Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity.

By Barbara Rychalska, Katarzyna Pakulska, Krystyna Chodorowska, Wojciech Walczak, Piotr Andruszkiewicz. 2016

55 citations recorded by the source corpus.

This paper describes our proposed solutions designed for a STS core track within the Se-mEval 2016 English Semantic Textual Similarity (STS) task. Our method of similarity detection combines recursive autoencoders with a WordNet award-penalty system that accounts for semantic relatedness, and an SVM classifier, which produces the final score from similarity matrices. This solution is further supported by an ensemble classifier, combining an aligner with a bi-directional Gated Recurrent Neural Network and additional features, which then performs Linear Support Vector Regression to determine another set of scores.

Subfields: Artificial Intelligence, Natural Language Processing, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sarah Kohail
2. Amr Rekaby Salama
3. Chris Biemann

In Natural Language Processing

1. Sarah Kohail
2. Chris Biemann
3. Amr Rekaby Salama

In Representation & Self-Supervised Learning

1. Sarah Kohail
2. Chris Biemann
3. Amr Rekaby Salama

Citations in the corpus

• STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble (2017, methodology): “The highest correlation score was obtained by Rychalska et al. (2016).”