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Ph@ttSessionz and Deutsch heute are two large German speech databases. They were created for different purposes: Ph@ttSessionz to test Internet-based recordings and to adapt speech recognizers to the voices of adolescent speakers, Deutsch heute to document regional variation of German. The databases differ in their recording technique, the selection of recording locations and speakers, elicitation mode, and data processing.
In this paper, we outline how the recordings were performed, how the data was processed and annotated, and how the two databases were imported into a single relational database system. We present acoustical measurements on the digit items of both databases. Our results confirm that the elicitation technique affects the speech produced, that f0 is quite comparable despite different recording procedures, and that large speech technology databases with suitable metadata may well be used for the analysis of regional variation of speech.
A syntax-based scheme for the annotation and segmentation of German spoken language interactions
(2018)
Unlike corpora of written language where segmentation can mainly be derived from orthographic punctuation marks, the basis for segmenting spoken language corpora is not predetermined by the primary data, but rather has to be established by the corpus compilers. This impedes consistent querying and visualization of such data. Several ways of segmenting have been proposed,
some of which are based on syntax. In this study, we developed and evaluated annotation and segmentation guidelines in reference to the topological field model for German. We can show that these guidelines are used consistently across annotators. We also investigated the influence of various interactional settings with a rather simple measure, the word-count per segment and unit-type. We observed that the word count and the distribution of each unit type differ in varying interactional settings and that our developed segmentation and annotation guidelines are used consistently across annotators. In conclusion, our syntax-based segmentations reflect interactional properties that are intrinsic to the social interactions that participants are involved in. This can be used for further analysis of social interaction and opens the possibility for automatic segmentation of transcripts.
The workshop presents ATHEN 1 (Annotation and Text Highlighting Environment), an extensible desktop-based annotation environment which supports more than just regular annotation. Besides being a general purpose annotation environment, ATHEN supports indexing and querying support of your data as well as the ability to automatically preprocess your data with Meta information. It is especially suited for those who want to extend existing general purpose annotation tools by implementing their own custom features, which cannot be fulfilled by other available annotation environments. On the according gitlab, we provide online tutorials, which demonstrate the use of specific features of ATHEN
Feedback utterances are among the most frequent in dialogue. Feedback is also a crucial aspect of all linguistic theories that take social interaction involving language into account. However, determining communicative functions is a notoriously difficult task both for human interpreters and systems. It involves an interpretative process that integrates various sources of information. Existing work on communicative function classification comes from either dialogue act tagging where it is generally coarse grained concerning the feed- back phenomena or it is token-based and does not address the variety of forms that feed- back utterances can take. This paper introduces an annotation framework, the dataset and the related annotation campaign (involving 7 raters to annotate nearly 6000 utterances). We present its evaluation not merely in terms of inter-rater agreement but also in terms of usability of the resulting reference dataset both from a linguistic research perspective and from a more applicative viewpoint.
So far, Sepedi negations have been considered more from the point of view of lexicographical treatment. Theoretical works on Sepedi have been used for this purpose, setting as an objective a neat description of these negations in a (paper) dictionary. This paper is from a different perspective: instead of theoretical works, corpus linguistic methods are used: (1) a Sepedi corpus is examined on the basis of existing descriptions of the occurrences of a relevant verb, looking at its negated forms from a purely prescriptive point of view; (2) a "corpus-driven" strategy is employed, looking only for sequences of negation particles (or morphemes) in order to list occurring constructions, without taking into account the verbs occurring in them, apart from their endings. The approach in (2) is only intended to show a possible methodology to extend existing theories on occurring negations. We would also like to try to help lexicographers to establish a frequency-based order of entries of possible negation forms in their dictionaries by showing them the number of respective occurrences. As with all corpus linguistic work, however, we must regard corpus evidence not as representative, but as tendencies of language use that can be detected and described. This is especially true for Sepedi, for which only few and small corpora exist. This paper also describes the resources and tools used to create the necessary corpus and also how it was annotated with part of speech and lemmas. Exploring the quality of available Sepedi part-of-speech taggers concerning verbs, negation morphemes and subject concords may be a positive side result.
Between classical symbolic word sense disambiguation (wsd) using explicit deep semantic representations of sentences and texts and statistical wsd using word co-occurrence information, there is a recent tendency towards mediating methods. Similar to so-called lightweight semantics (Marek, 2009) we suggest to only make sparse use of semantic information. We describe an approximation model based upon flat underspecified discourse representation structures (FUDRSs, cf. Eberle, 2004) that weighs knowledge about context structure, lexical semantic restrictions and interpretation preferences. We give a catalogue of guidelines for human annotation of texts by corresponding indicators. Using this, the reliability of an analysis tool that implements the model can be tested with respect to annotation precision and disambiguation prediction and how both can be improved by bootstrapping the knowledge of the system using corpus information. For the balanced test corpus considered the recognition rate of the preferred reading is 80-90% (depending on the smoothing of parse errors).
This paper describes work directed towards the development of a syllable prominence-based prosody generation functionality for a German unit selection speech synthesis system. A general concept for syllable prominence-based prosody generation in unit selection synthesis is proposed. As a first step towards its implementation, an automated syllable prominence annotation procedure based on acoustic analyses has been performed on the BOSS speech corpus. The prominence labeling has been evaluated against an existing annotation of lexical stress levels and manual prominence labeling on a subset of the corpus. We discuss methods and results and give an outlook on further implementation steps.
Active learning has been applied to different NLP tasks, with the aim of limiting the amount of time and cost for human annotation. Most studies on active learning have only simulated the annotation scenario, using prelabelled gold standard data. We present the first active learning experiment for Word Sense Disambiguation with human annotators in a realistic environment, using fine-grained sense distinctions, and investigate whether AL can reduce annotation cost and boost classifier performance when applied to a real-world task.
The availability of large multi-parallel corpora offers an enormous wealth of material to contrastive corpus linguists, translators and language learners, if we can exploit the data properly. Necessary preparation steps include sentence and word alignment across multiple languages. Additionally, linguistic annotation such as partof- speech tagging, lemmatisation, chunking, and dependency parsing facilitate precise querying of linguistic properties and can be used to extend word alignment to sub-sentential groups. Such highly interconnected data is stored in a relational database to allow for efficient retrieval and linguistic data mining, which may include the statistics-based selection of good example sentences. The varying information needs of contrastive linguists require a flexible linguistic query language for ad hoc searches. Such queries in the format of generalised treebank query languages will be automatically translated into SQL queries.
We present web services which implement a workflow for transcripts of spoken language following the 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.
We introduce a method for error detection in automatically annotated text, aimed at supporting the creation of high-quality language resources at affordable cost. Our method combines an unsupervised generative model with human supervision from active learning. We test our approach on in-domain and out-of-domain data in two languages, in AL simulations and in a real world setting. For all settings, the results show that our method is able to detect annotation errors with high precision and high recall.
With an increasing amount of text data available it is possible to automatically extract a variety of information about language. One way to obtain knowledge about subtle relations and analogies between words is to observe words which are used in the same context. Recently, Mikolov et al. proposed a method to efficiently compute Euclidean word representations which seem to capture subtle relations and analogies between words in the English language. We demonstrate that this method also captures analogies in the German language. Furthermore, we show that we can transfer information extracted from large non-annotated corpora into small annotated corpora, which are then, in turn, used for training NLP systems.
Der Beitrag beschreibt die Entwicklung und Anwendung des TEI-basierten ISO-Standards ISO 24624:2016 Transcription of spoken language, der seit einigen Jahren für gesprochensprachliche Forschungsdaten aus unterschiedlichen Kontexten eingesetzt wird. Ein standardisiertes Dateiformat ermöglicht Interoperabilität zwischen verschiedenen Werkzeugen und weiteren Angeboten von Datenzentren und Infrastrukturen. Durch die methodologisch fundierte Abwägung zwischen Standardisierung und Flexibilität kann der ISO/TEI-Standard zudem Forschungsdaten aus verschiedenen Forschungskontexten abbilden, und so interdisziplinäre Vorhaben erleichtern. Der Beitrag stellt einige Anwendungsbereiche aus dem Lebenszyklus gesprochensprachlicher Forschungsdaten vor, in denen auf dem ISO/TEI-Standard basierenden Erweiterungen existierender Softwarelösungen erfolgreich umgesetzt werden konnten, und zeigt weitere Beispiele für die zunehmende Verbreitung des Formats.
In 2010, ISO published a standard for syntactic annotation, ISO 24615:2010 (SynAF). Back then, the document specified a comprehensive reference model for the representation of syntactic annotations, but no accompanying XML serialisation. ISO’s subcommittee on language resource management (ISO TC 37/SC 4) is working on making the SynAF serialisation ISOTiger an additional part of the standard. This contribution addresses the current state of development of ISOTiger, along with a number of open issues on which we are seeking community feedback in order to ensure that ISOTiger becomes a useful extension to the SynAF reference model.
Accurate opinion mining requires the exact identification of the source and target of an opinion. To evaluate diverse tools, the research community relies on the existence of a gold standard corpus covering this need. Since such a corpus is currently not available for German, the Interest Group on German Sentiment Analysis decided to create such a resource and make it available to the research community in the context of a shared task. In this paper, we describe the selection of textual sources, development of annotation guidelines, and first evaluation results in the creation of a gold standard corpus for the German language.
Current Natural Language Processing (NLP) systems feature high-complexity processing pipelines that require the use of components at different levels of linguistic and application specific processing. These components often have to interface with external e.g. machine learning and information retrieval libraries as well as tools for human annotation and visualization. At the UKP Lab, we are working on the Darmstadt Knowledge Processing Software Repository (DKPro) (Gurevych et al., 2007a; Müller et al., 2008) to create a highly flexible, scalable and easy-to-use toolkit that allows rapid creation of complex NLP pipelines for semantic information processing on demand. The DKPro repository consists of several main parts created to serve the purposes of different NLP application areas
In mid-2017, as part of our activities within the TEI Special Interest Group for Linguists (LingSIG), we submitted to the TEI Technical Council a proposal for a new attribute class that would gather attributes facilitating simple token-level linguistic annotation. With this proposal, we addressed community feedback complaining about the lack of a specific tagset for lightweight linguistic annotation within the TEI. Apart from @lemma and @lemmaRef, up till now TEI encoders could only resort to using the generic attribute @ana for inline linguistic annotation, or to the quite complex system of feature structures for robust linguistic annotation, the latter requiring relatively complex processing even for the most basic types of linguistic features. As a result, there now exists a small set of basic descriptive devices which have been made available at the cost of only very small changes to the TEI tagset. The merit of a predefined TEI tagset for lightweight linguistic annotation is the homogeneity of tagging and thus better interoperability of simple linguistic resources encoded in the TEI. The present paper introduces the new attributes, makes a case for one more addition, and presents the advantages of the new system over the legacy TEI solutions.
Metadata provides important information relevant both to finding and understanding corpus data. Meaningful linguistic data requires both reasonable annotations and documentation of these annotations. This documentation is part of the metadata of a dataset. While corpus documentation has often been provided in the form of accompanying publications, machinereadable metadata, both containing the bibliographic information and documenting the corpus data, has many advantages. Metadata standards allow for the development of common tools and interfaces. In this paper I want to add a new perspective from an archive’s point of view and look at the metadata provided for four learner corpora and discuss the suitability of established standards for machine-readable metadata. I am are aware that there is ongoing work towards metadata standards for learner corpora. However, I would like to keep the discussion going and add another point of view: increasing findability and reusability of learner corpora in an archiving context.
We present a light-weight tool for the annotation of linguistic data on multiple levels. It is based on the simplification of annotations to sets of markables having attributes and standing in certain relations to each other. We describe the main features of the tool, emphasizing its simplicity, customizability and versatility
In this paper, we present the Multiple Annotation approach, which solves two problems: the problem of annotating overlapping structures, and the problem that occurs when documents should be annotated according to different, possibly heterogeneous tag sets. This approach has many advantages: it is based on XML, the modeling of alternative annotations is possible, each level can be viewed separately, and new levels can be added at any time. The files can be regarded as an interrelated unit, with the text serving as the implicit link. Two representations of the information contained in the multiple files (one in Prolog and one in XML) are described. These representations serve as a base for several applications.
The annotation of parts of speech (POS) in linguistically annotated corpora is a fundamental annotation layer which provides the basis for further syntactic analyses, and many NLP tools rely on POS information as input. However, most POS annotation schemes have been developed with written (newspaper) text in mind and thus do not carry over well to text from other domains and genres. Recent discussions have concentrated on the shortcomings of present POS annotation schemes with regard to their applicability to data from domains other than newspaper text.
