Effective Spell Checking Methods Using Clustering Algorithms

This paper presents a novel approach to spell checking using dictionary clustering. The main goal is to reduce the number of times distances have to be calculated when finding target words for misspellings. The method is unsupervised and combines the application of anomalous pattern initialization and partition around medoids (PAM). To evaluate the method, we used an English misspelling list compiled using real examples extracted from the Birkbeck spelling error corpus.Final Published versio

DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic Languages

Spelling error correction is the task of identifying and rectifying misspelled words in texts. It is a potential and active research topic in Natural Language Processing because of numerous applications in human language understanding. The phonetically or visually similar yet semantically distinct characters make it an arduous task in any language. Earlier efforts on spelling error correction in Bangla and resource-scarce Indic languages focused on rule-based, statistical, and machine learning-based methods which we found rather inefficient. In particular, machine learning-based approaches, which exhibit superior performance to rule-based and statistical methods, are ineffective as they correct each character regardless of its appropriateness. In this work, we propose a novel detector-purificator-corrector framework based on denoising transformers by addressing previous issues. Moreover, we present a method for large-scale corpus creation from scratch which in turn resolves the resource limitation problem of any left-to-right scripted language. The empirical outcomes demonstrate the effectiveness of our approach that outperforms previous state-of-the-art methods by a significant margin for Bangla spelling error correction. The models and corpus are publicly available at https://tinyurl.com/DPCSpell.Comment: 23 pages, 4 figures, and 7 table

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information

OCRspell: An interactive spelling correction system for OCR errors in text

In this thesis we describe a spelling correction system designed specifically for OCR (Optical Character Recognition) generated text that selects candidate words through the use of information gathered from multiple knowledge sources. This system for text correction is based on static and dynamic device mappings, approximate string matching, and n-gram analysis. Our statistically based, Bayesian system incorporates a learning feature that collects confusion information at the collection and document levels. An evaluation of the new system is presented as well

A Comparative Study of Text Summarization on E-mail Data Using Unsupervised Learning Approaches

Over the last few years, email has met with enormous popularity. People send and receive a lot of messages every day, connect with colleagues and friends, share files and information. Unfortunately, the email overload outbreak has developed into a personal trouble for users as well as a financial concerns for businesses. Accessing an ever-increasing number of lengthy emails in the present generation has become a major concern for many users. Email text summarization is a promising approach to resolve this challenge. Email messages are general domain text, unstructured and not always well developed syntactically. Such elements introduce challenges for study in text processing, especially for the task of summarization. This research employs a quantitative and inductive methodologies to implement the Unsupervised learning models that addresses summarization task problem, to efficiently generate more precise summaries and to determine which approach of implementing Unsupervised clustering models outperform the best. The precision score from ROUGE-N metrics is used as the evaluation metrics in this research. This research evaluates the performance in terms of the precision score of four different approaches of text summarization by using various combinations of feature embedding technique like Word2Vec /BERT model and hybrid/conventional clustering algorithms. The results reveals that both the approaches of using Word2Vec and BERT feature embedding along with hybrid PHA-ClusteringGain k-Means algorithm achieved increase in the precision when compared with the conventional k-means clustering model. Among those hybrid approaches performed, the one using Word2Vec as feature embedding method attained 55.73% as maximum precision value

NLP and ML Methods for Pre-processing, Clustering and Classification of Technical Logbook Datasets

Technical logbooks are a challenging and under-explored text type in automated event identification. These texts are typically short and written in non-standard yet technical language, posing challenges to off-the-shelf NLP pipelines. These datasets typically represent a domain (a technical field such as automotive) and an application (e.g., maintenance). The granularity of issue types described in these datasets additionally leads to class imbalance, making it challenging for models to accurately predict which issue each logbook entry describes. In this research, we focus on the problem of technical issue pre-processing, clustering, and classification by considering logbook datasets from the automotive, aviation, and facility maintenance domains. We developed MaintNet, a collaborative open source library including logbook datasets from various domains and a pre-processing pipeline to clean unstructured datasets. Additionally, we adapted a feedback loop strategy from computer vision for handling extreme class imbalance, which resamples the training data based on its error in the prediction process. We further investigated the benefits of using transfer learning from sources within the same domain (but different applications), from within the same application (but different domains), and from all available data to improve the performance of the classification models. Finally, we evaluated several data augmentation approaches including synonym replacement, random swap, and random deletion to address the issue of data scarcity in technical logbooks

Using compression to identify acronyms in text

Text mining is about looking for patterns in natural language text, and may be defined as the process of analyzing text to extract information from it for particular purposes. In previous work, we claimed that compression is a key technology for text mining, and backed this up with a study that showed how particular kinds of lexical tokens---names, dates, locations, etc.---can be identified and located in running text, using compression models to provide the leverage necessary to distinguish different token types (Witten et al., 1999)Comment: 10 pages. A short form published in DCC200