Journal articles | Book chapters | Conference papers | Industry talks

Journal articles

2024

V. Pekar, M. Candi, A. Beltagui, N. Stylos, and W. Liu. (2024). Explainable text-based features in predictive models of crowdfunding campaigns.
Annals of Operations Research. [DOI] [URL] [BibTex]

M. Saharti, S.M. Chaudhry, V. Pekar, and E. Bajoori. (2024). Environmental, social and governance (ESG) performance of firms in the era of geopolitical conflicts.
Journal of Environmental Management, vol. 351, pp. 119744. [DOI] [URL] [BibTex]

2021

V. Pekar, H. Najafi, J. Binner, R. Swanson, C. Rickard, and J. Fry. (2021). Voting intentions on social media and political opinion polls.
Government Information Quarterly. [DOI] [URL] [BibTex]

2019

V. Pekar, J. Binner, H. Najafi, C. Hale, and V. Schmidt. (2019). Early Detection of Heterogeneous Disaster Events Using Social Media. Journal of the Association for Information Science and Technology. [DOI] [URL] [BibTex]

2008

V. Pekar. (2008). Discovery of Event Entailment Knowledge from Text Corpora. Comput. Speech Lang., vol. 22, no. 1, pp. 1--16. [DOI] [URL] [BibTex]

V. Pekar and S. Ou. (2008). Discovery of subjective evaluations of product features in hotel reviews. Journal of Vacation Marketing, vol. 14, no. 2, pp. 145-155. [DOI] [URL] [BibTex]

2007

R. Mitkov, V. Pekar, D. Blagoev, and A. Mulloni. (2007). Methods for Extracting and Classifying Pairs of Cognates and False Friends. Machine Translation, vol. 21, no. 1, pp. 29--53. [DOI] [URL] [BibTex]

V. Pekar and R. Evans. (2007). Discovery of Language Resources on the Web: Information Extraction from Heterogeneous Documents. Literary and Linguistic Computing, vol. 22, no. 3, pp. 329-343. [DOI] [URL] [BibTex]

2006

V. Pekar, R. Mitkov, D. Blagoev, and A. Mulloni. (2006). Finding Translations for Low-frequency Words in Comparable Corpora. Machine Translation, vol. 20, no. 4, pp. 247--266. [DOI] [URL] [BibTex]

Book chapters

2003

A. Maedche, V. Pekar, and S. Staab. (2003). Ontology Learning Part One - on Discovering Taxonomic Relations from the Web. In N. Zhong, J. Liu, and Y. Yao. Web Intelligence, pp. 301--319. [DOI] [URL] [BibTex]

Conference papers

2020

V. Pekar. (2020). Purchase Intentions on Social Media as Predictors of Consumer Spending. Proceedings of the 14th International AAAI Conference on Web and Social Media, , pp. 545-556. [DOI] [URL] [BibTex]

2018

V. Pekar. (2018). Mining for Signals of Future Consumer Expenditure on Twitter and Google Trends. Proceedings of 2nd International Conference on Advanced Reserach Methods and Analytics (Internet and Big Data in Economics and Social Sciences), Valencia, Spain, pp. 157-165. [DOI] [URL] [BibTex]

2017

V. Pekar and J. Binner. (2017). Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media. Proceedings of the EMNLP'17 Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 92--101. [DOI] [URL] [BibTex]

2016

V. Pekar, J. Binner, H. Najafi, and C. Hale. (2016). Selecting classification features for detection of mass emergencies on social media. Proceedings of the 2016 International Conference on Security and Management. [BibTex]

2014

V. Pekar, J. Yu, M. El-karef, and B. Bohnet. (2014). Exploring Options for Fast Domain Adaptation of Dependency Parsers. Proceedings of the First Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages, Dublin, Ireland, pp. 54--65. [URL] [BibTex]

2010

N. Ponomareva, J.M. Gomez, and V. Pekar. (2010). AIR: A Semi-Automatic System for Archiving Institutional Repositories. Proceedings of the 14th International Conference on Applications of Natural Language to Information Systems, Berlin, Heidelberg, pp. 169--181. [DOI] [URL] [BibTex]

