Relation extraction on an open-domain knowledge base
Accompanying repository for our EMNLP 2017 paper. It contains the code to replicate the experiments and the pre-trained models for sentence-level relation extraction.
In this project, we provide our implementations of CNN [Zeng et al., 2014] and PCNN [Zeng et al.,2015] and their extended version with sentence-level attention scheme [Lin et al., 2016] .
NYT10 is originally released by the paper "Sebastian Riedel, Limin Yao, and Andrew McCallum. Modeling relations and their mentions without labeled text."
This is the home page of the ParsCit project, which performs reference string parsing, sometimes also called citation parsing or citation extraction. It is architected as a supervised machine learning procedure that uses Conditional Random Fields as its learning mechanism. You can download the code below, parse strings online, or send batch jobs to our web service (coming soon!). The code contains both the training data, feature generator and shell scripts to connect the system to a web service (used here too).
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