Abstract
In this paper we present a method for extracting IsA assertions (hyponymy relations), AtLocation assertions (informing of the location of an object or place), LocatedNear assertions (informing of neighboring locations), CreatedBy assertions (informing of the creator of an object) and MemberOf assertions (informing of group membership) automatically from Japanese Wikipedia XML dump files. We use the Hyponymy extraction tool v1.0, which analyses definition, category and hierarchy structures of Wikipedia articles to extract IsA assertions and produce information-rich taxonomy. From this taxonomy we extract additional information, in this case AtLocation, LocatedNear, CreatedBy and MemberOf types of assertions, using our original method. The presented experiments prove that both methods produce satisfactory results: we were able to acquire 5,866,680 IsA assertions with 96.0% reliability, 131,760 AtLocation assertion pairs with 93.5% reliability, 6,217 LocatedNear assertion pairs with 98.5% reliability, 270,230 CreatedBy assertion pairs with 78.5% reliability and 21,053 MemberOf assertions with 87.0% reliability. Our method surpassed the baseline system in terms of both precision and the number of acquired assertions.
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Curly brackets were used to mark the tags’ representations.
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To measure the agreement level between judges, we used Randolph’s free marginal multirater kappa instead of Fleiss’ fixed-marginal multirater kappa, due to high agreement low kappa paradox.
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We adjusted the number of evaluated pairs to balance the proportion between the total number of pairs and the test sample.
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Krawczyk, M., Rzepka, R., Araki, K. (2018). Exploiting Wikipedia-Based Information-Rich Taxonomy for Extracting Location, Creator and Membership Related Information for ConceptNet Expansion. In: Vetulani, Z., Mariani, J., Kubis, M. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2015. Lecture Notes in Computer Science(), vol 10930. Springer, Cham. https://doi.org/10.1007/978-3-319-93782-3_19
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