Research Area:  Machine Learning

Abstract:

While keyphrase extraction has received considerable attention in recent years, relatively few studies exist on extracting keyphrases from social media platforms such as Twitter, and even fewer for extracting disaster-related keyphrases from such sources. During a disaster, keyphrases can be extremely useful for filtering relevant tweets that can enhance situational awareness. Previously, joint training of two different layers of a stacked Recurrent Neural Network for keyword discovery and keyphrase extraction had been shown to be effective in extracting keyphrases from general Twitter data. We improve the model-s performance on both general Twitter data and disaster-related Twitter data by incorporating contextual word embeddings, POS-tags, phonetics, and phonological features. Moreover, we discuss the shortcomings of the often used F1-measure for evaluating the quality of predicted keyphrases with respect to the ground truth annotations. Instead of the F1-measure, we propose the use of embedding-based metrics to better capture the correctness of the predicted keyphrases. In addition, we also present a novel extension of an embedding-based metric. The extension allows one to better control the penalty for the difference in the number of ground-truth and predicted keyphrases.

Keywords:  
Keyphrase Extraction
Disaster-Related Tweets
Machine Learning
Deep Learning

Author(s) Name:  Jishnu Ray Chowdhury , Cornelia Caragea , Doina Caragea

Publisher name:  ACM

DOI:  10.1145/3308558.3313696

Volume Information:  

Paper Link:   https://dl.acm.org/doi/abs/10.1145/3308558.3313696