Computerlinguistik
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(2019)
Content
1 Predicting learner knowledge of individual words using machine learning
Drilon Avdiu, Vanessa Bui, Klára Ptacinová Klimci´ková
2 Automatic Generation and Semantic Grading of Esperanto Sentences in a Teaching Context
Eckhard Bick
3 Toward automatic improvement of language produced by non-native language learners
Mathias Creutz, Eetu Sjöblom
4 Linguistic features and proficiency classification in L2 Spanish and L2 Portuguese
Iria del Ri´o
5 Integrating large-scale web data and curated corpus data in a search engine supporting German literacy education
Sabrina Dittrich, Zarah Weiss, Hannes Schröter, Detmar Meurers
6 Formalism for a language agnostic language learning game and productive grid generation
Sylvain Hatier, Arnaud Bey, Mathieu Loiseau
7 Understanding Vocabulary Growth Through An Adaptive Language Learning System
Elma Kerz, Andreas Burgdorf, Daniel Wiechmann, Stefan Meeger,Yu Qiao, Christian Kohlschein, Tobias Meisen
8 Summarization Evaluation meets Short-Answer Grading
Margot Mieskes, Ulrike Padó
9 Experiments on Non-native Speech Assessment and its Consistency
Ziwei Zhou, Sowmya Vajjala, Seyed Vahid Mirnezami
10 The Impact of Spelling Correction and Task Context on Short Answer Assessment for Intelligent Tutoring Systems
Ramon Ziai, Florian Nuxoll, Kordula De Kuthy, Björn Rudzewitz, Detmar Meurers
The demo presents a minimalist, off-the-shelf AND tool which provides a fundamental AND operation, the comparison of two publications with ambiguous authors, as an easily accessible HTTP interface. The tool implements this operation using standard AND functionality, but puts particular emphasis on advanced methods from natural language processing (NLP) for comparing publication title semantics.
The use of digital resources and tools across humanities disciplines is steadily increasing, giving rise to new research paradigms and associated methods that are commonly subsumed under the term digital humanities. Digital humanities does not constitute a new discipline in itself, but rather a new approach to humanities research that cuts across different existing humanities disciplines. While digital humanities extends well beyond language-based research, textual resources and spoken language materials play a central role in most humanities disciplines.
The transfer of research data management from one institution to another infrastructural partner is all but trivial, but can be required, for instance, when an institution faces reorganization or closure. In a case study, we describe the migration of all research data, identify the challenges we encountered, and discuss how we addressed them. It shows that the moving of research data management to another institution is a feasible, but potentially costly enterprise. Being able to demonstrate the feasibility of research data migration supports the stance of data archives that users can expect high levels of trust and reliability when it comes to data safety and sustainability.
In this chapter, we discuss steps toward extending CMDI’s semantic interoperability beyond the Social Sciences and Humanities: We stress the need for an initial data curation step, in part supported by a relation registry that helps impose some structure on CMDI vocabulary; we describe the use of authority file information and other controlled vocabulary to help connecting CMDI-based metadata to existing Linked Data; we show how significant parts of CMDI-based metadata can be converted to bibliographic metadata standards and hence entered into library catalogs; and finally we describe first steps to convert CMDI-based metadata to RDF. The initial grassroots approach of CMDI (meaning that anybody can define metadata descriptors and components) mirrors the AAA slogan of the Semantic Web (“Anyone can say Anything about Any topic”). Ironically, this makes it hard to fully link CMDI-based metadata to other Semantic Web datasets. This paper discusses the challenges of this enterprise.
We propose a Cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with Semantic Role Labeling annotations in a resource-poor target language. Unlike annotation projection techniques, our model does not need parallel data during inference time. Our approach can be applied in monolingual, multilingual and cross-lingual settings and is able to produce dependencybased and span-based SRL annotations. We benchmark the labeling performance of our model in different monolingual and multilingual settings using well-known SRL datasets. We then train our model in a cross-lingual setting to generate new SRL labeled data. Finally, we measure the effectiveness of our method by using the generated data to augment the training basis for resource-poor languages and perform manual evaluation to show that it produces high-quality sentences and assigns accurate semantic role annotations. Our proposed architecture offers a flexible method for leveraging SRL data in multiple languages.