The following paper describes the first steps in the development of an ontology for the textbook research discipline. The aim of the project WorldViews is to establish a digital edition focussing on views of the world depicted in textbooks. For this purpose an initial TEI profile has been formalised and tested as a use case to enable the semantical encoding of the resource ‘textbook’. This profile shall provide a basic data model describing major facets of the textbook’s structure relevant to historians.
This article describes work on enabling the addition of temporal information to senses of words in linguistic linked open data lexica based on the lemonDia model. Our contribution in this article is twofold. On the one hand, we demonstrate how lemonDia enables the querying of diachronic lexical datasets using OWL-oriented Semantic Web based technologies. On the other hand, we present a preliminary version of an interactive interface intended to help users in creating lexical datasets that model meaning change over time.
This paper gives an overview of the MultiTal project, which aims to create a research infrastructure that ensures long-term distribution of NLP tools descriptions. The goal is to make NLP tools more accessible and usable to end-users of different disciplines. The infrastructure is built on a meta-data scheme modelling and standardising multilingual NLP tools documentation. The model is conceptualised using an OWL ontology. The formal representation of the ontology allows us to automatically generate organised and structured documentation in different languages for each represented tool.
Although spanning thousands of years and genres as diverse as liturgy, historiography, lyric and other forms of prose and poetry, the body of Latin texts is still relatively sparse compared to English. Data sparsity in Latin presents a number of challenges for traditional Named Entity Recognition techniques. Solving such challenges and enabling reliable Named Entity Recognition in Latin texts can facilitate many down-stream applications, from machine translation to digital historiography, enabling Classicists, historians, and archaeologists for instance, to track the relationships of historical persons, places, and groups on a large scale. This paper presents the first annotated corpus for evaluating Named Entity Recognition in Latin, as well as a fully supervised model that achieves over 90% F-score on a held-out test set, significantly outperforming a competitive baseline. We also present a novel active learning strategy that predicts how many and which sentences need to be annotated for named entities in order to attain a specified degree of accuracy when recognizing named entities automatically in a given text. This maximizes the productivity of annotators while simultaneously controlling quality.
The availability of Language Technology Resources and Tools generates a considerable methodological potential in the Digital Humanities: aspects of research questions from the Humanities and Social Sciences can be addressed on text collections in ways that were unavailable to traditional approaches. I start this talk by sketching some sample scenarios of Digital Humanities projects which involve various Humanities and Social Science disciplines, noting that the potential for a meaningful contribution to higher-level questions is highest when the employed language technological models are carefully tailored both (a) to characteristics of the given target corpus, and (b) to relevant analytical subtasks feeding the discipline-specific research questions. Keeping up a multidisciplinary perspective, I then point out a recurrent dilemma in Digital Humanities projects that follow the conventional set-up of collaboration: to build high-quality computational models for the data, fixed analytical targets should be specified as early as possible – but to be able to respond to Humanities questions as they evolve over the course of analysis, the analytical machinery should be kept maximally flexible. To reach both, I argue for a novel collaborative culture that rests on a more interleaved, continuous dialogue. (Re-)Specification of analytical targets should be an ongoing process in which the Humanities Scholars and Social Scientists play a role that is as important as the Computational Scientists’ role. A promising approach lies in the identification of re-occurring types of analytical subtasks, beyond linguistic standard tasks, which can form building blocks for text analysis across disciplines, and for which corpus-based characterizations (viz. annotations) can be collected, compared and revised. On such grounds, computational modeling is more directly tied to the evolving research questions, and hence the seemingly opposing needs of reliable target specifications vs. “malleable” frameworks of analysis can be reconciled. Experimental work following this approach is under way in the Center for Reflected Text Analytics (CRETA) in Stuttgart.
Multimodal question answering in the cultural heritage domain allows visitors to ask questions in a more natural way and thus provides better user experiences with cultural objects while visiting a museum, landmark or any other historical site. In this paper, we introduce the construction of a golden standard dataset that will aid research of multimodal question answering in the cultural heritage domain. The dataset, which will be soon released to the public, contains multimodal content including images of typical artworks from the fascinating old-Egyptian Amarna period, related image-containing documents of the artworks and over 800 multimodal queries integrating visual and textual questions. The multimodal questions and related documents are all in English. The multimodal questions are linked to relevant paragraphs in the related documents that contain the answer to the multimodal query.
