Proceedings of the Second Workshop on Annotation of Corpora for Research in the Humanities (ACRH-2). 29 November 2012, Lisbon, Portugal

Proceedings of the Second Workshop on Annotation of Corpora for Research in the Humanities (ACRH-2), held in Lisbon, Portugal on 29 November 2012

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification

The strength of long short-term memory neural networks (LSTMs) that have been applied is more located in handling sequences of variable length than in handling geometric variability of the image patterns. In this paper, an end-to-end convolutional LSTM neural network is used to handle both geometric variation and sequence variability. The best results for LSTMs are often based on large-scale training of an ensemble of network instances. We show that high performances can be reached on a common benchmark set by using proper data augmentation for just five such networks using a proper coding scheme and a proper voting scheme. The networks have similar architectures (convolutional neural network (CNN): five layers, bidirectional LSTM (BiLSTM): three layers followed by a connectionist temporal classification (CTC) processing step). The approach assumes differently scaled input images and different feature map sizes. Three datasets are used: the standard benchmark RIMES dataset (French); a historical handwritten dataset KdK (Dutch); the standard benchmark George Washington (GW) dataset (English). Final performance obtained for the word-recognition test of RIMES was 96.6%, a clear improvement over other state-of-the-art approaches which did not use a pre-trained network. On the KdK and GW datasets, our approach also shows good results. The proposed approach is deployed in the Monk search engine for historical-handwriting collections

Distributional Measures of Semantic Distance: A Survey

The ability to mimic human notions of semantic distance has widespread applications. Some measures rely only on raw text (distributional measures) and some rely on knowledge sources such as WordNet. Although extensive studies have been performed to compare WordNet-based measures with human judgment, the use of distributional measures as proxies to estimate semantic distance has received little attention. Even though they have traditionally performed poorly when compared to WordNet-based measures, they lay claim to certain uniquely attractive features, such as their applicability in resource-poor languages and their ability to mimic both semantic similarity and semantic relatedness. Therefore, this paper presents a detailed study of distributional measures. Particular attention is paid to flesh out the strengths and limitations of both WordNet-based and distributional measures, and how distributional measures of distance can be brought more in line with human notions of semantic distance. We conclude with a brief discussion of recent work on hybrid measures

