Search Results for author:
Found 7 papers, 2 papers with code
Variation in the Expression and Annotation of Emotions: A Wizard of Oz Pilot Study
Finally, for the third premise, we observed a positive correlation between the internal-external agreement on emotion labels and the personality traits conscientiousness and extraversion.
An Emotional Journey: Detecting Emotion Trajectories in Dutch Customer Service Dialogues
The ability to track fine-grained emotions in customer service dialogues has many real-world applications, but has not been studied extensively.
EmoTwiCS: A Corpus for Modelling Emotion Trajectories in Dutch Customer Service Dialogues on Twitter
In our business-oriented corpus, we view emotions as dynamic attributes of the customer that can change at each utterance of the conversation.
A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks
In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets.
LT3 at SemEval-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines
This paper presents two different systems for the SemEval shared task 7 on Assessing Humor in Edited News Headlines, sub-task 1, where the aim was to estimate the intensity of humor generated in edited headlines.
Identifying Cognates in English-Dutch and French-Dutch by means of Orthographic Information and Cross-lingual Word Embeddings
This paper investigates the validity of combining more traditional orthographic information with cross-lingual word embeddings to identify cognate pairs in English-Dutch and French-Dutch.
A Classification-Based Approach to Cognate Detection Combining Orthographic and Semantic Similarity Information
This paper presents proof-of-concept experiments for combining orthographic and semantic information to distinguish cognates from non-cognates.
