Combining Neural Networks and Log-linear Models to Improve Relation Extraction

18 Nov 2015 Thien Huu Nguyen Ralph Grishman

The last decade has witnessed the success of the traditional feature-based method on exploiting the discrete structures such as words or lexical patterns to extract relations from text. Recently, convolutional and recurrent neural networks has provided very effective mechanisms to capture the hidden structures within sentences via continuous representations, thereby significantly advancing the performance of relation extraction... (read more)

PDF Abstract
No code implementations yet. Submit your code now

Datasets


Results from the Paper


Ranked #3 on Relation Extraction on ACE 2005 (Relation classification F1 metric)

     Get a GitHub badge
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Relation Extraction ACE 2005 RNN+CNN Relation classification F1 67.7 # 3

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet