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Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.
Text corpora come in many different shapes and sizes and carry heterogeneous annotations, depending on their purpose and design. The true benefit of corpora is rooted in their annotation and the method by which this data is encoded is an important factor in their interoperability. We have accumulated a large collection of multilingual and parallel corpora and encoded it in a unified format which is compatible with a broad range of NLP tools and corpus linguistic applications. In this paper, we present our corpus collection and describe a data model and the extensions to the popular CoNLL-U format that enable us to encode it.
As the Web ought to be considered as a series of sources rather than as a source in itself, a problem facing corpus construction resides in meta-information and categorization. In addition, we need focused data to shed light on particular subfields of the digital public sphere. Blogs are relevant to that end, especially if the resulting web texts can be extracted along with metadata and made available in coherent and clearly describable collections.
Nearly all of the very large corpora of English are “static”, which allows a wide range of one-time, pre-processed data, such as collocates. The challenge comes with large “dynamic” corpora, which are updated regularly, and where preprocessing is much more difficult. This paper provides an overview of the NOW corpus (News on the Web), which is currently 8.2 billion words in size, and which grows by about 170 million words each month. We discuss the architecture of NOW, and provide many examples that show how data from NOW can (uniquely) be extracted to look at a wide range of ongoing changes in English.
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.
Automatic division of spoken language transcripts into sentence-like units is a challenging problem, caused by disfluencies, ungrammatical structures and the lack of punctuation. We present experiments on dividing up German spoken dialogues where we investigate the impact of task setup and data representation, encoding of context information as well as different model architectures for this task.
We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably observed on datasets that are created by focused sampling instead of random sampling. Datasets with a higher proportion of implicit abuse are more affected than datasets with a lower proportion.
We examine the new task of detecting derogatory compounds (e.g. curry muncher). Derogatory compounds are much more difficult to detect than derogatory unigrams (e.g. idiot) since they are more sparsely represented in lexical resources previously found effective for this task (e.g. Wiktionary). We propose an unsupervised classification approach that incorporates linguistic properties of compounds. It mostly depends on a simple distributional representation. We compare our approach against previously established methods proposed for extracting derogatory unigrams.
This paper reports on the latest developments of the European Reference Corpus EuReCo and the German Reference Corpus in relation to three of the most important CMLC topics: interoperability, collaboration on corpus infrastructure building, and legal issues. Concerning interoperability, we present new ways to access DeReKo via KorAP on the API and on the plugin level. In addition we report about advancements in the EuReCo- and ICC-initiatives with the provision of comparable corpora, and about recent problems with license acquisitions and our solution approaches using an indemnification clause and model licenses that include scientific exploitation.
Distributional models of word use constitute an indispensable tool in corpus based lexicological research for discovering paradigmatic relations and syntagmatic patterns (Belica et al. 2010). Recently, word embeddings (Mikolov et al. 2013) have revived the field by allowing to construct and analyze distributional models on very large corpora. This is accomplished by reducing the very high dimensionality of word cooccurrence contexts, the size of the vocabulary, to few dimensions, such as 100-200. However, word use and meaning can vary widely along dimensions such as domain, register, and time, and word embeddings tend to represent only the most prevalent meaning. In this paper we thus construct domain specific word embeddings to allow for systematically analyzing variations in word use. Moreover, we also demonstrate how to reconstruct domain specific co-occurrence contexts from the dense word embeddings.
Since 2013 representatives of several French and German CMC corpus projects have developed three customizations of the TEI-P5 standard for text encoding in order to adapt the encoding schema and models provided by the TEI to the structural peculiarities of CMC discourse. Based on the three schema versions, a 4th version has been created which takes into account the experiences from encoding our corpora and which is specifically designed for the submission of a feature request to the TEI council. On our poster we would present the structure of this schema and its relations (commonalities and differences) to the previous schemas.
This paper presents the prototype of a lexicographic resource for spoken German in interaction, which was conceived within the framework of the LeGeDe-project (LeGeDe=Lexik des gesprochenen Deutsch). First of all, it summarizes the theoretical and methodological approaches that were used for the initial planning of the resource. The headword candidates were selected by analyzing corpus-based data. Therefore, the data of two corpora (written and spoken German) were compared with quantitative methods. The information that was gathered on the selected headword candidates can be assigned to two different sections: meanings and functions in interaction.
Additionally, two studies on the expectations of future users towards the resource were carried out. The results of these two studies were also taken into account in the development of the prototype. Focusing on the presentation of the resource’s content, the paper shows both the different lexicographical information in selected dictionary entries, and the information offered by the provided hyperlinks and external texts. As a conclusion, it summarizes the most important innovative aspects that were specifically developed for the implementation of such a resource.
In this paper, we present WebAnno-MM, an extension of the popular web-based annotation tool WebAnno, which is designed for the linguistic annotation of transcribed spoken data with time aligned media files. Several new features have been implemented for our current use case: a novel teaching method based on pair-wise manual annotation of transcribed video data and systematic comparison of agreement between students. To enable the annotation of transcribed spoken language data, apart from technical and data model related challenges, WebAnno-MM offers an additional view to data: a (musical) score view for the inspection of parallel utterances, which is relevant for various methodological research questions regarding the analysis of interactions of spoken content.
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.
The Lehnwortportal Deutsch (2012 seqq.) serves as an integrated online information system on German lexical borrowings into other languages, synthesizing an increasing number of lexicographical dictionaries and providing basic cross-resource search options. The paper discusses the far-reaching revision of the system’s conceptual, lexicographical and technological underpinnings currently under way, focussing on their relevance for multilingual loanword lexicography.
A "polyglottal" speech synthesis - modifications for a replica of Kempelen's speaking machine
(2019)
Smooth turn-taking in conversation depends in part on speakers being able to communicate their intention to hold or cede the floor. Both prosodic and gestural cues have been shown to be used in this context. We investigate the interplay of pitch movements and hand gestures at locations at which speaker change becomes relevant, comparing their use in German and Swedish. We find that there are some shared functions of prosody and gesture with regard to turn-taking in the two languages, but that these shared functions appear to be mediated by the different phonological demands on pitch in the two languages.
In diesem Beitrag wird das Redewiedergabe-Korpus (RW-Korpus) vorgestellt, ein historisches Korpus fiktionaler und nicht-fiktionaler Texte, das eine detaillierte manuelle Annotation mit Redewiedergabeformen enthält. Das Korpus entsteht im Rahmen eines laufenden DFG-Projekts und ist noch nicht endgültig abgeschlossen, jedoch ist für Frühjahr 2019 ein Beta-Release geplant, welches der Forschungsgemeinschaft zur Verfügung gestellt wird. Das endgültige Release soll im Frühjahr 2020 erfolgen. Das RW-Korpus stellt eine neuartige Ressource für die Redewiedergabe-Forschung dar, die in dieser Detailliertheit für das Deutsche bisher nicht verfügbar ist, und kann sowohl für quantitative linguistische und literaturwissenschaftliche Untersuchungen als auch als Trainingsmaterial für maschinelles Lernen dienen.
This paper describes a rule-based approach to detect direct speech without the help of any quotation markers. As datasets fictional and non-fictional texts were used. Our evaluation shows that the results appear stable throughout different datasets in the fictional domain and are comparable to the results achieved in related work.
Graphenbasierte Ansätze spielen in der digitalen Lexikografie eine immer stärkere Rolle. Essentiell für die Erstellung, Verwaltung und Nutzung graphenbasierter lexikografischer Ressourcen ist jedoch eine leistungsfähige und zugleich einfach zu handhabende Zugriffsstruktur, die Suchen nach komplexen Konstellationen in solchen Graphen ermöglicht. Für heutige Graphendatenbanken stehen zahlreiche Abfragesprachen zur Verfügung, deren Verwendung jedoch verhältnismäßig voraussetzungsreich ist.
Das Poster stellt einen webbasierten, frei konfigurierbaren Query Builder vor, der die Formulierung semantisch sehr komplexer Suchabfragen an eine (mit dem Tinkerpop-Standard kompatible) Property-Graphendatenbank ermöglicht. Die Abfrage erfolgt durch einfaches visuell-interaktives Zusammenstellen hierarchisch angeordneter Abfrageelemente und liefert Antworten in Echtzeit. Dabei wird von den Komplexitäten der verwendeten Low-level-Abfragesprache Gremlin abstrahiert. Der Query Builder ist ein zentrales Modul eines derzeit entwickelten Open-Source-Softwaresystems zur Verwaltung und Online-Publikation graph-erweiterter lexikografischer Ressourcen.
A Supervised learning approach for the extraction of opinion sources and targets from German text
(2019)
We present the first systematic supervised learning approach for the extraction of opinion sources and targets on German language data. A wide choice of different features is presented, particularly syntactic features and generalization features. We point out specific differences between opinion sources and targets. Moreover, we explain why implicit sources can be extracted even with fairly generic features. In order to ensure comparability our classifier is trained and tested on the dataset of the STEPS shared task.