Contents:
1. Michal Křen: Recent Developments in the Czech National Corpus, S. 1
2. Dan Tufiş, Verginica Barbu Mititelu, Elena Irimia, Stefan Dumitrescu, Tiberiu Boros, Horia Nicolai Teodorescu: CoRoLa Starts Blooming – An update on the Reference Corpus of Contemporary Romanian Language, S. 5
3. Sebastian Buschjäger, Lukas Pfahler, Katharina Morik: Discovering Subtle Word Relations in Large German Corpora, S. 11
4. Johannes Graën, Simon Clematide: Challenges in the Alignment, Management and Exploitation of Large and Richly Annotated Multi-Parallel Corpora, S. 15
5. Stefan Evert, Andrew Hardie: Ziggurat: A new data model and indexing format for large annotated text corpora, S. 21
6. Roland Schäfer: Processing and querying large web corpora with the COW14 architecture, S. 28
7. Jochen Tiepmar: Release of the MySQL-based implementation of the CTS protocol, S. 35
In this paper, I present the COW14 tool chain, which comprises a web corpus creation tool called texrex, wrappers for existing linguistic annotation tools as well as an online query software called Colibri2. By detailed descriptions of the implementation and systematic evaluations of the performance of the software on different types of systems, I show that the COW14 architecture is capable of handling the creation of corpora of up to at least 100 billion tokens. I also introduce our running demo system which currently serves corpora of up to roughly 20 billion tokens in Dutch, English, French, German, Spanish, and Swedish
"Das im Januar 2022 gestartete Projekt "Sprachanfragen" (https://www.ids-mannheim.de/gra/projekte2/sprachanfragen/) verfolgt erstmalig das Ziel, Sprachanfragedaten zu erfassen, aufzubereiten und ein wissenschaftsöffentliches Monitorkorpus aus ihnen zu erstellen. Dazukommend wird eine Rechercheschnittstelle entwickelt, mit der die Sprachanfragen systematisch wissenschaftlich analysierbar gemacht werden. Das Poster gibt einen Überblick über das Projekt, zeigt erste Ergebnisse und bietet einen Ausblick auf Überlegungen zur Konzeption eines Chatbots zur automatisierten Beantwortung von Sprachanfragen." Ein Beitrag zur 9. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2023 Open Humanities Open Culture.
The Czech National Corpus (CNC) is a longterm project striving for extensive and continuous mapping of the Czech language. This effort results mostly in compilation, maintenance and providing free public access to a range of various corpora with the aim to offer a diverse, representative, and high-quality data for empirical research mainly in linguistics. Since 2012, the CNC is officially recognized as a research infrastructure funded by the Czech Ministry of Education, Youth and Sports which has caused a recent shift towards user service-oriented operation of the project. All project-related resources are now integrated into the CNC research portal at http://www.korpus.cz/. Currently, the CNC has an established and growing user community of more than 4,500 active users in the Czech Republic and abroad who put almost 1,900 queries per day using one of the user interfaces. The paper discusses the main CNC objectives for each particular domain, aiming at an overview of the current situation supplemented by an outline of future plans.
Die vorgestellte Studie untersucht die Anteile unterschiedlicher Redewiedergabeformen im Vergleich zwischen zwei Literaturtypen von gegensätzlichen Enden des Spektrums: Hochliteratur – definiert als Werke, die auf der Auswahlliste von Literaturpreisen standen – und Heftromanen, massenproduzierten Erzählwerken, die zumeist über den Zeitschriftenhandel vertrieben werden und früher abwertend als „Romane der Unterschicht” (Nusser 1981) bezeichnet wurden. Unsere These ist, dass sich diese Literaturtypen hinsichtlich ihrer Erzählweise unterscheiden, und sich dies in den verwendeten Wiedergabeformen niederschlägt. Der Fokus der Untersuchung liegt auf der Dichotomie zwischen direkter und nicht-direkter Wiedergabe, die schon in der klassischen Rhetorik aufgemacht wurde.
This contribution presents a quantitative approach to speech, thought and writing representation (ST&WR) and steps towards its automatic detection. Automatic detection is necessary for studying ST&WR in a large number of texts and thus identifying developments in form and usage over time and in different types of texts. The contribution summarizes results of a pilot study: First, it describes the manual annotation of a corpus of short narrative texts in relation to linguistic descriptions of ST&WR. Then, two different techniques of automatic detection – a rule-based and a machine learning approach – are described and compared. Evaluation of the results shows success with automatic detection, especially for direct and indirect ST&WR.
