S1: Korpuslinguistik
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The automatic recognition of idioms poses a challenging problem for NLP applications. Whereas native speakers can intuitively handle multiword expressions whose compositional meanings are hard to trace back to individual word semantics, there is still ample scope for improvement regarding computational approaches. We assume that idiomatic constructions can be characterized by gradual intensities of semantic non-compositionality, formal fixedness, and unusual usage context, and introduce a number of measures for these characteristics, comprising count-based and predictive collocation measures together with measures of context (un)similarity. We evaluate our approach on a manually labelled gold standard, derived from a corpus of German pop lyrics. To this end, we apply a Random Forest classifier to analyze the individual contribution of features for automatically detecting idioms, and study the trade-off between recall and precision. Finally, we evaluate the classifier on an independent dataset of idioms extracted from a list of Wikipedia idioms, achieving state-of-the art accuracy.
In order to differentiate between figurative and literal usage of verb-noun combinations for the shared task on the disambiguation of German Verbal Idioms issued for KONVENS 2021, we apply and extend an approach originally developed for detecting idioms in a dataset consisting of random ngram samples. The classification is done by implementing a rather shallow, statistics-based pipeline without intensive preprocessing and examinations on the morphosyntactic and semantic level. We describe the overall approach, the differences between the original dataset and the dataset of the KONVENS task, provide experimental classification results, and analyse the individual contributions of our feature sets.
In this Paper, we describe a schema and models which have been developed for the representation of corpora of computer-mediated communicatin (CMC corpora) using the representation framework provided by the Text Encoding Initiative (TEI). We characterise CMC discourse as dialogic, sequentially organised interchange between humans and point out that many features of CMC are not adequately handled by current corpus encoding schemas and tools. We formulate desiderata for a representation of CMC in encoding schemes and argue why the TEI is a suitable framework for the encoding of CMC corpora. We propose a model of basic CMC units (utterances, posts, and nonverbal activities) and the macro- and micro-level structures of interactions in CMC environments. Based on these models, we introduce CMC-core, a TEI customisation for the encoding of CMC corpora, which defines CMC-specific encoding features on the four levels of elements, model classes, attribute classes, and modules of the TEI infrastructure. The description of our customisation is illustrated by encoding examples from corpora by researchers of the TEI SIG CMC, representing a variety of CMC genres, i.e. chat, wiki talk, twitter, blog, and Second Life interactions. The material described, i.e. schemata, encoding examples, and documentation, is available from the of the TEI CMC SIG Wiki and will accompany a feature request to the TEI council in late 2019.
Dieses Kapitel gibt einen Überblick über Korpora internetbasierter Kommunikation, die als digitale Ressourcen frei zur Verfügung stehen und für eigene linguistische Forschungsarbeiten genutzt werden können. In Abschnitt 1 erläutern wir korpuslinguistische Basiskonzepte, die für die Arbeit mit Korpora internetbasierter Kommunikation benötigt werden, und präzisieren die Sprachgebrauchsdomäne Internetbasierte Kommunikation, die den Gegenstand des hier beschriebenen Ressourcentyps bildet. Abschnitt 2 gibt einen Überblick zu existierenden Korpusressourcen für das Deutsche und stellt ausgewählte Korpora zu weiteren europäischen Sprachen vor. In Abschnitt 3 geben wir abschließend einen kurzen Einblick in aktuelle Forschungsfelder, die sich im Bereich der Korpuslinguistik und Sprachtechnologie in Bezug auf den Aufbau und die Aufbereitung von Korpora internetbasierter Kommunikation stellen.
This paper presents an algorithm and an implementation for efficient tokenization of texts of space-delimited languages based on a deterministic finite state automaton. Two representations of the underlying data structure are presented and a model implementation for German is compared with state-of-the-art approaches. The presented solution is faster than other tools while maintaining comparable quality.
Die Korpusanalyseplattform KorAP ist von Grund auf sprachenunabhängig konzipiert. Dies gilt sowohl in Bezug auf die Lokalisierung der Benutzeroberfläche als auch hinsichtlich unterschiedlicher Anfragesprachen und der Unterstützung fremdsprachiger Korpora und ihren Annotationen. Diese Eigenschaften dienen im Rahmen der EuReCo Initiative aktuell besonders der Bereitstellung weiterer National- und Referenzkorpora neben DeReKo. EuReCo versucht, Kompetenzen beim Aufbau großer Korpora zu bündeln und durch die Verfügbarmachung vergleichbarer Korpora quantitative Sprachvergleichsforschung zu erleichtern. Hierzu bietet KorAP inzwischen, neben dem Zugang durch die Benutzeroberfläche, einen Web API Client an, der statistische Erhebungen, auch korpusübergreifend, vereinfacht.
When comparing different tools in the field of natural language processing (NLP), the quality of their results usually has first priority. This is also true for tokenization. In the context of large and diverse corpora for linguistic research purposes, however, other criteria also play a role – not least sufficient speed to process the data in an acceptable amount of time. In this paper we evaluate several state of the art tokenization tools for German – including our own – with regard to theses criteria. We conclude that while not all tools are applicable in this setting, no compromises regarding quality need to be made.
In this paper, we present our experiences and decisions in dealing with challenges in developing, maintaining and operating online research software tools in the field of linguistics. In particular, we highlight reproducibility, dependability, and security as important aspects of quality management – taking into account the special circumstances in which research software
is usually created.
Individuals with Autism Spectrum Disorder (ASD) experience a variety of symptoms sometimes including atypicalities in language use. The study explored diferences in semantic network organisation of adults with ASD without intellectual impairment. We assessed clusters and switches in verbal fuency tasks (‘animals’, ‘human feature’, ‘verbs’, ‘r-words’) via curve ftting in combination with corpus-driven analysis of semantic relatedness and evaluated socio-emotional and motor action related content. Compared to participants without ASD (n=39), participants with ASD (n=32) tended to produce smaller clusters, longer switches, and fewer words in semantic conditions (no p values survived Bonferroni-correction), whereas relatedness and content were similar. In ASD, semantic networks underlying cluster formation appeared comparably small without afecting strength of associations or content.