Application of Ensemble learning for computing semantic textual similarity

Semantic Textual Similarity (STS) evaluation

assesses the degree to which two parts

of texts are similar, based on their semantic

evaluation. In this paper, we describe three

models submitted to STS SemEval 2017.

Given two English parts of a text, each of

proposed methods outputs the assessment

of their semantic similarity.

We propose an approach for computing

monolingual semantic textual similarity

based on an ensemble of three distinct

methods. Our model consists of recursive

neural network (RNN) text auto-encoders

ensemble with supervised a model of vectorized

sentences using reduced part of

speech (PoS) weighted word embeddings

as well as unsupervised a method based

on word coverage (TakeLab). Additionally,

we enrich our model with additional

features that allow disambiguation of ensemble

methods based on their efficiency.

We have used Multi-Layer Perceptron as an

ensemble classifier basing on estimations

of trained Gradient Boosting Regressors.

Results of our research proves that using

such ensemble leads to a higher accuracy

due to a fact that each memberalgorithm

tends to specialize in particular

type of sentences. Simple model based

on PoS weighted Word2Vec word embeddings

seem to improve performance of

more complex RNN based auto-encoders in

the ensemble. In the monolingual EnglishEnglish

STS subtask our Ensemble based

model achieved mean Pearson correlation

of .785 compared with human annotators.