# Keyphrase Extraction from Disaster-related Tweets

arXiv: https://arxiv.org/abs/1910.07897 · HTML: https://mlsanity.com/paper/1910.07897/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2019.

## Built on by (most cited first, 12 of 12)

- [Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World](https://mlsanity.com/paper/2210.08523/) (2022)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [PWESuite: Phonetic Word Embeddings and Tasks They Facilitate](https://mlsanity.com/paper/2304.02541/) (2023)
- [Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey](https://mlsanity.com/paper/2209.07272/) (2022)
- [Infrastructure Ombudsman: Mining Future Failure Concerns from Structural Disaster Response](https://mlsanity.com/paper/2402.13528/) (2024)
- [Enhancing Keyphrase Extraction from Academic Articles Using Section Structure Information](https://mlsanity.com/paper/2505.14149/) (2025)
- [Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs](https://mlsanity.com/paper/2606.26485/) (2026)
- [Deep Keyphrase Completion](https://mlsanity.com/paper/2111.01910/) (2021)
- [Sarcasm Detection in a Disaster Context](https://mlsanity.com/paper/2308.08156/) (2023)
- [Abstractive Opinion Tagging](https://mlsanity.com/paper/2101.06880/) (2021)
- [Utilizing Social Media Attributes for Enhanced Keyword Detection: An IDF-LDA Model Applied to Sina Weibo](https://mlsanity.com/paper/2306.07978/) (2023)
- [Zero-Shot Keyphrase Generation: Investigating Specialized Instructions and Multi-Sample Aggregation on Large Language Models](https://mlsanity.com/paper/2503.00597/) (2025)

## Abstract (arXiv)

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