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20102024
most citedEfficiently Inducing Features of Conditional Random Fields

366 citations · 1.3k across the 54 of their papers we have counts for

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Showing 2017 · cs.CLShow all

12 papers · 2 filters

cs.CL2017★ 21 cited

Automatically Extracting Action Graphs from Materials Science Synthesis Procedures

Sheshera Mysore, Edward Kim, Emma Strubell +6

Computational synthesis planning approaches have achieved recent success in organic chemistry, where tabulated synthesis procedures are readily available for supervised learning. T…

cs.CL2017★ 14 cited

Finer Grained Entity Typing with TypeNet

Shikhar Murty, Patrick Verga, Luke Vilnis +1

We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchic…

cs.CL2017★ 8 cited

Attending to All Mention Pairs for Full Abstract Biological Relation Extraction

Patrick Verga, Emma Strubell, Ofer Shai +1

Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. However, many relation type…

cs.CL2017

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer +5

Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information. A pop…

cs.CL2017

Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection

Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis +1

Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, coreference, relation extraction, and question answering. Sup…

cs.CL2017

Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling

Dung Thai, Shikhar Murty, Trapit Bansal +3

In textual information extraction and other sequence labeling tasks it is now common to use recurrent neural networks (such as LSTM) to form rich embedded representations of long-t…