Word Position Information Research Articles

Capturing word positions does help: A multi-element hypergraph gated attention network for document classification

Over the last few years, graph-based methods have manifested a significant enhancement in document mining applications such as spam detection, news recommendation, and legal document classification. However, existing graph-based methods have a limited ability to utilize word position and multi-element information within the documents, limiting their effectiveness in practical application. To mitigate this limitation, we propose a novel multi-element hypergraph gated attention network that can capture word position and multi-element information for accurate document classification. Specifically, a new multi-element hypergraph is first proposed to describe the word position, sentence, and full content within the document. Then, a new multi-element homogenization module is applied to mitigate heterogeneity of constructed hypergraph. Meantime, a new hypergraph gated attention module is proposed to filter noise in the constructed hypergraph and derive various element representations that incorporate word position information. Finally, a new block-wise read-out module is designed to fuse learned element representations into comprehensive document representations for classification. Extensive experiments conducted on several real-world datasets demonstrate that the proposed method not only outperforms related state-of-the-art methods but is also faster, making it suitable for a wide range of practical applications. For instance, our method achieved an accuracy improvement of 1.1 % over the best comparative method on some datasets while also operating at a faster speed. Additionally, it demonstrated an impressive 14 % improvement in accuracy over the well-known Generative Pre-trained Transformer 3.5 (GPT-3.5) on one dataset.

Community Governance Based on Sentiment Analysis: Towards Sustainable Management and Development

The promotion of community governance by digital means is an important research topic in developing smart cities. Currently, community governance is mostly based on reactive response, which lacks timely and proactive technical means for emergency monitoring. The easiest way for residents to contact their properties is to call the property call center, and the call centers of many properties store many speech data. However, text sentiment classification in community scenes still faces challenges such as small corpus size, one-sided sentiment feature extraction, and insufficient sentiment classification accuracy. To address such problems, we propose a novel community speech text sentiment classification algorithm combining two-channel features and attention mechanisms to obtain effective emotional information and provide decision support for the emergency management of public emergencies. Firstly, text vectorization based on word position information is proposed, and a SKEP-based community speech–text enhancement model is constructed to obtain the corresponding corpus. Secondly, a dual-channel emotional text feature extraction method that integrates spatial and temporal sequences is proposed to extract diverse emotional features effectively. Finally, an improved cross-entropy loss function suitable for community speech text is proposed for model training, which can achieve sentiment analysis and obtain all aspects of community conditions. The proposed method is conducive to improving community residents’ sense of happiness, satisfaction, and fulfillment, enhancing the effectiveness and resilience of urban community governance.

An OCR Engine for Printed Receipt Images using Deep Learning Techniques

The digitization of receipts and invoices, and the recording of expenses in industry and accounting have begun to be used in the field of finance tracking. However, 100%success in character recognition for document digitization has not yet been achieved. In this study, a new Optical Character Recognition (OCR) engine called Nacsoft OCR was developed on Turkish receipt data by using artificial intelligence methods. The proposed OCR engine has been compared to widely used engines, Easy OCR, Tesseract OCR, and the Google Vision API. The benchmarking was made on English and Turkish receipts, and the accuracies of OCR engines in terms of character recognition and their speeds are presented. It is known that OCR character recognition engines perform better at word recognition when provided word position information. Therefore, the performance of the Nacsoft OCR engine in determining the word position was also compared with the performance of the other OCR engines, and the results were presented.

A context-blocks model for identifying clinical relationships in patient records

BackgroundPatient records contain valuable information regarding explanation of diagnosis, progression of disease, prescription and/or effectiveness of treatment, and more. Automatic recognition of clinically important concepts and the identification of relationships between those concepts in patient records are preliminary steps for many important applications in medical informatics, ranging from quality of care to hypothesis generation.MethodsIn this work we describe an approach that facilitates the automatic recognition of eight relationships defined between medical problems, treatments and tests. Unlike the traditional bag-of-words representation, in this work, we represent a relationship with a scheme of five distinct context-blocks determined by the position of concepts in the text. As a preliminary step to relationship recognition, and in order to provide an end-to-end system, we also addressed the automatic extraction of medical problems, treatments and tests. Our approach combined the outcome of a statistical model for concept recognition and simple natural language processing features in a conditional random fields model. A set of 826 patient records from the 4th i2b2 challenge was used for training and evaluating the system.ResultsResults show that our concept recognition system achieved an F-measure of 0.870 for exact span concept detection. Moreover the context-block representation of relationships was more successful (F-Measure = 0.775) at identifying relationships than bag-of-words (F-Measure = 0.402). Most importantly, the performance of the end-to-end system of relationship extraction using automatically extracted concepts (F-Measure = 0.704) was comparable to that obtained using manually annotated concepts (F-Measure = 0.711), and their difference was not statistically significant.ConclusionsWe extracted important clinical relationships from text in an automated manner, starting with concept recognition, and ending with relationship identification. The advantage of the context-blocks representation scheme was the correct management of word position information, which may be critical in identifying certain relationships. Our results may serve as benchmark for comparison to other systems developed on i2b2 challenge data. Finally, our system may serve as a preliminary step for other discovery tasks in medical informatics.

The interfering effect of serial cognitive structures in long-term memory on word-order in short-term memory

This experiment investigated the retention of word-position information, independent of item information, using a distractor STM paradigm. Triads of words, which formed a serial cognitive structure presumably in LTM, were presented to 192 Ss in natural or scrambled orders with serial-, backwards-, or free-recall instructions. Triads selected from different taxonomic classes were presented on the eight trials to ensure high item retention. Sine half of the Ss made no item errors at all, their responses were scored for retention of word-position information. There were few position errors with the natural ordered triads with either serial- or backswards-recall instructions. There was a build-up of position errors across trials for the scrambled order-serial recall group. The scrambled order-backwards recall group had a high rate of position errors beginning on Trial 1. With free-recall instructions, the scrambled triads were transposed frequently back to their natural serial order. These data are incompatible with features of some box models of memory.