Survey on Hinglish to English Translation and Classification Techniques

Code-mixing is the process of using many languages in one sentence and has a widespread occurrence in multilingual communities. It is particularly prevalent in texts on social media. Due to the widespread usage of social networking sites, a substantial amount of unstructured text is produced. Hinglish, i.e. code-mixed Hindi and English, is a frequent occurrence in everyday language use in India. Hence, a translation process is required to help monolingual users and to aid in the comprehension of language processing models. In this paper, we study the effective techniques for classification and translation tasks and also find gaps and challenges in the current research domain. After comparing a few existing methodologies for machine translation, a framework which showed an improvement in task of translation over the previous methods is proposed. &nbsp

Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation

Research on Korean grammatical error correction (GEC) is limited compared to other major languages such as English and Chinese. We attribute this problematic circumstance to the lack of a carefully designed evaluation benchmark for Korean. Thus, in this work, we first collect three datasets from different sources (Kor-Lang8, Kor-Native, and Kor-Learner) to cover a wide range of error types and annotate them using our newly proposed tool called Korean Automatic Grammatical error Annotation System (KAGAS). KAGAS is a carefully designed edit alignment & classification tool that considers the nature of Korean on generating an alignment between a source sentence and a target sentence, and identifies error types on each aligned edit. We also present baseline models fine-tuned over our datasets. We show that the model trained with our datasets significantly outperforms the public statistical GEC system (Hanspell) on a wider range of error types, demonstrating the diversity and usefulness of the datasets.Comment: Add affiliation and email addres

Graphical error mining for linguistic annotated corpora

Corpora contain linguistically annotated data. Producing these annotations is a complex process that easily leads to inconsistencies within the annotation. Since corpora are used to evaluate automatic language processing systems the evaluation may suffer when there are too many errors within the data. This thesis focuses on finding erroneous annotations within corpora. To detect sequence annotation errors within part-of-speech tags we implemented the algorithm introduced by Dickinson and Meurers (2003). Additionally for structured annotations we choose the approach shown in Boyd et al.(2008) that targets inconsistency within dependency structures. We designed and built a graphical user interface (GUI) that is easy to handle and user-friendly. Implementing state-of-the-art algorithms for error detection with an user-friendly interface increase the operation domain because the algorithms can be used by a wider audience without deeper knowledge of computers. It provides even non-expert users with the capability to find inconsistent pos tags and dependency structures within a corpus. We evaluate the system using the German TIGER corpus and the English Penn Treebank. For the TIGER corpus we also perform a manual evaluation where we sample 115 6-grams and check manually if these contain errors. We find that 94.96% are erroneous and it is easy to decide the correct tag as a human. For 4.20% we can say that these are errors but determining the correct tag is very to difficult. In total we detect errors with a precision of 99.16%. Only one case (0.84%) is not caused by inconsistency but constitutes genuine ambiguity

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

The Taming of the Shrew - non-standard text processing in the Digital Humanities

Natural language processing (NLP) has focused on the automatic processing of newspaper texts for many years. With the growing importance of text analysis in various areas such as spoken language understanding, social media processing and the interpretation of text material from the humanities, techniques and methodologies have to be reviewed and redefined since so called non-standard texts pose challenges on the lexical and syntactic level especially for machine-learning-based approaches. Automatic processing tools developed on the basis of newspaper texts show a decreased performance for texts with divergent characteristics. Digital Humanities (DH) as a field that has risen to prominence in the last decades, holds a variety of examples for this kind of texts. Thus, the computational analysis of the relationships of Shakespeare’s dramatic characters requires the adjustment of processing tools to English texts from the 16th-century in dramatic form. Likewise, the investigation of narrative perspective in Goethe’s ballads calls for methods that can handle German verse from the 18th century. In this dissertation, we put forward a methodology for NLP in a DH environment. We investigate how an interdisciplinary context in combination with specific goals within projects influences the general NLP approach. We suggest thoughtful collaboration and increased attention to the easy applicability of resulting tools as a solution for differences in the store of knowledge between project partners. Projects in DH are not only constituted by the automatic processing of texts but are usually framed by the investigation of a research question from the humanities. As a consequence, time limitations complicate the successful implementation of analysis techniques especially since the diversity of texts impairs the transferability and reusability of tools beyond a specific project. We answer to this with modular and thus easily adjustable project workflows and system architectures. Several instances serve as examples for our methodology on different levels. We discuss modular architectures that balance time-saving solutions and problem-specific implementations on the example of automatic postcorrection of the output text from an optical character recognition system. We address the problem of data diversity and low resource situations by investigating different approaches towards non-standard text processing. We examine two main techniques: text normalization and tool adjustment. Text normalization aims at the transformation of non-standard text in order to assimilate it to the standard whereas tool adjustment concentrates on the contrary direction of enabling tools to successfully handle a specific kind of text. We focus on the task of part-of-speech tagging to illustrate various approaches toward the processing of historical texts as an instance for non-standard texts. We discuss how the level of deviation from a standard form influences the performance of different methods. Our approaches shed light on the importance of data quality and quantity and emphasize the indispensability of annotations for effective machine learning. In addition, we highlight the advantages of problem-driven approaches where the purpose of a tool is clearly formulated through the research question. Another significant finding to emerge from this work is a summary of the experiences and increased knowledge through collaborative projects between computer scientists and humanists. We reflect on various aspects of the elaboration and formalization of research questions in the DH and assess the limitations and possibilities of the computational modeling of humanistic research questions. An emphasis is placed on the interplay of expert knowledge with respect to a subject of investigation and the implementation of tools for that purpose and the thereof resulting advantages such as the targeted improvement of digital methods through purposeful manual correction and error analysis. We show obstacles and chances and give prospects and directions for future development in this realm of interdisciplinary research