Dasha Bogdanova


If You Can’t Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking
Dasha Bogdanova | Jennifer Foster | Daria Dzendzik | Qun Liu
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We show that a neural approach to the task of non-factoid answer reranking can benefit from the inclusion of tried-and-tested handcrafted features. We present a neural network architecture based on a combination of recurrent neural networks that are used to encode questions and answers, and a multilayer perceptron. We show how this approach can be combined with additional features, in particular, the discourse features used by previous research. Our neural approach achieves state-of-the-art performance on a public dataset from Yahoo! Answers and its performance is further improved by incorporating the discourse features. Additionally, we present a new dataset of Ask Ubuntu questions where the hybrid approach also achieves good results.


This is how we do it: Answer Reranking for Open-domain How Questions with Paragraph Vectors and Minimal Feature Engineering
Dasha Bogdanova | Jennifer Foster
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


Detecting Semantically Equivalent Questions in Online User Forums
Dasha Bogdanova | Cícero dos Santos | Luciano Barbosa | Bianca Zadrozny
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

Learning Hybrid Representations to Retrieve Semantically Equivalent Questions
Cícero dos Santos | Luciano Barbosa | Dasha Bogdanova | Bianca Zadrozny
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


DCU: Aspect-based Polarity Classification for SemEval Task 4
Joachim Wagner | Piyush Arora | Santiago Cortes | Utsab Barman | Dasha Bogdanova | Jennifer Foster | Lamia Tounsi
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

Cross-Language Authorship Attribution
Dasha Bogdanova | Angeliki Lazaridou
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents a novel task of cross-language authorship attribution (CLAA), an extension of authorship attribution task to multilingual settings: given data labelled with authors in language X, the objective is to determine the author of a document written in language Y , where X is different from Y . We propose a number of cross-language stylometric features for the task of CLAA, such as those based on sentiment and emotional markers. We also explore an approach based on machine translation (MT) with both lexical and cross-language features. We experimentally show that MT could be used as a starting point to CLAA, since it allows good attribution accuracy to be achieved. The cross-language features provide acceptable accuracy while using jointly with MT, though do not outperform lexical features.


Modelling Fixated Discourse in Chats with Cyberpedophiles
Dasha Bogdanova | Paolo Rosso | Thamar Solorio
Proceedings of the Workshop on Computational Approaches to Deception Detection

On the Impact of Sentiment and Emotion Based Features in Detecting Online Sexual Predators
Dasha Bogdanova | Paolo Rosso | Thamar Solorio
Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis