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We report on a new project building a Natural Language Processing resource for Zulu by making use of resources already available. Combining tagging results with the results of morphological analysis semi-automatically, we expect to reduce the amount of manual work when generating a finely-grained gold standard corpus usable for training a tagger. From the tagged corpus, we plan to extract verb-argument pairs with the aim of compiling a verb valency lexicon for Zulu.
Der vorliegende Beitrag setzt sich mit dem computergestützten Transkriptionsverfahren arabisch-deutscher Gesprächsdaten für interaktionsbezogene Untersuchungen auseinander. Zunächst werden wesentliche methodische Herausforderungen der gesprächsanalytischen Arbeit adressiert: Hinsichtlich der derzeitigen Korpustechnologie ermöglicht die Verwendung von arabischen Schriftzeichen in einem mehrsprachigen, bidirektionalen Transkript keine analysegerechte Rekonstruktion von Reziprozität, Linearität und Simultaneität sprachlichen Handelns. Zudem ist die Verschriftung von arabischen Gesprächsdaten aufgrund der unzureichenden (gesprächsanalytischen) Beschäftigung mit den standardfernen Varietäten und gesprochensprachlichen Phänomenen erschwert. Daher widmet sich der zweite Teil des Beitrags den bisher erarbeiteten und erprobten Lösungsansätzen ̶ einem stringenten, gesprächsanalytisch fundierten Transkriptionssystem für gesprochenes Arabisch.
The paper deals with the process of computer-aided transcription regarding Arabic-German data material for interaction-based studies. First of all, it sheds light upon some major methodological challenges posed by the conversation-analytic approaches: due to current corpus technology, the reciprocity, linearity, and simultaneity of linguistic activities cannot be reconstructed in an analytically proper way when using the Arabic characters in multilingual and bidirectional transcripts. The difficulty of transcribing Arabic encounters is also compounded by the fact that Spoken Arabic as well as its varieties and phenomena have not been standardised enough (for conversation-analytic purposes). Therefore, the second part of this paper is dedicated to preliminary, self-developed solutions, namely a systematic method for transcribing Spoken Arabic.
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
In this paper, we describe a data processing pipeline used for annotated spoken corpora of Uralic languages created in the INEL (Indigenous Northern Eurasian Languages) project. With this processing pipeline we convert the data into a loss-less standard format (ISO/TEI) for long-term preservation while simultaneously enabling a powerful search in this version of the data. For each corpus, the input we are working with is a set of files in EXMARaLDA XML format, which contain transcriptions, multimedia alignment, morpheme segmentation and other kinds of annotation. The first step of processing is the conversion of the data into a certain subset of TEI following the ISO standard ’Transcription of spoken language’ with the help of an XSL transformation. The primary purpose of this step is to obtain a representation of our data in a standard format, which will ensure its long-term accessibility. The second step is the conversion of the ISO/TEI files to a JSON format used by the “Tsakorpus” search platform. This step allows us to make the corpora available through a web-based search interface. As an addition, the existence of such a converter allows other spoken corpora with ISO/TEI annotation to be made accessible online in the future.
In an earlier publication it was claimed that there is no useful relationship between Swahili-English dictionary look-up frequencies and the occurrence frequencies for the same wordforms in Swahili-English corpora, at least not beyond the top few thousand wordforms. This result was challenged using data for German by a different team of researchers using an improved methodology. In the present article the original Swahili-English data is revisited, using ten years’ worth of it rather than just two, and using the improved methodology. We conclude that there is indeed a positive relationship. In addition, we show that online dictionary look-up behaviour is remarkably similar across languages, even when, as in our case, one is dealing with languages from very dissimilar language families. Furthermore, online dictionaries turn out to have minimum look-up success rates, below which they simply cannot go. These minima are language-sensitive and vary depending on the regularity of the searched-for entries, but are otherwise constant no matter the size of randomly sampled dictionaries. Corpus-informed sampling always improves on any random method. Lastly, from the point of view of the graphical user interface, we argue that the average user of an online bilingual dictionary is better served with a single search box, rather than separate search boxes for each dictionary side.
Das Archiv für Gesprochenes Deutsch (AGD, Stift/Schmidt 2014) am Leibniz-Institut für Deutsche Sprache ist ein Forschungsdatenzentrum für Korpora des gesprochenen Deutsch. Gegründet als Deutsches Spracharchiv (DSAv) im Jahre 1932 hat es über Eigenprojekte, Kooperationen und Übernahmen von Daten aus abgeschlossenen Forschungsprojekten einen Bestand von bald 100 Variations-, Interview- und Gesprächskorpora aufgebaut, die u. a. dialektalen Sprachgebrauch, mündliche Kommunikationsformen oder die Sprachverwendung bestimmter Sprechertypen oder zu bestimmten Themen dokumentieren. Heute ist dieser Bestand fast vollständig digitalisiert und wird zu einem großen Teil der wissenschaftlichen Gemeinschaft über die Datenbank für Gesprochenes Deutsch (DGD) im Internet zur Nutzung in Forschung und Lehre angeboten.