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

NLP Highlights

English - November 10, 2017 00:28 - 34 minutes - 31.7 MB - ★★★★★ - 22 ratings
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Previous Episode: 40 - On the State of the Art of Evaluation in Neural Language Models, with Gábor Melis

Next Episode: 42 - Generating Sentences by Editing Prototypes, with Kelvin Guu

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