Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning

In the insurance industry nowadays, data is carrying the major asset and playing a key role. There is a wealth of information available to insurance transporters nowadays. We can identify three major eras in the insurance industry's more than 700-year history. The industry follows the manual era from the 15th century to 1960, the systems era from 1960 to 2000, and the current digital era, i.e., 2001-20X0. The core insurance sector has been decided by trusting data analytics and implementing new technologies to improve and maintain existing practices and maintain capital together. This has been the highest corporate object in all three periods.AI techniques have been progressively utilized for a variety of insurance activities in recent years. In this study, we give a comprehensive general assessment of the existing research that incorporates multiple artificial intelligence (AI) methods into all essential insurance jobs. Our work provides a more comprehensive review of this research, even if there have already been a number of them published on the topic of using artificial intelligence for certain insurance jobs. We study algorithms for learning, big data, block chain, data mining, and conversational theory, and their applications in insurance policy, claim prediction, risk estimation, and other fields in order to comprehensively integrate existing work in the insurance sector using AI approaches

Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input. However, when handling imbalanced class data, DBN encounters low performance as other machine learning algorithms. In this paper, the genetic algorithm (GA) and bootstrap sampling are incorporated into DBN to lessen the drawbacks occurs when imbalanced class datasets are used. The performance of the proposed algorithm is compared with DBN and is evaluated using performance metrics. The results showed that there is an improvement in performance when Evolutionary DBN with bootstrap sampling is used to handle imbalanced class datasets

Keyphrase Generation: A Multi-Aspect Survey

Extractive keyphrase generation research has been around since the nineties, but the more advanced abstractive approach based on the encoder-decoder framework and sequence-to-sequence learning has been explored only recently. In fact, more than a dozen of abstractive methods have been proposed in the last three years, producing meaningful keyphrases and achieving state-of-the-art scores. In this survey, we examine various aspects of the extractive keyphrase generation methods and focus mostly on the more recent abstractive methods that are based on neural networks. We pay particular attention to the mechanisms that have driven the perfection of the later. A huge collection of scientific article metadata and the corresponding keyphrases is created and released for the research community. We also present various keyphrase generation and text summarization research patterns and trends of the last two decades.Comment: 10 pages, 5 tables. Published in proceedings of FRUCT 2019, the 25th Conference of the Open Innovations Association FRUCT, Helsinki, Finlan

Automatic Keyword Tagging With Machine Learning Approach

With the explosive growth of information in the Internet age, the use of keywords has become the main tool for users to search for content of interest in a large amount of information. Keyword tagging can be divided into in-text keyword extraction and out-of-text keyword assignment. Keyword extraction is an important area in natural language processing (NLP), but the technology still has a lot of immaturity. Traditional keyword extraction methods are difficult to meet the commonly desired three characteristics simultaneously, i.e., understandability, relevance and good coverage, and thus even now in Web 2.0 many tags of web pages are still tagged manually. In this thesis, we propose a novel unsupervised keyword extraction method that integrates word embedding (GloVe and fastText) with clustering (Affinity Propagation, Mean Shift and K-means). We use semantic relevance to cluster the terms in a document, and extract the noun phrase nearest to the center of the cluster as the keyword. This method ensures that the extracted keywords satisfy the above three characteristics at the same time. Our computer simulation results based on Hulth-2003, Krapivin-2009 and Nguyen-2007 datasets show that the proposed method outperforms all other existing methods in terms of common evaluation metrics such as Precision, Recall and F1-Score. This thesis also proposes a CNN-BiLSTM model for keyword assignment, which uses word embedding method and attention mechanism. This model overcomes the limitation of single CNN model in ignoring the semantic and syntactic information of the input context, and effectively avoids the problem of gradient disappearance or gradient diffusion in traditional RNNs. Moreover, the use of attention mechanism can highlight important information and avoid the influence of invalid information on text sentiment and classification. Experimental results on three datasets, i.e., 20 Newsgroups, IMDB, SemEval 2018 task-1, show that the proposed keyword assignment method outperforms previous methods in terms of common evaluation metrics such as F1-Score, Accuracy and AUC, indicating the wide applicability of our method to various datasets