Identification of Fine-Grained Location Mentions in Crisis Tweets

• URL: http://arxiv.org/abs/2111.06334v1
• Date: Thu, 11 Nov 2021 17:48:03 GMT
• Title: Identification of Fine-Grained Location Mentions in Crisis Tweets
• Authors: Sarthak Khanal, Maria Traskowsky, Doina Caragea
• Abstract summary: We assemble two tweet crisis datasets and manually annotate them with specific location types. The first dataset contains tweets from a mixed set of crisis events, while the second dataset contains tweets from the global COVID-19 pandemic. We investigate the performance of state-of-the-art deep learning models for sequence tagging on these datasets, in both in-domain and cross-domain settings.
• Score: 7.627299398469962
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identification of fine-grained location mentions in crisis tweets is central in transforming situational awareness information extracted from social media into actionable information. Most prior works have focused on identifying generic locations, without considering their specific types. To facilitate progress on the fine-grained location identification task, we assemble two tweet crisis datasets and manually annotate them with specific location types. The first dataset contains tweets from a mixed set of crisis events, while the second dataset contains tweets from the global COVID-19 pandemic. We investigate the performance of state-of-the-art deep learning models for sequence tagging on these datasets, in both in-domain and cross-domain settings.

Related papers

• CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification [51.58605842457186]
  We present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data.
  arXiv  Detail & Related papers  (2023-10-23T07:01:09Z)
• Where did you tweet from? Inferring the origin locations of tweets based on contextual information [0.2320417845168326]
  Less than 1% of tweets are geotagged; in both cases--point location or bounding place information. A major issue with tweets is that Twitter users can be at location A and exchange conversations specific to location B. We propose a framework that uses machine-level natural language understanding to identify tweets that conceivably contain their origin location information.
  arXiv  Detail & Related papers  (2022-11-18T01:33:01Z)
• CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization [62.77066949111921]
  This paper presents CrisisLTLSum, the largest dataset of local crisis event timelines available to date. CrisisLTLSum contains 1,000 crisis event timelines across four domains: wildfires, local fires, traffic, and storms. Our initial experiments indicate a significant gap between the performance of strong baselines compared to the human performance on both tasks.
  arXiv  Detail & Related papers  (2022-10-25T17:32:40Z)
• One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement [59.14062241534754]
  We propose a two-stage framework for one-shot medical landmark localization. In stage I, we learn an end-to-end cascade of global alignment and local deformations, under the guidance of novel loss functions. In stage II, we explore self-consistency for selecting reliable pseudo labels and cross-consistency for semi-supervised learning.
  arXiv  Detail & Related papers  (2022-07-31T15:42:28Z)
• Dynamic Focus-aware Positional Queries for Semantic Segmentation [94.6834904076914]
  We propose a simple yet effective query design for semantic segmentation termed Dynamic Focus-aware Positional Queries. Our framework achieves SOTA performance and outperforms Mask2former by clear margins of 1.1%, 1.9%, and 1.1% single-scale mIoU with ResNet-50, Swin-T, and Swin-B backbones.
  arXiv  Detail & Related papers  (2022-04-04T05:16:41Z)
• Traffic Event Detection as a Slot Filling Problem [18.61490760235035]
  We introduce the new problem of extracting fine-grained traffic information from Twitter streams by making publicly available the two (constructed) traffic-related datasets from Belgium and the Brussels capital region. We propose the use of several methods that process the two subtasks either separately or in a joint setting, and we evaluate the effectiveness of the proposed methods for solving the traffic event detection problem.
  arXiv  Detail & Related papers  (2021-09-13T15:02:40Z)
• Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes [131.9067467127761]
  We focus on improving the action recognition by fully-utilizing the information of scenes and collecting new data. Specifically, we adopt a strong human detector to detect spatial location of each frame. We then apply action recognition models to learn thetemporal information from video frames on both the HIE dataset and new data with diverse scenes from the internet.
  arXiv  Detail & Related papers  (2020-10-16T13:08:50Z)
• I-AID: Identifying Actionable Information from Disaster-related Tweets [0.0]
  Social media plays a significant role in disaster management by providing valuable data about affected people, donations and help requests. We propose I-AID, a multimodel approach to automatically categorize tweets into multi-label information types. Our results indicate that I-AID outperforms state-of-the-art approaches in terms of weighted average F1 score by +6% and +4% on the TREC-IS dataset and COVID-19 Tweets, respectively.
  arXiv  Detail & Related papers  (2020-08-04T19:07:50Z)
• Phase Consistent Ecological Domain Adaptation [76.75730500201536]
  We focus on the task of semantic segmentation, where annotated synthetic data are aplenty, but annotating real data is laborious. The first criterion, inspired by visual psychophysics, is that the map between the two image domains be phase-preserving. The second criterion aims to leverage ecological statistics, or regularities in the scene which are manifest in any image of it, regardless of the characteristics of the illuminant or the imaging sensor.
  arXiv  Detail & Related papers  (2020-04-10T06:58:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.