LADS: Rapid Development of a Learning-To-Rank Based Related Entity Finding System using Open Advancement
نویسندگان
چکیده
In this paper, we present our system called LADS, tailored to work on the TREC Entity Track Task of Related Entity Finding. The LADS system consists of four key components: document retrieval, entity extraction, feature extraction and entity ranking. We adopt the open advancement framework for the rapid development and use a learning-to-rank approach to rank candidate entities. We also experiment with various commercial and academic NLP tools. In our final experiments with the TREC 2010 dataset, our system achieves the fourth rank compared to the fifteen teams who participated in TREC 2010.
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