In: 2019 6th Swiss Conference on Data Science (SDS), 2019, p. 63–68
One of the major difficulties in applying distant supervision to relation extraction is class imbalance, as the distribution of relations appearing in text is heavily skewed. This is particularly damaging for the multi-instance variant of relation extraction. In this work, we introduce a new model called Aggregated Piecewise Convolutional Neural Networks, or APCNN, to address this problem....
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In: Proceedings of the Web Conference (WWW 2019)
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In: Encyclopedia of Big Data Technologies, 2018, p. 1–7
In this chapter, we discuss approaches leveraging distant supervision for relation extraction. We start by introducing the key ideas behind distant supervision as well as their main shortcomings. We then discuss approaches that improve over the basic method, including approaches based on the at-least-one-principle along with their extensions for handling false negative labels, and approaches...
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In: ACM Comput. Surv., 2018, vol. 51, no. 5, p. 106:1–106:35
Relation extraction is a subtask of information extraction where semantic relationships are extracted from natural language text and then classified. In essence, it allows us to acquire structured knowledge from unstructured text. In this article, we present a survey of relation extraction methods that leverage pre-existing structured or semi- structured data to guide the extraction process....
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