorporate alternative text sources like micro-blogging posts and news has proven challenging as they struggle to model open-domain entities and relations, typically found in these sources. In this paper, we propose an enhanced information extraction pipeline tailored to the extraction of a knowledge graph comprising open-domain entities from micro-blogging posts on social media platforms. Our pipeline leverages dependency parsing and classifies entity relations in an unsupervised manner through hierarchical clustering over word embeddings. We provide a use case on extracting semantic triples from a corpus of 100 thousand tweets about digital transformation and publicly release the generated knowledge graph. On the same dataset, we conduct two experimental evaluations, showing that the system produces triples with precision over 95% and outperforms similar pipelines of around 5% in terms of precision, while generating a comparatively higher number of triples. |
| title | Triplètoile: Extraction of Knowledge from Microblogging Text |
| topic | Information Retrieval Computational Engineering, Finance, and Science Computation and Language 68T01, 68T50 I.2.7; I.2.1 |
| url | https://arxiv.org/abs/2408.14908 |