scholarly journals Extraction and Analysis of Social Networks Data to Detect Traffic Accidents

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Nestor Suat-Rojas ◽  
Camilo Gutierrez-Osorio ◽  
Cesar Pedraza
Keyword(s):  
Social Networks ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Low Cost ◽  
Information Source ◽  
Named Entity Recognition ◽  
Industrial Area ◽  
Entity Recognition ◽  
Detection Methods ◽  
Traffic Information

Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods.

scholarly journals A Low-Cost Named Entity Recognition Research Based on Active Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Han Huang ◽  
Hongyu Wang ◽  
Dawei Jin
Keyword(s):  
Active Learning ◽  
Language Processing ◽  
Selection Process ◽  
Conditional Random Field ◽  
Low Cost ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Training Set ◽  
Processing Technologies ◽  
Named Entity

Named entity recognition (NER) is an indispensable and very important part of many natural language processing technologies, such as information extraction, information retrieval, and intelligent Q & A. This paper describes the development of the AL-CRF model, which is a NER approach based on active learning (AL). The algorithmic sequence of the processes performed by the AL-CRF model is the following: first, the samples are clustered using the k-means approach. Then, stratified sampling is performed on the produced clusters in order to obtain initial samples, which are used to train the basic conditional random field (CRF) classifier. The next step includes the initiation of the selection process which uses the criterion of entropy. More specifically, samples having the highest entropy values are added to the training set. Afterwards, the learning process is repeated, and the CRF classifier is retrained based on the obtained training set. The learning and the selection process of the AL is running iteratively until the harmonic mean F stabilizes and the final NER model is obtained. Several NER experiments are performed on legislative and medical cases in order to validate the AL-CRF performance. The testing data include Chinese judicial documents and Chinese electronic medical records (EMRs). Testing indicates that our proposed algorithm has better recognition accuracy and recall rate compared to the conventional CRF model. Moreover, the main advantage of our approach is that it requires fewer manually labelled training samples, and at the same time, it is more effective. This can result in a more cost effective and more reliable process.

scholarly journals Evaluation of Named Entity Recognition Algorithms in Short Texts

2017 ◽  
Author(s):  
Edgar Casasola Murillo ◽  
Raquel Fonseca
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Redes Sociales ◽  
Named Entity ◽  
Processing Strategies ◽  
New Type ◽  
Manual Processing ◽  
The Given

Abstract: One of the major consequences of the growth of social networks has been the generation of huge volumes of content. The text that is generated in social networks constitutes a new type of content, that is short, informal, lacking grammar in some cases, and noise prone. Given the volume of information that is produced every day, a manual processing of this data is unpractical, causing the need of exploring and applying automatic processing strategies, like Entity Recognition (ER). It becomes necessary to evaluate the performance of traditional ER algorithms in corpus with those characteristics. This paper presents the results of applying AlchemyAPI y Dandelion API algorithms in a corpus provided by The SemEval-2015 Aspect Based Sentiment Analysis Conference. The entities recognized by each algorithm were compared against the ones annotated in the collection in order to calculate their precision and recall. Dandelion API got better results than AlchemyAPI with the given corpus.  Spanish Abstract: Una de las principales consecuencias del auge actual de las redes sociales es la generación de grandes volúmenes de información. El texto generado en estas redes corresponde a un nuevo género de texto: corto, informal, gramaticalmente deficiente y propenso a ruido. Debido a la tasa de producción de la información, el procesamiento manual resulta poco práctico, surgiendo así la necesidad de aplicar estrategias de procesamiento automático, como Reconocimiento de Entidades (RE). Debido a las características del contenido, surge además la necesidad de evaluar el desempeño de los algoritmos tradicionales, en corpus extraídos de estas redes sociales. Este trabajo presenta los resultados obtenidos al aplicar los algoritmos de AlchemyAPI y Dandelion API en un corpus provisto por la conferencia The SemEval-2015 Aspect Based Sentiment Analysis. Las entidades reconocidas por cada algoritmo fueron comparadas con las anotadas en la colección, para calcular su precisión y exhaustividad. Dandelion API obtuvo mejores resultados que AlchemyAPI en el corpus dado.

scholarly journals Evaluating named entity recognition tools for extracting social networks from novels

2019 ◽  
Vol 5 ◽  
pp. e189 ◽  
Author(s):  
Niels Dekker ◽  
Tobias Kuhn ◽  
Marieke van Erp
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Computer Assisted ◽  
Early 20Th Century ◽  
Automatic Extraction ◽  
Named Entities ◽  
Named Entity

