Question Similarity Detection in Turkish Using Semantic Textual Similarity Methods

In this study, we evaluate the performance of various semantic textual similarity methods on question similarity detection task in Turkish. Various handcrafted features and neural models, specifically siamese recurrent networks, are studied to detect questions which have a similar meaning to given q...

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Bibliographic Details
Published in2019 27th Signal Processing and Communications Applications Conference (SIU) pp. 1 - 4
Main Authors Yıldız, Eray, Findik, Yasin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2019
Subjects
Dogs
Feature extraction
Google
Natural language processing
question answering
question similarity
Recurrent neural networks
Semantics
Task analysis
textual similarity
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Summary:In this study, we evaluate the performance of various semantic textual similarity methods on question similarity detection task in Turkish. Various handcrafted features and neural models, specifically siamese recurrent networks, are studied to detect questions which have a similar meaning to given question in a dataset. Several experiments have been performed to compare the performance of features and neural methods. Our Experiments demonstrate that siamese recurrent networks significantly outperforms traditional methods which are based on handcrafted features such as word and stem matching counts, TFIDF vectors and similarity of word embeddings. We also observed that the performance of siamese recurrent networks could be further improved by incorporating handcrafted features to the process.
DOI:10.1109/SIU.2019.8806308