2009

N. Afzal and V. Pekar. (2009). Unsupervised Relation Extraction for Automatic Generation of Multiple-Choice Questions. Proceedings of Recent Advances in Natural Language Processing). [BibTex]

2008

S. Ou, V. Pekar, C. Orasan, C. Spurk, and M. Negri. (2008). Development and Alignment of a Domain-Specific Ontology for Question Answering. Proceedings of the International Conference on Language Resources and Evaluation, LREC 2008, 26 May - 1 June 2008, Marrakech, Morocco. [URL] [BibTex]

G. Corpas, R. Mitkov, N. Afzal, L. Garcia-Moya, and V. Pekar. (2008). Translation universals: do they exist? A corpus-based NLP study of convergence and simplification. Proceedings of The Eighth Conference of the Association for Machine Translation in the Americas (AMTA-08). [BibTex]

2007

R. Mitkov, R. Evans, C. Orăsan, L.A. Ha, and V. Pekar. (2007). Anaphora Resolution: To What Extent Does It Help NLP Applications?. Proceedings of the 6th Discourse Anaphora and Anaphor Resolution Conference on Anaphora: Analysis, Algorithms and Applications, Berlin, Heidelberg, pp. 179--190. [URL] [BibTex]

2006

V. Pekar. (2006). Acquisition of Verb Entailment from Text. Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL), Stroudsburg, PA, USA, pp. 49--56. [DOI] [URL] [BibTex]

V. Pekar. (2006). Discovery of Entailment Relations from Event Co-Occurrences. Proceedings of the 2006 Conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva Del Garda, Italy, Amsterdam, The Netherlands, The Netherlands, pp. 516--520. [URL] [BibTex]

2005

V. Pekar. (2005). Information Extraction from Email Announcements. Proceedings of the 10th International Conference on Natural Language Processing and Information Systems, Berlin, Heidelberg, pp. 372--375. [DOI] [URL] [BibTex]

2004

V. Pekar. (2004). Linguistic Preprocessing for Distributional Classification of Words. Proceedings of the COLING Workshop on Enhancing and Using Electronic Dictionaries, Stroudsburg, PA, USA, pp. 15--21. [URL] [BibTex]

V. Pekar, M. Krkoska, and S. Staab. (2004). Feature Weighting for Co-occurrence-based Classification of Words. Proceedings of the 20th International Conference on Computational Linguistics (COLING), Stroudsburg, PA, USA. [DOI] [URL] [BibTex]

2003

V. Pekar and S. Staab. (2003). Word Classification Based on Combined Measures of Distributional and Semantic Similarity. Proceedings of the Tenth Conference on European Chapter of the Association for Computational Linguistics (EACL) - Volume 2, Stroudsburg, PA, USA, pp. 147--150. [DOI] [URL] [BibTex]

2002

V. Pekar and S. Staab. (2002). Taxonomy Learning: Factoring the Structure of a Taxonomy into a Semantic Classification Decision. Proceedings of the 19th International Conference on Computational Linguistics (COLING) - Volume 1, Stroudsburg, PA, USA, pp. 1--7. [DOI] [URL] [BibTex]

2001

V. Pekar. (2001). Specification in Terms of Interactional Properties As a Way to Optimize the Representation of Spatial Expressions. Proceedings of the ACL Workshop on Temporal and Spatial Information Processing - Volume 13, Stroudsburg, PA, USA, pp. 21--28. [DOI] [URL] [BibTex]

Industry talks

Predicting Consumer Spending from Social Media. Data Science Seminar. Bank of England, London, UK, November 2016.

Design Considerations for a SaaS System for Text Analytics of Customer Feedback (with Mark Rogers). The ASC One Day Conference Series. The Association for Survey Computing, London, UK. November 2018. [URL]

How can Artificial Intelligence help SMEs? (with Victoria Uren). SME Practical Insights – Workshop Series. Aston University. Birmingham, UK. 18th July 2019. [URL]