Dr Xingyi Song

Department of Computer Science

Academic Fellow in Computational Media Analysis, Natural Language Processing

Member of the Natural Language Processing reserach group

Profile photo of Xingyi Song
x.song@sheffield.ac.uk
+44 114 222 1867

Full contact details

Dr Xingyi Song
Department of Computer Science
Regent Court (DCS)
211 Portobello
Sheffield
S1 4DP
Profile

Dr Xingyi Song, an Academic Fellow at the Department of Computer Science, University of Sheffield, UK. He is a member of the Natural Language Processing group and GATE team (https://gate.ac.uk/)

Previously he worked as a machine translation specialist at Iconic Translation Machine (2015-2016) and Research Associate for several EU funded projects such as Kconnect, Knowmak and Risis2 (from 2016-2021)) at the University of Sheffield. 

He completed his MSc and PhD in Natural Language Processing group at the University of Sheffield. His research interests are in Natural Language Processing, Computational Social Science, sentiment analysis and Bio-medical text processing. 

Publications

Journal articles

Conference proceedings papers

  • Jiang Y, Wang Y, Maynard D & Song X (2020) Comparing topic-aware neural networks for bias detection of news. Proceedings of 24th European Conference on Artificial Intelligence (ECAI 2020), Vol. 325 (pp 2054-2061). Santiago de Compostela, Spain, 29 August 2020 - 2 September 2020. View this article in WRRO RIS download Bibtex download
  • Song X, Downs J, Velupillai S, Holden R, Kikoler M, Bontcheva K, Dutta R & Roberts A (2020) Using deep neural networks with intra- And inter-sentence context to classify suicidal behaviour. LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings (pp 1303-1310) RIS download Bibtex download
  • Gao J, Han S, Song X & Ciravegna F (2020) RP-DNN: A tweet level propagation context based deep neural networks for early rumor detection in social media. LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings (pp 6094-6105) View this article in WRRO RIS download Bibtex download
  • Jiang Y, Petrak J, Song X, Bontcheva K & Maynard D (2019) Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network. Proceedings of the 13th International Workshop on Semantic Evaluation, June 2019 - June 2019. RIS download Bibtex download
  • Jiang Y, Petrak J, Song X, Bontcheva K & Maynard D (2019) Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network. Proceedings of the 13th International Workshop on Semantic Evaluation. Minneapolis, Minnesota, USA, 6 June 2019 - 7 June 2019. View this article in WRRO RIS download Bibtex download
  • Song X, Petrak J & Roberts A (2018) A deep neural network sentence level classification method with context information. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp 900-904). Brussels, Belgium, 31 October 2018 - 4 November 2018. View this article in WRRO RIS download Bibtex download
  • Blain F, Song X & Specia L (2016) Sheffield Systems for the English-Romanian WMT Translation Task. Proceedings of the First Conference on Machine Translation RIS download Bibtex download
  • Song X, Specia L & Cohn T (2014) Data selection for discriminative training in statistical machine translation. Proceedings of the 17th Annual Conference of the European Association for Machine Translation, EAMT 2014 (pp 45-52) RIS download Bibtex download
  • Song X, Cohn T & Specia L (2013) BLEU deconstructed: Designing a Better MT Evaluation Metric. Proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLING) RIS download Bibtex download
  • Song X & Cohn T (2011) Regression and Ranking based Optimisation for Sentence Level Machine Translation Evaluation. Proceedings of the Sixth Workshop on Statistical Machine Translation. Edinburgh, UK RIS download Bibtex download

Datasets