In India, there is a problem of reaction time in case of emergencies. Due to the problem of delayed reaction time during emergencies, it is important to discuss how we can use NLP to speed up the deployment of emergen...
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In India, there is a problem of reaction time in case of emergencies. Due to the problem of delayed reaction time during emergencies, it is important to discuss how we can use NLP to speed up the deployment of emergen...
详细信息
ISBN:
(数字)9781665419604
ISBN:
(纸本)9781665429986
In India, there is a problem of reaction time in case of emergencies. Due to the problem of delayed reaction time during emergencies, it is important to discuss how we can use NLP to speed up the deployment of emergency services. This paper aims to classify a statement made by a caller to a Police Station in case of an emergency. We have four categories: Police, Ambulance, Fire Brigade, and Non-crime. We will be analyzing the statements made by the caller during the phone call, which we will use to try to extract information and opinions regarding events that are happening, or have already occurred. This paper focuses on analyzing the statement made by the person and classify the statement, which will help to predict if the caller needs Police Assistance, Medical Assistance, or Fire Brigade Assistance, which will help in better decision making and will speed up the process of dispatching the required emergency service(s).
Almost all the problems in NLP are solved using various techniques from machine learning to Deep Learning. Still, there is mystery in language localization. NLP problems are unclear for languages other than English. T...
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Almost all the problems in NLP are solved using various techniques from machine learning to Deep Learning. Still, there is mystery in language localization. NLP problems are unclear for languages other than English. T...
ISBN:
(数字)9781728172743
ISBN:
(纸本)9781728172750
Almost all the problems in NLP are solved using various techniques from machine learning to Deep Learning. Still, there is mystery in language localization. NLP problems are unclear for languages other than English. The problems may be named as Entity Extraction, OcR or classification and prediction in sequence modelling. The amount of people using local language (Tamil, Telegu, Hindi etc) in the social media is increasing, so it is important to automate the process of classifying those contents. Here, the aim is to classify the Tamil news articles to its related topics (Sports, cinema, Politics). In the existing work they have approached traditional machine learning methods with TFIDF of words as features. In this work we have compared the existing TFIDF feature learning along with Pre-Trained embeddings given to convolutional Neural Networks (cNN). We found that cNN with pretrained embeddings gave better F1 score compare to TFIDF feature learned with Support Vector Machine (SVM), Naive Bayes (NB) algorithm.
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