Evangelia Spiliopoulou


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Event-Related Bias Removal for Real-time Disaster Events
Salvador Medina Maza | Evangelia Spiliopoulou | Eduard Hovy | Alexander Hauptmann
Findings of the Association for Computational Linguistics: EMNLP 2020

Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volumes of data in real-time. This poses a complex problem due to the large amount of posts that do not contain any actionable information. Furthermore, the classification of information in real-time systems requires training on out-of-domain data, as we do not have any data from a new emerging crisis. Prior work focuses on models pre-trained on similar event types. However, those models capture unnecessary event-specific biases, like the location of the event, which affect the generalizability and performance of the classifiers on new unseen data from an emerging new event. In our work, we train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.

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Definition Frames: Using Definitions for Hybrid Concept Representations
Evangelia Spiliopoulou | Artidoro Pagnoni | Eduard Hovy
Proceedings of the 28th International Conference on Computational Linguistics

Advances in word representations have shown tremendous improvements in downstream NLP tasks, but lack semantic interpretability. In this paper, we introduce Definition Frames (DF), a matrix distributed representation extracted from definitions, where each dimension is semantically interpretable. DF dimensions correspond to the Qualia structure relations: a set of relations that uniquely define a term. Our results show that DFs have competitive performance with other distributional semantic approaches on word similarity tasks.


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Event Detection Using Frame-Semantic Parser
Evangelia Spiliopoulou | Eduard Hovy | Teruko Mitamura
Proceedings of the Events and Stories in the News Workshop

Recent methods for Event Detection focus on Deep Learning for automatic feature generation and feature ranking. However, most of those approaches fail to exploit rich semantic information, which results in relatively poor recall. This paper is a small & focused contribution, where we introduce an Event Detection and classification system, based on deep semantic information retrieved from a frame-semantic parser. Our experiments show that our system achieves higher recall than state-of-the-art systems. Further, we claim that enhancing our system with deep learning techniques like feature ranking can achieve even better results, as it can benefit from both approaches.

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Linguistic Markers of Influence in Informal Interactions
Shrimai Prabhumoye | Samridhi Choudhary | Evangelia Spiliopoulou | Christopher Bogart | Carolyn Rose | Alan W Black
Proceedings of the Second Workshop on NLP and Computational Social Science

There has been a long standing interest in understanding ‘Social Influence’ both in Social Sciences and in Computational Linguistics. In this paper, we present a novel approach to study and measure interpersonal influence in daily interactions. Motivated by the basic principles of influence, we attempt to identify indicative linguistic features of the posts in an online knitting community. We present the scheme used to operationalize and label the posts as influential or non-influential. Experiments with the identified features show an improvement in the classification accuracy of influence by 3.15%. Our results illustrate the important correlation between the structure of the language and its potential to influence others.