PLAY PODCASTS
41 - Cross-Sentence N-ary Relation Extraction with Graph LSTMs, with Nanyun (Violet) Peng

41 - Cross-Sentence N-ary Relation Extraction with Graph LSTMs, with Nanyun (Violet) Peng

TACL 2017 paper, by Nanyun Peng, Hoifung Poon, Ch…

NLP Highlights · Allen Institute for Artificial Intelligence

November 10, 201734m 37s

Audio is streamed directly from the publisher (podtrac.com) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.

Show Notes

TACL 2017 paper, by Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. Most relation extraction work focuses on binary relations, like (Seattle, located in, Washington), because extracting n-ary relations is difficult. Nanyun (Violet) and her colleagues came up with a model to extract n-ary relations, focusing on drug-mutation-gene interactions, using graph LSTMs (a construct pretty similar to graph CNNs, which was developed around the same time). Nanyun comes on the podcast to tell us about her work. https://www.semanticscholar.org/paper/Cross-Sentence-N-ary-Relation-Extraction-with-Grap-Peng-Poon/03a2f871cc841e8047ab3291806dc301c5144bec