Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases presenting a super‐human performance. DNNs are the state‐of‐the‐art machine learning algorithms solving many NLP tasks that are relevant for Digital Humanities (DH) research, such as spell checking, language detection, entity extraction, author detection, question answering, and other tasks. These supervised algorithms learn patterns from a large number of “right” and “wrong” examples and apply them to new examples. However, using DNNs for analyzing the text resources in DH research presents two main challenges: (un)availability of training data and a need for domain adaptation. This paper explores these challenges by analyzing multiple use‐cases of DH studies in recent literature and their possible solutions and lays out a practical decision model for DH experts for when and how to choose the appropriate deep learning approaches for their research. Moreover, in this paper, we aim to raise awareness of the benefits of utilizing deep learning models in the DH community.

Text analysis using deep neural networks in digital humanities and information science

We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory. By tapping open online data sources the corpus is meant to offer a more sizable alternative to smaller manually created annotated data sets, while avoiding pitfalls such as imbalanced or unknown composition, licensing problems, and low-quality natural language processing. We harness knowledge from multiple annotation layers in order to achieve a "better than NLP" benchmark and evaluate the accuracy of the resulting resource.

pdf/amalgum-a-free-balanced-multilayer-english-web-corpus-ry80raokpy.pdf

AMALGUM -- A Free, Balanced, Multilayer English Web Corpus

Despite much progress in recent years, the vast majority of work in natural language processing (NLP) is on standard languages with many speakers. In this work, we instead focus on low-resource languages and in particular non-standardized low-resource languages. Even within branches of major language families, often considered well-researched, little is known about the extent and type of available resources and what the major NLP challenges are for these language varieties. The first step to address this situation is a systematic survey of available corpora (most importantly, annotated corpora, which are particularly valuable for NLP research). Focusing on Germanic low-resource language varieties, we provide such a survey in this paper. Except for geolocation (origin of speaker or document), we find that manually annotated linguistic resources are sparse and, if they exist, mostly cover morphosyntax. Despite this lack of resources, we observe that interest in this area is increasing: there is active development and a growing research community. To facilitate research, we make our overview of over 80 corpora publicly available.

A Survey of Corpora for Germanic Low-Resource Languages and Dialects

We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory. By tapping open online data sources the corpus is meant to offer a more sizable alternative to smaller manually created annotated data sets, while avoiding pitfalls such as imbalanced or unknown composition, licensing problems, and low-quality natural language processing. We harness knowledge from multiple annotation layers in order to achieve a “better than NLP” benchmark and evaluate the accuracy of the resulting resource.

AMALGUM – A Free, Balanced, Multilayer English Web Corpus

POS-HOML: POS Tagging Technique For Gujarati Language Using Hybrid Optimal And Machine Learning Approaches

By building a part-of-speech (POS) tagger for Middle High German, we investigate strategies for dealing with a low resource, diverse and non-standard language in the domain of natural language processing. We highlight various aspects such as the data quantity needed for training and the influence of data quality on tagger performance. Since the lack of annotated resources poses a problem for training a tagger, we exemplify how existing resources can be adapted fruitfully to serve as additional training data. The resulting POS model achieves a tagging accuracy of about 91% on a diverse test set representing the different genres, time periods and varieties of MHG. In order to verify its general applicability, we evaluate the performance on different genres, authors and varieties of MHG, separately. We explore self-learning techniques which yield the advantage that unannotated data can be utilized to improve tagging performance on specific subcorpora.

From 0 to 10 million annotated words: part-of-speech tagging for Middle High German

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From 0 to 10 million annotated words: part-of-speech tagging for Middle High German