Indonesian Ministry of Finance had endorsed 10 Key Opinion Leaders to help promoting government retail bonds SBR011 during selling period of 25 May-16 June 2022. This study analyzed effectiveness of the endorsement by using several indicators; engagement rate, enthusiasm rate and sentiment analysis of feedbacks from KOL audiens. Data was gathered from social media Instagram and TikTok social platform used by the KOL to post their marketing contents. This paper found that the endorsement is quite effective to promote the SBR011 and yields mostly positive feedback on the marketing campaign. Definisi Key Opinion Leader (KOL) Menurut influencermarketinghub, KOL dideskripsikan sebagai "person or organization who has expert product knowledge and influence in a respective field. They are trusted by relevant interest groups and have significant effects on consumer behavior" [7].

Information extraction (IE) from documents is an intensive area of research with a large set of industrial applications. Current state-of-the-art methods focus on scanned documents with approaches combining computer vision, natural language processing and layout representation. We propose to challenge the usage of computer vision in the case where both token style and visual representation are available (i.e native PDF documents). Our experiments on three real-world complex datasets demonstrate that using token style attributes based embedding instead of a raw visual embedding in LayoutLM model is beneficial. Depending on the dataset, such an embedding yields an improvement of 0.18% to 2.29% in the weighted F1-score with a decrease of 30.7% in the final number of trainable parameters of the model, leading to an improvement in both efficiency and effectiveness.

The pre-conference day included a wide array of workshops and tutorials, spanning a range of topics. The tutorials covered the latest techniques in machine learning (including deep learning and BERT), information extraction, causal inference, word embeddings, and the use of Twitter API v2, and addressed use cases including mis/disinformation and business decision making. The workshops included those on Cyber Social Threats (CySoc), Social Sensing (SocialSens): Special Edition on Belief Dynamics, Images in Online Political Communication (PhoMemes), Novel Evaluation Approaches for Text Classification Systems on Social Media (NEATCLasS), Social Media for Emergency Response (SoMER), Data for the Wellbeing of Most Vulnerable, and News Media and Computational Journalism (MEDIATE). A Data Challenge was also held on this day, with a special focus on Health-Related Discourse on the Web. For the main conference, 454 reviewers and 86 senior PC members evaluated 455 papers submitted to the conference, with 122 being accepted for publication.

Signal Processing: Image Communication manuscript No. (will be inserted by the editor) Abstract While storing invoice content as metadata comparison of 9 AC per manually processed invoice and to avoid paper document processing may be the future 2 AC per automated processing of one invoice based on trend, almost all of daily issued invoices are still surveys in 2004 and 2003 respectively. A 2016 report by printed on paper or generated in digital formats such the Institute of Finance and Management [2] suggested as PDFs. In this paper, we introduce the OCRMiner that the average cost to process an invoice was $12.90. The system on Scanned Receipt OCR and Information Extraction is designed to process the document in a similar way a (SROIE) at ICDAR 2019 [3] or the Mobile-Captured human reader uses, i.e. to employ different layout and Image Document Recognition for Vietnamese Receipts text attributes in a coordinated decision. Still, annotated benchmark invoice consists of a set of interconnected modules that start datasets are not generally available due to confidential with (possibly erroneous) character-based output from information, and the published papers do not offer a standard OCR system and allow to apply different detailed dataset descriptions and error analyses of the techniques and to expand the extracted knowledge at content. Moreover, although receipts and invoices have each step. Using an open source OCR, the system is some common attributes, their analyses differ vastly able to recover the invoice data in 90% for English and due to complex graphical layouts and richer content in 88% for the Czech set. In 2006, Lewis et al. [6] published the IIT 1 Introduction Complex Document Information Processing Test Collection (IIT-CDIP) based on the Legacy Tobacco Documents Automatic invoice processing systems gain significant Library, containing roughly 40 millions scanned interest of large companies who deal with enormous pages for evaluation of document information processing numbers of invoices each day, due to not only their tasks.

Get expert advice on the technical aspects of modern data science, with a focus on stochastic/statistical analysis rather than kernel-based methods.

As this century progresses, businesses are discovering that the most incredible way to gain the best customer service is to know them deeply. With AI advancing at an exponential rate, it's become possible for companies to use artificial intelligence (AI) to gain valuable insight into their customers. In particular, advances in artificial intelligence are leading to increased efficiency in customer service throughout different industry vertices. Machine learning and AI-based interactive voice response systems have created a new paradigm for what customers and customer service agents can expect from these technologies. When applied correctly, artificial intelligence will enhance the customer experience in various ways, from identifying their interests through sentiment analysis to gathering data about their preferences. AI is the production and display of intelligence by computers and machines instead of humans.

Paris – An analysis of 21 billion Facebook friendships shows that children from poorer homes are likely to earn more later in life if they grow up in areas where they can become friends with wealthier children. It has long been believed that having rich friends can help children rise up out of poverty, but previous research has had small sample sizes or limited data, according to two studies published in the journal Nature on Monday. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page. We humbly apologize for the inconvenience.

Key-value pair extraction is at the heart of document processing. To understand how it works and what it brings, it's necessary to explain the concepts behind the words. We will then see why it's so important for companies and organizations of all industries. KVPs are two related data items, a key, and a value. The key defines the data and is fixed, and the value is variable and describes the key.

This article will review how to use Azure Bicep to deploy a Cognitive Services resource and an Azure Container Instances resource to create a container image that can be used for text analytics. Before you move forward, take a moment to read the below article that explains in detail the architecture and objectives. Let's analyze the Bicep template. Create a new file in your working directory and name it'main.bicep'. Note we declare two resources: the Azure Cognitive Service resource and the Azure Container Instance resource.

In this paper, we propose an end-to-end sentiment-aware conversational agent based on two models: a reply sentiment prediction model, which leverages the context of the dialogue to predict an appropriate sentiment for the agent to express in its reply; and a text generation model, which is conditioned on the predicted sentiment and the context of the dialogue, to produce a reply that is both context and sentiment appropriate. Additionally, we propose to use a sentiment classification model to evaluate the sentiment expressed by the agent during the development of the model. This allows us to evaluate the agent in an automatic way. Both automatic and human evaluation results show that explicitly guiding the text generation model with a pre-defined set of sentences leads to clear improvements, both regarding the expressed sentiment and the quality of the generated text.