Fig 1 - uploaded by Scott B. Miles
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Illustration of the new natural language processing method for computing the frequency that user-specified propositions are expressed in a corpus that describes a disaster recovery process. The frequency of these occurrences can be plotted across time.
Citations
... This procedure is used to investigate, process, and organize data. Further, we can examine textual relationships between structured and unstructured data using preprocessing [11]. ...
... Furthermore, [24] combines the twitter and Instagram data sets related to earthquakes. Three scenarios were used to evaluate the CRF model, namely: CRF combined with LSTM CRF, Optimization of CRF, and combination of LSTM with CRF. ...
... The results show that CRF Optimization is superior to other models. To evaluate this model [24] developed a natural language processing (NLP) model to analyze a collection of disaster recovery texts. The presented method uses statistical syntax-based semantic matching. ...
... An automated natural language processing (NLP) technique looks for, compile, characterise, structure, and collate news coverage descriptions of disaster recovery which are important to grow the appeal of the analysis of text-heavy corpora in furthering the study of pre and post disaster planning [8]. ...
A lot of news sources picked up on Typhoon Rai (also known locally as Typhoon Odette), along with fake news outlets. The study honed in on the issue, to create a model that can identify between legitimate and illegitimate news articles. With this in mind, we chose the following machine learning algorithms in our development: Logistic Regression, Random Forest and Multinomial Naive Bayes. Bag of Words, TF-IDF and Lemmatization were implemented in the Model. Gathering 160 datasets from legitimate and illegitimate sources, the machine learning was trained and tested. By combining all the machine learning techniques, the Combined BOW model was able to reach an accuracy of 91.07%, precision of 88.33%, recall of 94.64%, and F1 score of 91.38% and Combined TF-IDF model was able to reach an accuracy of 91.18%, precision of 86.89%, recall of 94.64%, and F1 score of 90.60%.
... An automated natural language processing (NLP) technique looks for, compile, characterise, structure, and collate news coverage descriptions of disaster recovery which are important to grow the appeal of the analysis of text-heavy corpora in furthering the study of pre and post disaster planning [8]. ...
A lot of news sources picked up on Typhoon Rai (also known locally as Typhoon Odette), along with fake news outlets. The study honed in on the issue, to create a model that can identify between legitimate and illegitimate news articles. With this in mind, we chose the following machine learning algorithms in our development: Logistic Regression, Random Forest and Multinomial Naive Bayes. Bag of Words, TF-IDF and Lemmatization were implemented in the Model. Gathering 160 datasets from legitimate and illegitimate sources, the machine learning was trained and tested. By combining all the machine learning techniques, the Combined BOW model was able to reach an accuracy of 91.07%, precision of 88.33%, recall of 94.64%, and F1 score of 91.38% and Combined TF-IDF model was able to reach an accuracy of 91.18%, precision of 86.89%, recall of 94.64%, and F1 score of 90.60%.
... Following Lin et al. (2018), our extraction procedure is decomposed into two phases: one for speed and one for precision. The first phase serves as a coarse-grained filter to efficiently collect an initial pool of ATOMIC knowledge candidates P i for each datapoint d i ∈ D: if there is word overlap between d i and a ATOMIC candidate event, the ATOMIC candidate is added to P i . ...
... To overcome the limitation, a system for damage collection and report process is required. In addition, techniques for unstructured report processing [28,41,42] may be adopted to enable automatic analysis and further reduce the labor and time cost. ...
This study developed a chatbot to improve the efficiency of government activation of mine safety procedures during natural disasters. Taiwan has a comprehensive governmental system dedicated to responding to frequent natural disasters, and the Bureau of Mines has instituted clear procedures to ensure the delivery of disaster alarms and damage reports. However, the labor- and time-consumption procedures are inefficient. In this study, we propose a system framework for disaster-related information retrieval and immediate notifications to support the execution of mine safety procedures. The framework utilizes instant messaging (IM) applications as the user interface to look up information and send messages to announce the occurrence of disaster events. We evaluated the efficiency of the procedures before and after adopting the system and achieved a time-cost reduction of 55.8 min among three types of disaster events. The study has proven the feasibility of adopting novel techniques for decision-making and assures the improvement of the efficiency and effectiveness of the procedure activation.
... McDaniels and Chang characterize lifeline failure interdependencies using manual content analysis of newspapers and technical reports [94,95]. In contrast, Lin et al. [96] make use of natural language processing to analyze newspaper stories from New Zealand after the Canterbury earthquakes with the goal of tracking long-term recovery. Doubleday et al. [97] use daily bicycle and pedestrian activity as an indicator of disaster recovery. ...
... Another alternative data source, Lin et al. [96] use natural language processing to generate recovery data from news stories about disasters. While their analysis is focused on long-term recovery, a similar approach could be used for modeling shorter-term restoration, perhaps using a different source such as Twitter data [101,102,103]. ...
Disaster recovery is widely regarded as the least understood phase of the disaster cycle. In particular, the literature around lifeline infrastructure restoration modeling frequently mentions the lack of empirical quantitative data available. Despite limitations, there is a growing body of research on modeling lifeline infrastructure restoration, often developed using empirical quantitative data. This study reviews this body of literature and identifies the data collection and usage patterns present across modeling approaches to inform future efforts using empirical quantitative data. We classify the modeling approaches into simulation, optimization, and statistical modeling. The number of publications in this domain has increased over time with the most rapid growth of statistical modeling. Electricity infrastructure restoration is most frequently modeled, followed by the restoration of multiple infrastructures, water infrastructure, and transportation infrastructure. Interdependency between multiple infrastructures is increasingly considered in recent literature. Researchers gather the data from a variety of sources, including collaborations with utility companies, national databases, and post-event damage and restoration reports. This study provides discussion and recommendations around data usage practices within the lifeline restoration modeling field. Following the recommendations would facilitate the development of a community of practice around restoration modeling and provide greater opportunities for future data sharing.
... From both our user study and post-hoc evaluation with other models, we found that while entailment models offer small improvements over the word-vector-averaging baselines, our application requires more than detecting sentence-level entailment. 19 For more measurement examples drawn from our earthquake recovery data, please refer to Lin et al. (2018). Entities. ...
We consider the case of a domain expert who wishes to explore the extent to which a particular idea is expressed in a text collection. We propose the task of semantically matching the idea, expressed as a natural language proposition, against a corpus. We create two preliminary tasks derived from existing datasets, and then introduce a more realistic one on disaster recovery designed for emergency managers, whom we engaged in a user study. On the latter, we find that a new model built from natural language entailment data produces higher-quality matches than simple word-vector averaging, both on expert-crafted queries and on ones produced by the subjects themselves. This work provides a proof-of-concept for such applications of semantic matching and illustrates key challenges.
