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Der Beitrag beschreibt Konzeption und Umsetzung der Anbindung von lexikalischen Datenbanken an das grammatische Informationssystem grammis, das seit Mitte 1993 am Institut für deutsche Sprache (IDS) entwickelt wird. Im Rahmen dieses Projekts wird erforscht, wie grammatisches Wissen mit moderner Computertechnik anschaulich dargestellt und verständlich vermittelt werden kann.
We describe a simple procedure for the automatic creation of word-level alignments between printed documents and their respective full-text versions. The procedure is unsupervised, uses standard, off-the-shelf components only, and reaches an F-score of 85.01 in the basic setup and up to 86.63 when using pre- and post-processing. Potential areas of application are manual database curation (incl. document triage) and biomedical expression OCR.
This paper will address the challenge of creating a knowledge graph from a corpus of historical encyclopedias with a special focus on word sense alignment (WSA) and disambiguation (WSD). More precisely, we examine WSA and WSD approaches based on article similarity to link messy historical data, utilizing Wikipedia as aground-truth component – as the lack of a critical overlap in content paired with the amount of variation between and within the encyclopedias does not allow for choosing a ”baseline” encyclopedia to align the others to. Additionally, we are comparing the disambiguation performance of conservative methods like the Lesk algorithm to more recent approaches, i.e. using language models to disambiguate senses.
N-grams are of utmost importance for modern linguistics and language technology. The legal status of n-grams, however, raises many practical questions. Traditionally, text snippets are considered copyrightable if they meet the originality criterion, but no clear indicators as to the minimum length of original snippets exist; moreover, the solutions adopted in some EU Member States (the paper cites German and French law as examples) are considerably different. Furthermore, recent developments in EU law (the CJEU's Pelham decision and the new right of press publishers) also provide interesting arguments in this debate. The paper presents the existing approaches to the legal protection of n-grams and tries to formulate some clear guidelines as to the length of n-grams that can be freely used and shared.
Vorwort
(2007)
Vorwort
(2008)
Vorwort
(2010)
Die zentrale Aufgabenstellung des Verbundprojektes TextTransfer (Pilot) war eine Machbarkeitsprüfung für die Entwicklung eines Text-Mining-Verfahrens, mit dem Forschungsergebnisse automatisiert auf Hinweise zu Transfer- und Impactpotenzialen untersucht werden können. Das vom Projektkoordinator IDS verantwortete Teilprojekt konzentrierte sich dabei auf die Entwicklung der methodischen Grundlagen, während der Projektpartner TIB vornehmlich für die Bereitstellung eines geeigneten Datensatzes verantwortlich war. Solchen automatisierten Verfahren liegen zumeist textbasierte Daten als physisches Manifest wissenschaftlicher Erkenntnisse zugrunde, die im Falle von TextTransfer (Pilot) als empirische Grundlage herangezogen wurden. Das im Verbund zur Anwendung gebrachte maschinelle Lernverfahren stützte sich ausschließlich auf deutschsprachige Projektendberichte öffentlich geförderter Forschung. Diese Textgattung eignet sich insbesondere hinsichtlich ihrer öffentlichen Verfügbarkeit bei zuständigen Gedächtnisorganisationen und aufgrund ihrer im Vergleich zu anderen Formaten wissenschaftlicher Publikation relativen strukturellen wie sprachlichen Homogenität. TextTransfer (Pilot) ging daher grundsätzlich von der Annahme struktureller bzw. sprachlicher Ähnlichkeit in Berichtstexten aus, bei denen der Nachweis tatsächlich erfolgten Transfers zu erbringen war. Im Folgenden wird in diesen Fällen von Texten bzw. textgebundenen Forschungsergebnissen mit Transfer- und Impactpotenzial gesprochen werden. Es wurde ferner postuliert, dass sich diese Indizien von sprachlichen Eigenschaften in Texten zu Projekten ohne nachzuweisenden bzw. ggf. auch niemals erfolgtem, aber potenziell möglichem Transfer oder Impact unterscheiden lassen. Mit einer Verifizierung dieser Annahmen war es möglich, Transfer- oder Impactwahrscheinlichkeiten in großen Mengen von Berichtsdaten ohne eingehende Lektüre zu prognostizieren.
As a part of the ZuMult-project, we are currently modelling a backend architecture that should provide query access to corpora from the Archive of Spoken German (AGD) at the Leibniz-Institute for the German Language (IDS). We are exploring how to reuse existing search engine frameworks providing full text indices and allowing to query corpora by one of the corpus query languages (QLs) established and actively used in the corpus research community. For this purpose, we tested MTAS - an open source Lucene-based search engine for querying on text with multilevel annotations. We applied MTAS on three oral corpora stored in the TEI-based ISO standard for transcriptions of spoken language (ISO 24624:2016). These corpora differ from the corpus data that MTAS was developed for, because they include interactions with two and more speakers and are enriched, inter alia, with timeline-based annotations. In this contribution, we report our test results and address issues that arise when search frameworks originally developed for querying written corpora are being transferred into the field of spoken language.
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.
Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus
(2021)
Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.
We present WOMBAT, a Python tool which supports NLP practitioners in accessing word embeddings from code. WOMBAT addresses common research problems, including unified access, scaling, and robust and reproducible preprocessing. Code that uses WOMBAT for accessing word embeddings is not only cleaner, more readable, and easier to reuse, but also much more efficient than code using standard in-memory methods: a Python script using WOMBAT for evaluating seven large word embedding collections (8.7M embedding vectors in total) on a simple SemEval sentence similarity task involving 250 raw sentence pairs completes in under ten seconds end-to-end on a standard notebook computer.
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.
Linguistics is facing the challenge of many other sciences as it continues to grow into increasingly complex subfields, each with its own separate or overarching branches. While linguists are certainly aware of the overall structure of the research field, they cannot follow all developments other than those of their subfields. It is thus important to help specialists but also newcomers alike to bushwhack through evolved or unknown territory of linguistic data. A considerable amount of research data in linguistics is described with metadata. While studies described and published in archived journals and conference proceedings receive a quite homogeneous set of metadata tags — e.g., author, title, publisher —, this does not hold for the empirical data and analyses that underlie such studies. Moreover, lexicons, grammars, experimental data, and other types of resources come in different forms; and to make things worse, their description in terms of metadata is also not uniform, if existing at all. These problems are well-known and there are now a number of international initiatives — e.g., CLARIN, FlareNet, MetaNet, DARIAH — to build infrastructures for managing linguistic resources. The NaLiDa project, funded by the German Research Foundation, aims at facilitating the management and access to linguistic resources originating from German research institutions. In cooperation with the German SFB 833 research center, we are developing a combination of faceted and full-text search to give integrated access through heterogeneous metadata sets. Our approach is supported by a central registry for metadata field descriptors, and a component repository for structured groups of data categories as larger building blocks.
This paper addresses the task of finding antecedents for locally uninstantiated arguments. To resolve such null instantiations, we develop a weakly supervised approach that investigates and combines a number of linguistically motivated strategies that are inspired by work on semantic role labeling and corefence resolution. The performance of the system is competitive with the current state-of-the-art supervised system.