Abstract
Extracting relations out of unstructured text is essential for a wide range of applications. Minimal human effort, scalability and high precision are desirable characteristics. We introduce a distant supervised closed relation extraction approach based on distributional semantics and a tree generalization. Our approach uses training data obtained from a reference knowledge base to derive dependency parse trees that might express a relation. It then uses a novel generalization algorithm to construct dependency tree patterns for the relation. Distributional semantics are used to eliminate false candidate patterns. We evaluate the performance in experiments on a large corpus using ninety target relations. Our evaluation results suggest that our approach achieves a higher precision than two state-of-the-art systems. Moreover, our results also underpin the scalability of our approach. Our open source implementation can be found at https://github.com/dice-group/Ocelot.
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Notes
- 1.
\(A:= \{ label, lemma, pos, ner, domain, range, general\}\) are the vertex attributes used throughout this paper.
- 2.
The shortest sentence with a relation has at least two tokens for the named entity arguments, one token for the relation mention and one for the end punctuation.
- 3.
Seven types are applied (Place, Person, Organization, Money, Percent, Date, Time).
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- 9.
In our approach we utilize Organization, Person and Place.
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We provided an example of an RDF serialisation of the framework in Listing 1.1.
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Acknowledgement
This work has been supported by the H2020 project HOBBIT (no. 688227), the BMWI projects GEISER (no. 01MD16014E) and OPAL (no. 19F2028A), the EuroStars projects DIESEL (no. 01QE1512C) and QAMEL (no. 01QE1549C).
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Speck, R., Ngomo Ngonga, AC. (2018). On Extracting Relations Using Distributional Semantics and a Tree Generalization. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds) Knowledge Engineering and Knowledge Management. EKAW 2018. Lecture Notes in Computer Science(), vol 11313. Springer, Cham. https://doi.org/10.1007/978-3-030-03667-6_27
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