The QUEST (QUality ESTablished) project aims at ensuring the reusability of audio-visual datasets (Wamprechtshammer et al., 2022) by devising quality criteria and curating processes. RefCo (Reference Corpora) is an initiative within QUEST in collaboration with DoReCo (Documentation Reference Corpus, Paschen et al. (2020)) focusing on language documentation projects. Previously, Aznar and Seifart (2020) introduced a set of quality criteria dedicated to documenting fieldwork corpora. Based on these criteria, we establish a semi-automatic review process for existing and work-in-progress corpora, in particular for language documentation. The goal is to improve the quality of a corpus by increasing its reusability. A central part of this process is a template for machine-readable corpus documentation and automatic data verification based on this documentation. In addition to the documentation and automatic verification, the process involves a human review and potentially results in a RefCo certification of the corpus. For each of these steps, we provide guidelines and manuals. We describe the evaluation process in detail, highlight the current limits for automatic evaluation and how the manual review is organized accordingly.
This paper analyses reply relations in computer-mediated communication (CMC), which occur between post units in CMC interactions and which describe references between posts. We take a look at existing practices in the description and annotation of such relations in chat, wiki talk, and blog corpora. We distinguish technical reply structures, indentation structures, and interpretative reply relations, which include reply relations induced by linguistic markers. We sort out the different levels of description and annotation that are involved and propose a solution for their combined representation within the TEI annotation framework.
This paper analyses reply relations in computer-mediated communication (CMC), which occur between post units in CMC interactions and which describe references between posts. We take a look at existing practices in the description and annotation of such relations in chat, wiki talk, and blog corpora. We distinguish technical reply structures, indentation structures, and interpretative reply relations, which include reply relations induced by linguistic markers. We sort out the different levels of description and annotation that are involved and propose a solution for their combined representation within the TEI annotation framework.
In der Computerlinguistik ist eine kaskadische Prozessierung von Texten üblich. Dabei werden diese zuerst segmentiert (tokenisiert), d.h. Tokens und ggf. Satzgrenzen werden erkannt. Dabei entsteht meist eine Liste bzw. eine einspaltige Tabelle, die sukzessive durch weitere Prozessierungschritte um zusätzliche Spalten – also positionale Annotationen wie z.B. Wortarten und Lemmata für die Tokens in der ersten Spalte – ergänzt wird. Bei der Tokenisierung werden alle Spatien (Leerzeichen) gelöscht. Schon immer problematisch waren dabei Interpunktionszeichen, da diese äußerst ambig sein können, aber auch mehrteilige Namen, die Leerzeichen enthalten und eigentlich zusammengehören. Dieser Beitrag fokussiert auf den Apostroph, der in vielfältiger Weise in den Texten Udo Lindenbergs eingesetzt wird sowie auf mehrteilige Namen, die wir als Tokens erhalten möchten. Wir nutzen dafür das komplette Lindenberg-Archiv des song-korpus.de-Repositoriums, kategorisieren die auftretenden Phänomene, erstellen einen Goldstandard und entwickeln ein teils regel-, teils auf maschinellem Lernen basierendes Segmentierungswerkzeug, das insbesondere die auftretenden Apostrophe, aber auch -lexikonbasiert - mehrteilige Namen nach unseren Vorstellungen erkennt und tokenisiert. Im Anschluss trainieren wir den RNN-Tagger (Schmid, 2019) und zeigen auf, dass ein spezifisch für diese Texte angepasstes Training zu Genauigkeiten ≥ 96% führt. Dabei entsteht nicht nur ein Goldstandard des annotierten Korpus, das dem Songkorpus-Repositorium zur Verfügung gestellt wird, sondern auch eine angepasste Version des RNN-Taggers (verfügbar auf github), die für ähnliche Texte verwendet werden kann.
We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning. We evaluate our method in different scenarios: (i) for error detection in dependency treebanks and (ii) for improving parsing accuracy on in- and out-of-domain data.