The analysis of literary works has experienced a surge in computer-assisted processing. To obtain insights into the community structures and social interactions portrayed in novels, the creation of social networks from novels has gained popularity. Many methods rely on identifying named entities and relations for the construction of these networks, but many of these tools are not specifically created for the literary domain. Furthermore, many of the studies on information extraction from literature typically focus on 19th and early 20th century source material. Because of this, it is unclear if these techniques are as suitable to modern-day literature as they are to those older novels. We present a study in which we evaluate natural language processing tools for the automatic extraction of social networks from novels as well as their network structure. We find that there are no significant differences between old and modern novels but that both are subject to a large amount of variance. Furthermore, we identify several issues that complicate named entity recognition in our set of novels and we present methods to remedy these. We see this work as a step in creating more culturally-aware AI systems.

scholarly journals Evaluating social network extraction for classic and modern fiction literature

2018 ◽  
Author(s):  
Niels Dekker ◽  
Tobias Kuhn ◽  
Marieke van Erp
Keyword(s):  
Social Networks ◽  
Science Fiction ◽  
19Th Century ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Computer Assisted ◽  
Named Entities ◽  
Named Entity ◽  
Modern Fiction

The analysis of literary works has experienced a surge in computer-assisted processing. To obtain insights into the community structures and social interactions portrayed in novels the creation of social networks from novels has gained popularity. Many methods rely on identifying named entities and relations for the construction of these networks, but many of these tools are not specifically created for the literary domain. Furthermore, many of the studies on information extraction from literature typically focus on 19th century source material. Because of this, it is unclear if these techniques are as suitable to modern-day science fiction and fantasy literature as they are to those 19th century classics. We present a study to compare classic literature to modern literature in terms of performance of natural language processing tools for the automatic extraction of social networks as well as their network structure. We find that there are no significant differences between the two sets of novels but that both are subject to a high amount of variance. Furthermore, we identify several issues that complicate named entity recognition in modern novels and we present methods to remedy these.

scholarly journals Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of Kuwait

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 442
Author(s):  
Abdullah M. Almeshal ◽  
Mohammad R. Alenezi ◽  
Abdullah K. Alshatti
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Mean Squared Error ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Extreme Environments ◽  
3D Models ◽  
Operating Conditions ◽  
Aerial Vehicle

This study presents the first accuracy assessment of a low cost small unmanned aerial vehicle (sUAV) in reconstructing three dimensional (3D) models of traffic accidents at extreme operating environments. To date, previous studies have focused on the feasibility of adopting sUAVs in traffic accidents photogrammetry applications as well as the accuracy at normal operating conditions. In this study, 3D models of simulated accident scenes were reconstructed using a low-cost sUAV and cloud-based photogrammetry platform. Several experiments were carried out to evaluate the measurements accuracy at different flight altitudes during high temperature, low light, scattered rain and dusty high wind environments. Quantitative analyses are presented to highlight the precision range of the reconstructed traffic accident 3D model. Reported results range from highly accurate to fairly accurate represented by the root mean squared error (RMSE) range between 0.97 and 4.66 and a mean percentage absolute error (MAPE) between 1.03% and 20.2% at normal and extreme operating conditions, respectively. The findings offer an insight into the robustness and generalizability of UAV-based photogrammetry method for traffic accidents at extreme environments.

scholarly journals Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

2018 ◽  
Author(s):  
Aldo Hernandez-Suarez ◽  
Gabriel Sanchez-Perez ◽  
Karina Toscano-Medina ◽  
Hector Perez-Meana ◽  
Jose Portillo-Portillo ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Named Entity Recognition ◽  
Name Entity Recognition ◽  
Spatial Modelling ◽  
Entity Recognition ◽  
Language Models ◽  
Geographic Space ◽  
Named Entity ◽  
Estimation Function

In recent years, online social networks have received important consideration in spatial modelling fields given the critical information that can be extracted from them for events in real time; one of the most latent issues is that regarding various natural disasters such as earthquakes. Although it is possible to retrieve data from these social networks with embedded geographic information provided by GPS, in many cases this is not possible. An alternative solution is to reconstruct specific locations using probabilistic language models, more specifically those based on Name Entity Recognition (NER), which extracts names from a user’s description about an event occurring in a specific place (e.g., a collapsed building on a specific avenue). In this work, we present a methodology to use twitter as a social sensor system for disasters. The methodology scores NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space is proposed. The proposed methodology is evaluated with tweets related to the 2017 earthquake in Mexico.

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