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

Extracting keywords from tweets

Nos últimos anos, uma enorme quantidade de informações foi disponibilizada na Internet. As redes sociais estão entre as que mais contribuem para esse aumento no volume de dados. O Twitter, em particular, abriu o caminho, enquanto plataforma social, para que pessoas e organizações possam interagir entre si, gerando grandes volumes de dados a partir dos quais é possível extrair informação útil. Uma tal quantidade de dados, permitirá por exemplo, revelar-se importante se e quando, vários indivíduos relatarem sintomas de doença ao mesmo tempo e no mesmo lugar. Processar automaticamente um tal volume de informações e obter a partir dele conhecimento útil, torna-se, no entanto, uma tarefa impossível para qualquer ser humano. Os extratores de palavras-chave surgem neste contexto como uma ferramenta valiosa que visa facilitar este trabalho, ao permitir, de uma forma rápida, ter acesso a um conjunto de termos caracterizadores do documento. Neste trabalho, tentamos contribuir para um melhor entendimento deste problema, avaliando a eficácia do YAKE (um algoritmo de extração de palavras-chave não supervisionado) em cima de um conjunto de tweets, um tipo de texto, caracterizado não só pelo seu reduzido tamanho, mas também pela sua natureza não estruturada. Embora os extratores de palavras-chave tenham sido amplamente aplicados a textos genéricos, como a relatórios, artigos, entre outros, a sua aplicabilidade em tweets é escassa e até ao momento não foi disponibilizado formalmente nenhum conjunto de dados. Neste trabalho e por forma a contornar esse problema optámos por desenvolver e tornar disponível uma nova coleção de dados, um importante contributo para que a comunidade científica promova novas soluções neste domínio. O KWTweet foi anotado por 15 anotadores e resultou em 7736 tweets anotados. Com base nesta informação, pudemos posteriormente avaliar a eficácia do YAKE! contra 9 baselines de extração de palavra-chave não supervisionados (TextRank, KP-Miner, SingleRank, PositionRank, TopicPageRank, MultipartiteRank, TopicRank, Rake e TF.IDF). Os resultados obtidos demonstram que o YAKE! tem um desempenho superior quando comparado com os seus competidores, provando-se assim a sua eficácia neste tipo de textos. Por fim, disponibilizamos uma demo que visa demonstrar o funcionamento do YAKE! Nesta plataforma web, os utilizadores têm a possibilidade de fazer uma pesquisa por utilizador ou hashtag e dessa forma obter as palavras chave mais relevantes através de uma nuvem de palavra

Open Data

Open data is freely usable, reusable, or redistributable by anybody, provided there are safeguards in place that protect the data’s integrity and transparency. This book describes how data retrieved from public open data repositories can improve the learning qualities of digital networking, particularly performance and reliability. Chapters address such topics as knowledge extraction, Open Government Data (OGD), public dashboards, intrusion detection, and artificial intelligence in healthcare

Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Peer reviewe

Knowledge technologies process and cultures - improving information and knowledge sharing at the Amateur Swimming Association (ASA)

Over the last few years the ASA determined KM as a priority to assist with reducing knowledge loss, realising information assets and reducing work duplication by attempting to implement IKM tools and strategies. This research employed a pragmatic viewpoint, using a mix of both quantitative and qualitative methods to check reliability, to ensure validity while undertaking the task of implementing the IKM tools. Using a case study strategy and action research was justified, as to be pragmatic the researcher needed to understand the extent of the problem within a specified context. The research discussed in this thesis, provides a new framework for implementing KM tools; focusing on the NSO category, which the case study organisation falls into. The literature agrees enlisting influential members onto the project is vital for success; however, the findings suggested that success was not only tied to this buy-in alone, but also to the organisation s ability to retain these members for the duration of the project. The research proposed the use of a newly developed tool within the new framework, as an approach to reduce the time it takes to undertake traditional social network analysis of the organisation, as it became clear that there was a need for a method of producing updated results of the SNA, which would span the length of long projects within organisations with significantly high staff turn-over rates. Privacy was given as a factor to consider the in literature; however, the findings from this study indicated that a majority of the participants were comfortable with the system. Email knowledge extraction, and email social network systems are not new concepts, however this research presents EKESNA; a novel tool that combines both concepts in a way that allows for the continuous discovery, visualisation, and analysis of knowledge networks around specified topics of interest within an organisation; linking conversations to specific expert knowledge. EKESNA s continuous discovery of the organisation s knowledge network affords members up-to-date data to inform business process reengineering. This is a potentially ground breaking new tool that has the possibility of transforming the KM landscape in NSOs as well as a whole range of other kinds of enterprises

