Volltext-Downloads (blau) und Frontdoor-Views (grau)
The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 10 of 532
Back to Result List

Combining word embeddings and convolutional neural networks to detect duplicated questions

  • 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.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Name:Dimitrov, Yoan
URN:urn:nbn:de:bsz:rt2-opus4-27505
URL:https://infoinside.reutlingen-university.de/?page_id=31
ISBN:978-3-00-065431-2
Erschienen in:Informatics inside: connect(IT) : Informatik-Konferenz der Hochschule Reutlingen ; 20. Mai 2020 : Tagungsband. - (Informatics inside ; 20)
Publisher:Hochschule Reutlingen
Place of publication:Reutlingen
Editor:Uwe Kloos
Document Type:Conference Proceeding
Language:English
Year of Publication:2020
Tag:convolutional neural networks; deep learning; natural language processing; sentence classification; word embeddings
Pagenumber:9
First Page:74
Last Page:82
Catalogue entry:Im Katalog der Hochschule Reutlingen ansehen
Dewey Decimal Classification:004 Informatik
Open Access:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International