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

Disambiguoiva morfologinen jäsennys probabilistisilla sekvenssimalleilla

A morphological tagger is a computer program that provides complete morphological descriptions of sentences. Morphological taggers find applications in many NLP fields. For example, they can be used as a pre-processing step for syntactic parsers, in information retrieval and machine translation. The task of morphological tagging is closely related to POS tagging but morphological taggers provide more fine-grained morphological information than POS taggers. Therefore, they are often applied to morphologically complex languages, which extensively utilize inflection, derivation and compounding for encoding structural and semantic information. This thesis presents work on data-driven morphological tagging for Finnish and other morphologically complex languages. There exists a very limited amount of previous work on data-driven morphological tagging for Finnish because of the lack of freely available manually prepared morphologically tagged corpora. The work presented in this thesis is made possible by the recently published Finnish dependency treebanks FinnTreeBank and Turku Dependency Treebank. Additionally, the Finnish open-source morphological analyzer OMorFi is extensively utilized in the experiments presented in the thesis. The thesis presents methods for improving tagging accuracy, estimation speed and tagging speed in presence of large structured morphological label sets that are typical for morphologically complex languages. More specifically, it presents a novel formulation of generative morphological taggers using weighted finite-state machines and applies finite-state taggers to context sensitive spelling correction of Finnish. The thesis also explores discriminative morphological tagging. It presents structured sub-label dependencies that can be used for improving tagging accuracy. Additionally, the thesis presents a cascaded variant of the averaged perceptron tagger. In presence of large label sets, a cascaded design results in substantial reduction of estimation speed compared to a standard perceptron tagger. Moreover, the thesis explores pruning strategies for perceptron taggers. Finally, the thesis presents the FinnPos toolkit for morphological tagging. FinnPos is an open-source state-of-the-art averaged perceptron tagger implemented by the author.Disambiguoiva morfologinen jäsennin on ohjelma, joka tuottaa yksikäsitteisiä morfologisia kuvauksia virkkeen sanoille. Tällaisia jäsentimiä voidaan hyödyntää monilla kielenkäsittelyn osa-alueilla, esimerkiksi syntaktisen jäsentimen tai konekäännösjärjestelmän esikäsittelyvaiheena. Kieliteknologisena tehtävänä disambiguoiva morfologinen jäsennys muistuttaa perinteistä sanaluokkajäsennystä, mutta se tuottaa hienojakoisempaa morfologista informaatiota kuin perinteinen sanaluokkajäsennin. Tämän takia disambiguoivia morfologisia jäsentimiä hyödynnetäänkin pääsääntöisesti morfologisesti monimutkaisten kielten, kuten suomen kielen, kieliteknologiassa. Tällaisissa kielissä käytetään paljon sananmuodostuskeinoja kuten taivutusta, johtamista ja yhdyssananmuodostusta. Väitöskirjan esittelemä tutkimus liittyy morfologisesti rikkaiden kielten disambiguoivaan morfologiseen jäsentämiseen koneoppimismenetelmin. Vaikka suomen disambiguoivaa morfologista jäsentämistä on tutkittu aiemmin (esim. Constraint Grammar -formalismin avulla), koneoppimismenetelmiä ei ole aiemmin juurikaan sovellettu. Tämä johtuu siitä että jäsentimen oppimiseen tarvittavia korkealuokkaisia morfologisesti annotoituja korpuksia ei ole ollut avoimesti saatavilla. Tässä väitöskirjassa esitelty tutkimus hyödyntää vastikään julkaistuja suomen kielen dependenssijäsennettyjä FinnTreeBank ja Turku Dependency Treebank korpuksia. Lisäksi tutkimus hyödyntää suomen kielen avointa morfologista OMorFi-jäsennintä. Väitöskirja esittelee menetelmiä jäsennystarkkuuden parantamiseen ja jäsentimen opetusnopeuden sekä jäsennysnopeuden kasvattamiseen. Väitöskirja esittää uuden tavan rakentaa generatiivisia jäsentimiä hyödyntäen painollisia äärellistilaisia koneita ja soveltaa tällaisia jäsentimiä suomen kielen kontekstisensitiiviseen oikeinkirjoituksentarkistukseen. Lisäksi väitöskirja käsittelee diskriminatiivisia jäsennysmalleja. Se esittelee tapoja hyödyntää morfologisten analyysien osia jäsennystarkkuuden parantamiseen. Lisäksi se esittää kaskadimallin, jonka avulla jäsentimen opetusaika lyhenee huomattavasi. Väitöskirja esittää myös tapoja jäsenninmallien pienentämiseen. Lopuksi esitellään FinnPos, joka on kirjoittaman toteuttama avoimen lähdekoodin työkalu disambiguoivien morfologisten jäsentimien opettamiseen

Deep Learning for Automatic Assessment and Feedback of Spoken English

Growing global demand for learning a second language (L2), particularly English, has led to considerable interest in automatic spoken language assessment, whether for use in computerassisted language learning (CALL) tools or for grading candidates for formal qualifications. This thesis presents research conducted into the automatic assessment of spontaneous nonnative English speech, with a view to be able to provide meaningful feedback to learners. One of the challenges in automatic spoken language assessment is giving candidates feedback on particular aspects, or views, of their spoken language proficiency, in addition to the overall holistic score normally provided. Another is detecting pronunciation and other types of errors at the word or utterance level and feeding them back to the learner in a useful way. It is usually difficult to obtain accurate training data with separate scores for different views and, as examiners are often trained to give holistic grades, single-view scores can suffer issues of consistency. Conversely, holistic scores are available for various standard assessment tasks such as Linguaskill. An investigation is thus conducted into whether assessment scores linked to particular views of the speaker’s ability can be obtained from systems trained using only holistic scores. End-to-end neural systems are designed with structures and forms of input tuned to single views, specifically each of pronunciation, rhythm, intonation and text. By training each system on large quantities of candidate data, individual-view information should be possible to extract. The relationships between the predictions of each system are evaluated to examine whether they are, in fact, extracting different information about the speaker. Three methods of combining the systems to predict holistic score are investigated, namely averaging their predictions and concatenating and attending over their intermediate representations. The combined graders are compared to each other and to baseline approaches. The tasks of error detection and error tendency diagnosis become particularly challenging when the speech in question is spontaneous and particularly given the challenges posed by the inconsistency of human annotation of pronunciation errors. An approach to these tasks is presented by distinguishing between lexical errors, wherein the speaker does not know how a particular word is pronounced, and accent errors, wherein the candidate’s speech exhibits consistent patterns of phone substitution, deletion and insertion. Three annotated corpora x of non-native English speech by speakers of multiple L1s are analysed, the consistency of human annotation investigated and a method presented for detecting individual accent and lexical errors and diagnosing accent error tendencies at the speaker level