In this paper we present a new combination of existing language tools for Polish with a popular data mining platform intended to help researchers from digital humanities perform computational analyses without any programming. The toolset includes RapidMiner Studio, a software solution offering graphical setup of integrated analytical processes and Multiservice, a Web service offering access to several state-of-the-art linguistic tools for Polish. The setting is verified in a simple task of counting frequencies of unknown words in a small corpus.
We present ANNISVis, a webapp for comparative visualization of geographical distribution of linguistic data, as well as a sample deployment for a corpus of Middle High German texts. Unlike existing geographical visualization solutions, which work with pre-existing data sets, or are bound to specific corpora, ANNISVis allows the user to formulate multiple ad-hoc queries and visualizes them on a map, and it can be configured for any corpus that can be imported into ANNIS. This enables explorative queries of the quantitative aspects of a corpus with geographical features. The tool will be made available to download in open source.
The increasing amount of multilingual text collections available in different domains makes its automatic processing essential for the development of a given field. However, standard processing techniques based on statistical clues and keyword searches have clear limitations. Instead, we propose a knowledge-based processing pipeline which overcomes most of the limitations of these techniques. This, in turn, enables direct comparison across texts in different languages without the need of translation. In this paper we show the potential of this approach for semantically indexing multilingual text collections in the history domain. In our experiments we used a version of the Bible translated in four different languages, evaluating the precision of our semantic indexing pipeline and showing its reliability on the cross-lingual text retrieval task.
Historical treebanks tend to be manually annotated, which is not surprising, since state-of-the-art parsers are not accurate enough to ensure high-quality annotation for historical texts. We test whether automatic parsing can be an efficient pre-annotation tool for Old East Slavic texts. We use the TOROT treebank from the PROIEL treebank family. We convert the PROIEL format to the CONLL format and use MaltParser to create syntactic pre-annotation. Using the most conservative evaluation method, which takes into account PROIEL-specific features, MaltParser by itself yields 0.845 unlabelled attachment score, 0.779 labelled attachment score and 0.741 secondary dependency accuracy (note, though, that the test set comes from a relatively simple genre and contains rather short sentences). Experiments with human annotators show that preparsing, if limited to sentences where no changes to word or sentence boundaries are required, increases their annotation rate. For experienced annotators, the speed gain varies from 5.80% to 16.57%, for inexperienced annotators from 14.61% to 32.17% (using conservative estimates). There are no strong reliable differences in the annotation accuracy, which means that there is no reason to suspect that using preparsing might lower the final annotation quality.
In this paper we describe how the complexity of human communication can be analysed with the help of language technology. We present the HuComTech corpus, a multimodal corpus containing 50 hours of videotaped interviews containing a rich annotation of about 2 million items annotated on 33 levels. The corpus serves as a general resource for a wide range of re-search addressing natural conversation between humans in their full complexity. It can benefit particularly digital humanities researchers working in the field of pragmatics, conversational analysis and discourse analysis. We will present a number of tools and automated methods that can help such enquiries. In particular, we will highlight the tool Theme, which is designed to uncover hidden temporal patterns (called T-patterns) in human interaction, and will show how it can applied to the study of multimodal communication.
We introduce the third major release of WebAnno, a generic web-based annotation tool for distributed teams. New features in this release focus on semantic annotation tasks (e.g. semantic role labelling or event annotation) and allow the tight integration of semantic annotations with syntactic annotations. In particular, we introduce the concept of slot features, a novel constraint mechanism that allows modelling the interaction between semantic and syntactic annotations, as well as a new annotation user interface. The new features were developed and used in an annotation project for semantic roles on German texts. The paper briefly introduces this project and reports on experiences performing annotations with the new tool. On a comparative evaluation, our tool reaches significant speedups over WebAnno 2 for a semantic annotation task.
