# EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION

## TL;DR

This paper presents an ensemble of neural network models for classifying keyphrases in scientific publications, achieving competitive results in SemEval-2017 with a focus on deep learning techniques.

## Contribution

The authors developed an ensemble of diverse neural models and demonstrated improved performance by training on the full dataset, advancing keyphrase classification methods.

## Key findings

- Ensemble achieved a micro-F1-score of 0.63 on test data.
- Training on full data improved the score to 0.69.
- Our approach ranked 2nd in the SemEval-2017 Task 10.

## Abstract

This paper describes our approach to the SemEval 2017 Task 10: "Extracting Keyphrases and Relations from Scientific Publications", specifically to Subtask (B): "Classification of identified keyphrases". We explored three different deep learning approaches: a character-level convolutional neural network (CNN), a stacked learner with an MLP meta-classifier, and an attention based Bi-LSTM. From these approaches, we created an ensemble of differently hyper-parameterized systems, achieving a micro-F1-score of 0.63 on the test data. Our approach ranks 2nd (score of 1st placed system: 0.64) out of four according to this official score. However, we erroneously trained 2 out of 3 neural nets (the stacker and the CNN) on only roughly 15% of the full data, namely, the original development set. When trained on the full data (training+development), our ensemble has a micro-F1-score of 0.69. Our code is available from https://github.com/UKPLab/semeval2017-scienceie.