We present the second edition of the GermEval Shared Task on the Identification of Offensive Language. This shared task deals with the classification of German tweets from Twitter. Two subtasks were continued from the first edition, namely a coarse-grained binary classification task and a fine-grained multi-class classification task. As a novel subtask, we introduce the classification of offensive tweets as explicit or implicit.
The shared task had 13 participating groups submitting 28 runs for the coarse-grained
task, another 28 runs for the fine-grained task, and 17 runs for the implicit-explicit
task.
We evaluate the results of the systems submitted to the shared task. The shared task homepage can be found at https://projects.fzai.h-da.de/iggsa/
Der Beitrag beschreibt ein mehrfach annotiertes Korpus deutschsprachiger Songtexte als Datenbasis für interdisziplinäre Untersuchungsszenarien. Die Ressource erlaubt empirisch begründete Analysen sprachlicher Phänomene, systemischstruktureller Wechselbeziehungen und Tendenzen in den Texten moderner Popmusik. Vorgestellt werden Design und Annotationen des in thematische und autorenspezifische Archive stratifizierten Korpus sowie deskriptive Statistiken am Beispiel des Udo-Lindenberg-Archivs.
We present a descriptive analysis on the two datasets from the shared task on Source, Subjective Expression and Target Extraction from Political Speeches (STEPS), the only existing German dataset for opinion role extraction of its size. Our analysis discusses the individual properties of the three components, subjective expressions, sources and targets and their relations towards each other. Our observations should help practitioners and researchers when building a system to extract opinion roles from German data.
In this paper, we present our work-inprogress to automatically identify free indirect representation (FI), a type of thought representation used in literary texts. With a deep learning approach using contextual string embeddings, we achieve f1 scores between 0.45 and 0.5 (sentence-based evaluation for the FI category) on two very different German corpora, a clear improvement on earlier attempts for this task. We show how consistently marked direct speech can help in this task. In our evaluation, we also consider human inter-annotator scores and thus address measures of certainty for this difficult phenomenon.
German subjectively veridical sicher sein ‘be certain’ can embed ob-clauses in negative contexts, while subjectively veridical glauben ‘believe’ and nonveridical möglich sein ‘be possible’ cannot. The Logical Form of F isn’t certain if M is in Rome is regarded as the negated disjunction of two sentences ¬(cf σ ∨ cf ¬σ) or ¬cf σ ∧ ¬cf ¬σ. Be certain can have this LF because ¬cf σ and ¬cf ¬σ are compatible and nonveridical. Believe excludes this LF because ¬bf σ and ¬bf ¬σ are incompatible in a question-under-discussion context. It follows from this incompatibility and from the incompatibility of bf σ and bf ¬σ that bf ¬σ and ¬bf σ are equivalent. Therefore believe cannot be nonveridical. Be possible doesn’t allow the LF either. Similar to believe, ¬pf σ and ¬pf ¬σ are incompatible. But unlike believe, pf σ and pf ¬σ are compatible.
We investigate whether prototypicality or prominence of semantic roles can account for role-related effects in sentence interpretation. We present two acceptability-rating experiments testing three different constructions: active, personal passive and DO-clefts involving the same type of transitive verbs that differ with respect to the agentive role features they select. Our results reveal that there is no cross-constructional advantage for prototypical roles (e.g., agents), hence disconfirming a central tenet of role prototypicality. Rather, acceptability clines depend on the construction under investigation, thereby highlighting different role features. This finding is in line with one core assumption of the prominence account stating that role features are flexibly highlighted depending on the discourse function of the respective construction.
Preface
(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.
Статтю присвячено дослідженню комунікативних невдач у мовленнєвому жанрі відеоінтерв’ю крізь призму української національної ідентичності. Визначено тематику, типи і жанрово-мовну специфіку українського відеоінтерв’ю як зразка діалогічного мовлення. Встановлено специфіку комунікативних невдач у цьому жанрі (зі спортсменами, політиками і культурними діячами) з огляду на позиції комунікантів, структурні рівні досліджуваного жанру та максими спілкування.
We present web services implementing a workflow for transcripts of spoken language following TEI guidelines, in particular ISO 24624:2016 "Language resource management - Transcription of spoken language". The web services are available at our website and will be available via the CLARIN infrastructure, including the Virtual Language Observatory and WebLicht.