Nicolaj Filrup Rasmussen


2020

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Buhscitu at SemEval-2020 Task 7: Assessing Humour in Edited News Headlines Using Hand-Crafted Features and Online Knowledge Bases
Kristian Nørgaard Jensen | Nicolaj Filrup Rasmussen | Thai Wang | Marco Placenti | Barbara Plank
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes a system that aims at assessing humour intensity in edited news headlines as part of the 7th task of SemEval-2020 on “Humor, Emphasis and Sentiment”. Various factors need to be accounted for in order to assess the funniness of an edited headline. We propose an architecture that uses hand-crafted features, knowledge bases and a language model to understand humour, and combines them in a regression model. Our system outperforms two baselines. In general, automatic humour assessment remains a difficult task.

2019

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Cross-Domain Sentiment Classification using Vector Embedded Domain Representations
Nicolaj Filrup Rasmussen | Kristian Nørgaard Jensen | Marco Placenti | Thai Wang
Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing

Due to the differences between reviews in different product categories, creating a general model for cross-domain sentiment classification can be a difficult task. This paper proposes an architecture that incorporates domain knowledge into a neural sentiment classification model. In addition to providing a cross-domain model, this also provides a quantifiable representation of the domains as numeric vectors. We show that it is possible to cluster the domain vectors and provide qualitative insights into the inter-domain relations. We also a) present a new data set for sentiment classification that includes a domain parameter and preprocessed data points, and b) perform an ablation study in order to determine whether some word groups impact performance.