Distantly Supervised Morpho-Syntactic Model for Relation Extraction
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916096605421568 |
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| author | Gutehrlé, Nicolas Atanassova, Iana |
| author_facet | Gutehrlé, Nicolas Atanassova, Iana |
| contents | The task of Information Extraction (IE) involves automatically converting unstructured textual content into structured data. Most research in this field concentrates on extracting all facts or a specific set of relationships from documents. In this paper, we present a method for the extraction and categorisation of an unrestricted set of relationships from text. Our method relies on morpho-syntactic extraction patterns obtained by a distant supervision method, and creates Syntactic and Semantic Indices to extract and classify candidate graphs. We evaluate our approach on six datasets built on Wikidata and Wikipedia. The evaluation shows that our approach can achieve Precision scores of up to 0.85, but with lower Recall and F1 scores. Our approach allows to quickly create rule-based systems for Information Extraction and to build annotated datasets to train machine-learning and deep-learning based classifiers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10002 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distantly Supervised Morpho-Syntactic Model for Relation Extraction Gutehrlé, Nicolas Atanassova, Iana Computation and Language The task of Information Extraction (IE) involves automatically converting unstructured textual content into structured data. Most research in this field concentrates on extracting all facts or a specific set of relationships from documents. In this paper, we present a method for the extraction and categorisation of an unrestricted set of relationships from text. Our method relies on morpho-syntactic extraction patterns obtained by a distant supervision method, and creates Syntactic and Semantic Indices to extract and classify candidate graphs. We evaluate our approach on six datasets built on Wikidata and Wikipedia. The evaluation shows that our approach can achieve Precision scores of up to 0.85, but with lower Recall and F1 scores. Our approach allows to quickly create rule-based systems for Information Extraction and to build annotated datasets to train machine-learning and deep-learning based classifiers. |
| title | Distantly Supervised Morpho-Syntactic Model for Relation Extraction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.10002 |