Deep Learning for Technical Document Classification

In large technology companies, the requirements for managing and organizing technical documents created by engineers and managers have increased dramatically in recent years, which has led to a higher demand for more scalable, accurate, and automated document classification. Prior studies have only focused on processing text for classification, whereas technical documents often contain multimodal information. To leverage multimodal information for document classification to improve the model performance, this paper presents a novel multimodal deep learning architecture, TechDoc, which utilizes three types of information, including natural language texts and descriptive images within documents and the associations among the documents. The architecture synthesizes the convolutional neural network, recurrent neural network, and graph neural network through an integrated training process. We applied the architecture to a large multimodal technical document database and trained the model for classifying documents based on the hierarchical International Patent Classification system. Our results show that TechDoc presents a greater classification accuracy than the unimodal methods and other state-of-the-art benchmarks. The trained model can potentially be scaled to millions of real-world multimodal technical documents, which is useful for data and knowledge management in large technology companies and organizations.Comment: 16 pages, 8 figures, 9 table

Book Acquisition and Technical Processing Pattern in Government First Grade College Libraries of Karnataka

The present study examines the book acquisition and technical processing pattern in the libraries of Government First Grade College (GFGC) of Karnataka. The study includes 249 colleges spread across the state and shows various methods of book procurement and technical processing employed in the selected college libraries. It is found that 90% of colleges procure library documents based on demand from faculty and student. Almost 45% of colleges use the publisher’s catalogue tool to select documents. At most (83.94%) colleges indicated that insufficient funding was the main obstacle to acquiring reading material. In addition, more than 51% of libraries have adopted the DDC classification scheme for organizing documents, and 45% of libraries provide Online Public Access Catalogue (OPAC) services. The collection development pattern in the library is low compared to the user strength. The technical processing that consist classification and cataloguing and found nearly half of the surveyed libraries have not classified their books with any standard scheme

Technical Processing of University Library: A Theoretical Study

Technical services operations comprise of ordering, claiming and receipt of materials, cataloging and classification of materials; and serials control. Beside these, other technical services operations contain circulation, documents, foreign language and special collections, and bibliographic instruction in technical services areas. This leads me to believe that the distinction between technical services and public or reader services in individual libraries is based on custom and tradition arising out of incidental circumstances, rather than on fundamental principle. In this article, I tried to provide some basic aspect regarding technical processing which help in maintaining the library bitterly. In this article, I include basic aspect of acquisition, classification, cataloguing and information retrieval. Beside this I tried to focus on library management software which is most important for technical processing now a day

Аналіз україномовних перекладів текстів технічної документації

(uk) У статті аналізується якість текстів технічної документації, виокремлюються критерії якісного перекладу та демонструються шляхи його забезпечення. Помилки, екстраговані з текстів технічної докуметації, подаються у вигляді класифікації, аналізуються їхні причини й пропонуються альтернативні варіанти перекладу, які демонструють способи усунення цих помилок.(en) The article deals with the analysis of the technical documents texts quality while highlighting the criteria of the qualified translation as well as the means of its provision. The mistakes extracted from the technical documents texts have been given in a classification followed by the analysis of their reasons. Alternative translation sequences are given, they demonstrate the ways for removing abovementioned mistakes

Аналіз україномовних перекладів текстів технічної документації

(uk) У статті аналізується якість текстів технічної документації, виокремлюються критерії якісного перекладу та демонструються шляхи його забезпечення. Помилки, екстраговані з текстів технічної докуметації, подаються у вигляді класифікації, аналізуються їхні причини й пропонуються альтернативні варіанти перекладу, які демонструють способи усунення цих помилок.(en) The article deals with the analysis of the technical documents texts quality while highlighting the criteria of the qualified translation as well as the means of its provision. The mistakes extracted from the technical documents texts have been given in a classification followed by the analysis of their reasons. Alternative translation sequences are given, they demonstrate the ways for removing abovementioned mistakes

An Analysis of the Effectiveness of Applying a Machine Learning Approach for Classification of Technical Documents in Knowledge Discovery Systems

An important component of knowledge management (KM) is the organization of documents for quick and easy access. One advantageous and effective way of organizing these documents is to group them by a fixed set of specific knowledge categories. For large-scale technical teams, the number of categories can reach thousands or even tens of thousands, which makes the aforementioned cataloging especially useful. Text classification (TC) is a sophisticated process that involves data pre-processing, transformation, dimensionality reduction, application of classification techniques, classifier evaluation, and classifier validation. TC remains a prominent research topic and still depends on human work rather than on machine learning (ML). It is a relatively new area of research and remains in a premature phase. The goal is to develop and evaluate a prototype model that uses ML algorithms to classify technical documentation in a KM system for technical teams of financial institutions involved in software development projects. This research contributes to the field of KM by determining whether an ML approach constitutes a feasible solution for TC in knowledge discovery