TY - CHAP U1 - Konferenzveröffentlichung A1 - Dimitrov, Yoan T1 - Combining word embeddings and convolutional neural networks to detect duplicated questions T2 - Connect(IT) : Informatik-Konferenz der Hochschule Reutlingen ; 20. Mai 2020 : Tagungsband. - (Informatics Inside ; 20) N2 - Detecting semantic similarities between sentences is still a challenge today due to the ambiguity of natural languages. In this work, we propose a simple approach to identifying semantically similar questions by combining the strengths of word embeddings and Convolutional Neural Networks (CNNs). In addition, we demonstrate how the cosine similarity metric can be used to effectively compare feature vectors. Our network is trained on the Quora dataset, which contains over 400k question pairs. We experiment with different embedding approaches such as Word2Vec, Fasttext, and Doc2Vec and investigate the effects these approaches have on model performance. Our model achieves competitive results on the Quora dataset and complements the well-established evidence that CNNs can be utilized for paraphrase recognition tasks. KW - natural language processing KW - word embeddings KW - sentence classification KW - convolutional neural networks KW - deep learning Y1 - 2020 U6 - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-27505 UN - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-27505 SN - 978-3-00-065431-2 SB - 978-3-00-065431-2 SP - 74 EP - 82 S1 - 9 PB - Hochschule Reutlingen CY - Reutlingen ER -