#publications


Unsupervised pretraining for text classification using siamese transfer learning

posted 5 months, 2 weeks ago in #CLEF, #publications

When training neural networks, huge amounts of training data typically lead to better results. When only a small amount of training data is available, it has been proven useful to initialize a network with pretrained layers. For NLP tasks, networks are usually only given pretrained word embeddings, the rest of the network is not pretrained since pretraining recurrent networks for NLP tasks is difficult. In this article, we present a siamese architecture for pretraining recurrent networks on textual data. The network has to map pairs of sentences onto a vector representation. When a sentence pair is appearing coherently in our corpus, the vector representations should be similar, if not, the representations should be dissimilar. After having pretrained that network, we enhance it and train it on a smaller dataset in order to have it classify textual data. We show that using this kind of approach for pretraining results in better results comparing to doing no pretraining or only using pretrained embeddings when doing text classification for a task with only a small amount of training data. For evaluation, we are using the bots and gender profiling dataset provided by PAN 2019.

PAN | Paper | Poster

Transforming scholarship in the archives through handwritten text recognition: Transkribus as a case study

posted 5 months, 2 weeks ago in #Journal of Documentation, #publications

Purpose An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues.

Design/methodology/approach This paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material.

Findings Transkribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified.

Research limitations/implications The paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc.

Practical implications Only HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field.

Social implications The increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals.

Originality/value This is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector.

https://doi.org/10.1108/JD-07-2018-0114

Evaluation of CNN architectures for text detection in historical maps

posted 9 months, 2 weeks ago in #DATeCH, #publications

We evaluate different densely connected fully convolutional neural network architectures to find and extract text from maps. This is a necessary preprocessing step before OCR can be performed. In order to locate the text, we train a neural network to classify whether a given input is text or not. Our main focus is on the output level, either classifying text or no text for the whole input or predicting the text position pixel wise by outputting a mask. Acquiring enough training data especially for pixel wise prediction is quite a time consuming task, so we investigate a method to generate artificial training data. We compare three training scenarios. First training with images from historical maps, which is quite a small dataset, second adding artificially generated images and third training just with the artificially generated data.

Full Abstract | Poster

Objdetect: Eine Plattform zur Visualisierung von Vorhersagen objekterkennender neuronaler Netze

posted 1 year, 2 months ago in #DHDL, #publications

Im Rahmen unterschiedlicher Projekte und Qualifizierungsarbeiten entstanden und entstehen eine Reihe neuronaler Netze. Diese Netze dienen entweder der Klassifizierung von Bildern oder dem Auffinden von Objekten in Bildern. Um diese neuronalen Netze besser miteinander hinsichtlich unterschiedlicher Architekturen, unterschiedlicher Hyperparameter oder unterschiedlicher Trainingsdaten zu vergleichen und sie einem breiteren Personenkreis verfügbar zu machen, wurde eine Webseite entwickelt, auf der man trainierte neuronale Netze und Bilder hochladen, eine Objekterkennung starten und die Ergebnisse einheitlich visualisieren kann. Die Webseite soll sowohl für Entwickler neuronaler Netze sein, die Ihre Netze vergleichen wollen, als auch für Forscher, die auf ihren Bildern eine Objekterkennung durchführen wollen.

https://fdhl.info/dhdl-2018-materialien/

Generierung von Trainingsdaten für die Handschrifterkennung aus TEI annotierten Dokumenten – Ein Erfahrungsbericht aus dem EU-Projekt READ

posted 1 year, 4 months ago in #INF-DH, #publications

Zum Trainieren maschineller Lernverfahren zur Erkennung von Handschriften werden Textdaten mit korrespondierenden Bildern benötigt. Die Textdaten liegen häufig im TEI-Format das diverse Möglichkeiten eröffnet, um textuelle und semantische Phänomene auszuzeichnen, weiter können gar eigene Tags oder Auszeichnungsarten eingeführt werden. In diesem Beitrag wird ein im EU-Projekt READ entwickeltes parametrisierbares Tool beschrieben, das mit unterschiedlichen Auszeichnungsstilen in TEI umgehen kann und Textdateien auf Seitenbasis liefert, die zur Zuordnung von Text zu Bilddaten (text-to-image) genutzt werden können und somit zur Aufbereitung von Trainingsdaten für Modelle der Handschriftenerkennung dienen. Die gezeigten Beispiele und Anwendungen stammen alle aus Projekten, die ihre Daten für READ zur Verfügung stellten.

https://dl.gi.de/handle/20.500.12116/16992

Masterthesis: Objekterkennung mit Hilfe von Convolutional Neural Networks am Beispiel ägyptischer Hieroglyphen

posted 2 years, 12 months ago in #publications

In this thesis different architectures of Convolutional Neural Networks (CNN) and their suitability for object recognition were investigated by using the example of Egyptian hieroglyphs.

First, basic principles for artificial neural networks and the components of CNNs, such as convolutional layers, are introduced and explained, followed by explanations of the used data sets and the associated difficulties. We then present libraries for the concrete implementation and use of artificial neural networks and describe the sequence of the evaluation of object recognition.

The CNNs were trained and evaluated with different numbers of classes and the associated number of images. The experiments are divided by the three used training methods. For the first, the CNNs were trained with the help of autoencoders. For the second, the CNNs were trained block-wise, and for the third deeper network architectures were investigated. Various network architectures such as Residual Networks (ResNet) and Densely Connected Convolutional Networks, described in the literature, were implemented and evaluated.

The results of the experiments show that it is possible to train CNNs with up to 69 convolutional layers, to classify about 6500 different Egyptian hieroglyphs and finally to carry out an object recognition with very good results. The best results for object detection 0.92362 were achieved for a CNN with 6465 classes and 13 convolutional layers.

PDF | BibLaTex

Bachelorthesis: Multi-Label Klassifikation am Beispiel sozialwissenschaftlicher Texte

posted 3 years ago in #publications

In this thesis different machine learning algorithms are evaluated for the task of multi-label classification. The evaluation is done with the binary classifiers naive Bayes and support vector machine (SVM) and the multi-class classifier supervised latent Dirichlet allocation (SLDA). To enable naive Bayes and SVM to do multi-label classification the RAkEL transformation is used and for SLDA a topic model multi-label learner is developed and used.

The Reuters-21578 corpus is used. Since not all texts have labels and not all labels occur in sufficient frequency a selection of texts was used. Two corpora were created and used for classification.

The classification results show that the best results are archived with SVM. Naive Bayes and SLDA give very similar results, but SLDA has a very long runtime.

PDF | BibLaTex