Distantly Supervised Morpho-Syntactic Model for Relation Extraction
Nicolas Gutehrlé Iana Atanassova
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.