This paper presents an extended annotation and analysis of interpretative reply relations focusing on a comparison of reply relation types and targets between conflictual pages and neutral pages of German Wikipedia (WP) talk pages. We briefly present the different categories identified for interpretative reply relations to analyze the relationship between WP postings as well as linguistic cues for each category. We investigate referencing strategies of WP authors in discussion page postings, illustrated by means of reply relation types and targets taking into account the degree of disagreement displayed on a WP talk page. We provide richly annotated data that can be used for further analyses such as the identification of interactional relations on higher levels, or for training tasks in machine learning algorithms.
This paper describes the TEI-based ISO standard 24624:2016 ‘Transcription of spoken language’ and other formats used within CLARIN for spoken language resources. It assesses the current state of support for the standard and the interoperability between these formats and with rele- vant tools and services. The main idea behind the paper is that a digital infrastructure providing language resources and services to researchers should also allow the combined use of resources and/or services from different contexts. This requires syntactic and semantic interoperability. We propose a solution based on the ISO/TEI format and describe the necessary steps for this format to work as an exchange format with basic semantic interoperability for spoken language resources across the CLARIN infrastructure and beyond.
In the paper we investigate the impact of data size on a Word Sense Disambiguation task (WSD). We question the assumption that the knowledge acquisition bottleneck, which is known as one of the major challenges for WSD, can be solved by simply obtaining more and more training data. Our case study on 1,000 manually annotated instances of the German verb drohen (threaten) shows that the best performance is not obtained when training on the full data set, but by carefully selecting new training instances with regard to their informativeness for the learning process (Active Learning). We present a thorough evaluation of the impact of different sampling methods on the data sets and propose an improved method for uncertainty sampling which dynamically adapts the selection of new instances to the learning progress of the classifier, resulting in more robust results during the initial stages of learning. A qualitative error analysis identifies problems for automatic WSD and discusses the reasons for the great gap in performance between human annotators and our automatic WSD system.
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.
This article presents a discussion on the main linguistic phenomena which cause difficulties in the analysis of user-generated texts found on the web and in social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework of syntactic analysis. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this article is twofold: (1) to provide a condensed, though comprehensive, overview of such treebanks—based on available literature—along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The overarching goal of this article is to provide a common framework for researchers interested in developing similar resources in UD, thus promoting cross-linguistic consistency, which is a principle that has always been central to the spirit of UD.
Twenty-two historical encyclopedias encoded in TEI: a new resource for the Digital Humanities
(2020)
This paper accompanies the corpus publication of EncycNet, a novel XML/TEI annotated corpus of 22 historical German encyclopedias from the early 18th to early 20th century. We describe the creation and annotation of the corpus, including the rationale for its development, suggested methodology for TEI annotation, possible use cases and future work. While many well-developed annotation standards for lexical resources exist, none can adequately model the encyclopedias at hand, and we therefore suggest how the TEI Lex-0 standard may be modified with additional guidelines for the annotation of historical encyclopedias. As the digitization and annotation of historical encyclopedias are settling on TEI as the de facto standard, our methodology may inform similar projects.
Universal Dependency (UD) annotations, despite their usefulness for cross-lingual tasks and semantic applications, are not optimised for statistical parsing. In the paper, we ask what exactly causes the decrease in parsing accuracy when training a parser on UD-style annotations and whether the effect is similarly strong for all languages. We conduct a series of experiments where we systematically modify individual annotation decisions taken in the UD scheme and show that this results in an increased accuracy for most, but not for all languages. We show that the encoding in the UD scheme, in particular the decision to encode content words as heads, causes an increase in dependency length for nearly all treebanks and an increase in arc direction entropy for many languages, and evaluate the effect this has on parsing accuracy.
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 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.
This paper presents an annotation scheme for English modal verbs together with sense-annotated data from the news domain. We describe our annotation scheme and discuss problematic cases for modality annotation based on the inter-annotator agreement during the annotation. Furthermore, we present experiments on automatic sense tagging, showing that our annotations do provide a valuable training resource for NLP systems.
The IMS Open Corpus Workbench (CWB) software currently uses a simple tabular data model with proven limitations. We outline and justify the need for a new data model to underlie the next major version of CWB. This data model, dubbed Ziggurat, defines a series of types of data layer to represent different structures and relations within an annotated corpus; each such layer may contain variables of different types. Ziggurat will allow us to gradually extend and enhance CWB’s existing CQP-syntax for corpus queries, and also make possible more radical departures relative not only to the current version of CWB but also to other contemporary corpus-analysis software.