We are constructing an annotated diachronic corpora of the Japanese language. In part of thiswork, we construct a corpus of Manyosyu, which is an old Japanese poetry anthology. In thispaper, we describe how to align the transcribed text and its original text semiautomatically to beable to cross-reference them in our Manyosyu corpus. Although we align the original charactersto the transcribed words manually, we preliminarily align the transcribed and original charactersby using an unsupervised automatic alignment technique of statistical machine translation toalleviate the work. We found that automatic alignment achieves an F1-measure of 0.83; thus, each poem has 1–2 alignment errors. However, finding these errors and modifying them are less workintensiveand more efficient than fully manual annotation. The alignment probabilities can beutilized in this modification. Moreover, we found that we can locate the uncertain transcriptionsin our corpus and compare them to other transcriptions, by using the alignment probabilities.
We present an approach to detect differences in lexical semantics across English language registers, using word embedding models from distributional semantics paradigm. Models trained on register-specific subcorpora of the BNC corpus are employed to compare lists of nearest associates for particular words and draw conclusions about their semantic shifts depending on register in which they are used. The models are evaluated on the task of register classification with the help of the deep inverse regression approach. Additionally, we present a demo web service featuring most of the described models and allowing to explore word meanings in different English registers and to detect register affiliation for arbitrary texts. The code for the service can be easily adapted to any set of underlying models.
This paper presents a tool to investigate the design of multimodal instructions (MIs), i.e., instructions that contain both text and pictures. The benefit of including pictures in information presentation has been established, but the characteristics of those pictures and of their textual counterparts and the relation(s) between them have not been researched in a systematic manner. We present the PAT Workbench, a tool to store, annotate and retrieve MIs based on a validated coding scheme with currently 42 categories that describe instructions in terms of textual features, pictorial elements, and relations between text and pictures. We describe how the PAT Workbench facilitates collaborative annotation and inter-annotator agreement calculation. Future work on the tool includes expanding its functionality and usability by (i) making the MI annotation scheme dynamic for adding relevant features based on empirical evaluations of the MIs, (ii) implementing algorithms for automatic tagging of MI features, and (iii) implementing automatic MI evaluation algorithms based on results obtained via e.g. crowdsourced assessments of MIs.
Machine Translation (MT) plays a critical role in expanding capacity in the translation industry. However, many valuable documents, including digital documents, are encoded in non-accessible formats for machine processing (e.g., Historical or Legal documents). Such documents must be passed through a process of Optical Character Recognition (OCR) to render the text suitable for MT. No matter how good the OCR is, this process introduces recognition errors, which often renders MT ineffective. In this paper, we propose a new OCR to MT framework based on adding a new OCR error correction module to enhance the overall quality of translation. Experimentation shows that our new system correction based on the combination of Language Modeling and Translation methods outperforms the baseline system by nearly 30% relative improvement.
We examine two different methods for finding rising words (among which neologisms) and falling words (among which archaisms) in decades of magazine texts (millions of words) and in years of tweets (billions of words): one based on correlation coefficients of relative frequencies and time, and one based on comparing initial and final word frequencies of time intervals. We find that smoothing frequency scores improves the precision scores of both methods and that the correlation coefficients perform better on magazine text but worse on tweets. Since the two ranking methods find different words they can be used in side-by-side to study the behavior of words over time.
We here describe a novel methodology for measuring affective language in historical text by expanding an affective lexicon and jointly adapting it to prior language stages. We automatically construct a lexicon for word-emotion association of 18th and 19th century German which is then validated against expert ratings. Subsequently, this resource is used to identify distinct emotional patterns and trace long-term emotional trends in different genres of writing spanning several centuries.
In the Danish CLARIN-DK infrastructure, chaining language technology (LT) tools into a workflow is easy even for a non-expert user, because she only needs to specify the input and the desired output of the workflow. With this information and the registered input and output profiles of the available tools, the CLARIN-DK workflow management system (WMS) computes combinations of tools that will give the desired result. This advanced functionality was originally not envisaged, but came within reach by writing the WMS partly in Java and partly in a programming language for symbolic computation, Bracmat. Handling LT tool profiles, including the computation of workflows, is easier with Bracmat’s language constructs for tree pattern matching and tree construction than with the language constructs offered by mainstream programming languages.
