Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management
Authors: Wenlin Yao, Cheng Zhang, Shiva Saravanan, Ruihong Huang, Ali Mostafavi532-539
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The evaluation on two hurricanes, Harvey and Florence, shows that using only 1-2 person-hours of human supervision, the rapidly trained weakly supervised classifiers outperform supervised classifiers trained using more than ten thousand annotated tweets created in over 50 person-hours. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Texas A&M University 2Department of Civil Engineering, Texas A&M University 3Department of Computer Science, Princeton University |
| Pseudocode | No | The paper describes algorithms and models (e.g., SLPA, Bi LSTM) in text and with diagrams, but it does not include any formal pseudocode blocks or sections labeled 'Algorithm'. |
| Open Source Code | Yes | Our system is available at https://github.com/wenlinyao/AAAI20-EventRecognitionForDisaster. |
| Open Datasets | No | The paper describes how tweets were retrieved using the GNIP API and processed to create datasets for Hurricane Harvey and Florence. However, it does not provide public access (e.g., a link, DOI, or explicit statement of public availability) to these specific collected datasets. |
| Dataset Splits | Yes | We train and evaluate a supervised classifier (multi-channel) using annotated tweets under the 10-fold cross validation setting. [...] we randomly sample a certain percentage of tweets from nine training folds as training data, ranging from 0.1 to 0.9 in increments of 0.1. [...] For training both our systems and the baseline systems, we used around 65k and 69.8k unlabeled tweets for Harvey and Florence respectively that were posted 12 hours (half a day) preceding the test time period and are therefore strictly separated from the tweets used for evaluation. |
| Hardware Specification | Yes | the bootstrapping process stopped after 9 iterations, and each iteration took around 10 minutes using a NVIDIA s Ge Force GTX 1080 GPU. |
| Software Dependencies | No | The paper mentions software components and tools like Bi LSTM encoders, pre-trained GloVe, and Adam optimizer, but does not specify their version numbers (e.g., 'TensorFlow 2.x' or 'Python 3.x'). |
| Experiment Setup | Yes | For all Bi LSTM encoders, we use one hidden-layer of 300 units, pre-trained Glo Ve (Pennington, Socher, and Manning 2014) word embeddings of 300 dimensions, Adam optimizer (Kingma and Ba 2014) with a learning rate of 0.0001. [...] To deal with imbalanced distributions of event categories, we re-scale the prediction loss of each class (proportional to 1 Class Size) so that smaller classes are weighted more heavily in the final loss function. [...] we randomly cover 20% of keywords occurrences in every training epoch [...] the confidence score was initially set at 0.9 and lowered by 0.1 each time when the number of selected tweets is less than 100. |