LAMBRETTA: Learning to Rank for Twitter Soft Moderation

To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run LAMBRETTA on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media.Comment: 44th IEEE Symposium on Security & Privacy (S&P 2023

Extensible metadata management framework for personal data lake

Common Internet users today are inundated with a deluge of diverse data being generated and siloed in a variety of digital services, applications, and a growing body of personal computing devices as we enter the era of the Internet of Things. Alongside potential privacy compromises, users are facing increasing difficulties in managing their data and are losing control over it. There appears to be a de facto agreement in business and scientific fields that there is critical new value and interesting insight that can be attained by users from analysing their own data, if only it can be freed from its silos and combined with other data in meaningful ways. This thesis takes the point of view that users should have an easy-to-use modern personal data management solution that enables them to centralise and efficiently manage their data by themselves, under their full control, for their best interests, with minimum time and efforts. In that direction, we describe the basic architecture of a management solution that is designed based on solid theoretical foundations and state of the art big data technologies. This solution (called Personal Data Lake - PDL) collects the data of a user from a plurality of heterogeneous personal data sources and stores it into a highly-scalable schema-less storage repository. To simplify the user-experience of PDL, we propose a novel extensible metadata management framework (MMF) that: (i) annotates heterogeneous data with rich lineage and semantic metadata, (ii) exploits the garnered metadata for automating data management workflows in PDL – with extensive focus on data integration, and (iii) facilitates the use and reuse of the stored data for various purposes by querying it on the metadata level either directly by the user or through third party personal analytics services. We first show how the proposed MMF is positioned in PDL architecture, and then describe its principal components. Specifically, we introduce a simple yet effective lineage manager for tracking the provenance of personal data in PDL. We then introduce an ontology-based data integration component called SemLinker which comprises two new algorithms; the first concerns generating graph-based representations to express the native schemas of (semi) structured personal data, and the second algorithm metamodels the extracted representations to a common extensible ontology. SemLinker outputs are utilised by MMF to generate user-tailored unified views that are optimised for querying heterogeneous personal data through low-level SPARQL or high-level SQL-like queries. Next, we introduce an unsupervised automatic keyphrase extraction algorithm called SemCluster that specialises in extracting thematically important keyphrases from unstructured data, and associating each keyphrase with ontological information drawn from an extensible WordNet-based ontology. SemCluster outputs serve as semantic metadata and are utilised by MMF to annotate unstructured contents in PDL, thus enabling various management functionalities such as relationship discovery and semantic search. Finally, we describe how MMF can be utilised to perform holistic integration of personal data and jointly querying it in native representations

Knowledge-Based Techniques for Scholarly Data Access: Towards Automatic Curation

Accessing up-to-date and quality scientific literature is a critical preliminary step in any research activity. Identifying relevant scholarly literature for the extents of a given task or application is, however a complex and time consuming activity. Despite the large number of tools developed over the years to support scholars in their literature surveying activity, such as Google Scholar, Microsoft Academic search, and others, the best way to access quality papers remains asking a domain expert who is actively involved in the field and knows research trends and directions. State of the art systems, in fact, either do not allow exploratory search activity, such as identifying the active research directions within a given topic, or do not offer proactive features, such as content recommendation, which are both critical to researchers. To overcome these limitations, we strongly advocate a paradigm shift in the development of scholarly data access tools: moving from traditional information retrieval and filtering tools towards automated agents able to make sense of the textual content of published papers and therefore monitor the state of the art. Building such a system is however a complex task that implies tackling non trivial problems in the fields of Natural Language Processing, Big Data Analysis, User Modelling, and Information Filtering. In this work, we introduce the concept of Automatic Curator System and present its fundamental components.openDottorato di ricerca in InformaticaopenDe Nart, Dari