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This paper reports on the efforts of twelve national teams in building the International Comparable Corpus (ICC; https://korpus.cz/icc) that will contain highly comparable datasets of spoken, written and electronic registers. The languages currently covered are Czech, Finnish, French, German, Irish, Italian, Norwegian, Polish, Slovak, Swedish and, more recently, Chinese, as well as English, which is considered to be the pivot language. The goal of the project is to provide much-needed data for contrastive corpus-based linguistics. The ICC corpus is committed to the idea of re-using existing multilingual resources as much as possible and the design is modelled, with various adjustments, on the International Corpus of English (ICE). As such, ICC will contain approximately the same balance of forty percent of written language and 60 percent of spoken language distributed across 27 different text types and contexts. A number of issues encountered by the project teams are discussed, ranging from copyright and data sustainability to technical advances in data distribution.
Dictionaries have been part and parcel of literate societies for many centuries. They assist in communication, particularly across different languages, to aid in understanding, creating, and translating texts. Communication problems arise whenever a native speaker of one language comes into contact with a speaker of another language. At the same time, English has established itself as a lingua franca of international communication. This marked tendency gives lexicography of English a particular significance, as English dictionaries are used intensively and extensively by huge numbers of people worldwide.
We introduce DeReKoGram, a novel frequency dataset containing lemma and part-of-speech (POS) information for 1-, 2-, and 3-grams from the German Reference Corpus. The dataset contains information based on a corpus of 43.2 billion tokens and is divided into 16 parts based on 16 corpus folds. We describe how the dataset was created and structured. By evaluating the distribution over the 16 folds, we show that it is possible to work with a subset of the folds in many use cases (e.g., to save computational resources). In a case study, we investigate the growth of vocabulary (as well as the number of hapax legomena) as an increasing number of folds are included in the analysis. We cross-combine this with the various cleaning stages of the dataset. We also give some guidance in the form of Python, R, and Stata markdown scripts on how to work with the resource.
Neologisms, i.e., new words or meanings, are finding their way into everyday language use all the time. In the process, already existing elements of a language are recombined or linguistic material from other languages is borrowed. But are borrowed neologisms accepted similarly well by the speech community as neologisms that were formed from “native” material? We investigate this question based on neologisms in German. Building on the corresponding results of a corpus study, we test the hypothesis of whether “native” neologisms are more readily accepted than those borrowed from English. To do so, we use a psycholinguistic experimental paradigm that allows us to estimate the degree of uncertainty of the participants based on the mouse trajectories of their responses. Unexpectedly, our results suggest that the neologisms borrowed from English are accepted more frequently, more quickly, and more easily than the “native” ones. These effects, however, are restricted to people born after 1980, the so-called millenials. We propose potential explanations for this mismatch between corpus results and experimental data and argue, among other things, for a reinterpretation of previous corpus studies.
Based on the privative derivational suffix -los, we test statements found in the literature on word formation using a – at least in this field – novel empirical basis: a list of affective-emotional ratings of base nouns and associated -los derivations. In addition to a frequency analysis based on the German Reference Corpus, we show that, in general, emotional polarity (so-called valence, positive vs. negative emotions) is reversed by suffixation with -los. This change is stronger for more polarized base nouns. The perceived intensity of emotion (so-called arousal) is generally lower for -los derivations than for base nouns. Finally, to capture the results theoretically, we propose a prototypical -los construction in the framework of Construction Morphology.
Poster des Text+ Partners Leibniz-Institut für Deutsche Sprache Mannheim präsentiert beim Workshop "Wohin damit? Storing and reusing my language data" am 22. Juni 2023 in Mannheim. Das Poster wurde im Kontext der Arbeit des Vereins Nationale Forschungsdateninfrastruktur (NFDI) e.V. verfasst. NFDI wird von der Bundesrepublik Deutschland und den 16 Bundesländern finanziert, und das Konsortium Text+ wird gefördert durch die Deutsche Forschungsgemeinschaft (DFG) – Projektnummer 460033370. Die Autor:innen bedanken sich für die Förderung sowie Unterstützung. Ein Dank geht außerdem an alle Einrichtungen und Akteur:innen, die sich für den Verein und dessen Ziele engagieren.
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.
We present a gold standard for semantic relation extraction in the food domain for German. The relation types that we address are motivated by scenarios for which IT applications present a commercial potential, such as virtual customer advice in which a virtual agent assists a customer in a supermarket in finding those products that satisfy their needs best. Moreover, we focus on those relation types that can be extracted from natural language text corpora, ideally content from the internet, such as web forums, that are easy to retrieve. A typical relation type that meets these requirements are pairs of food items that are usually consumed together. Such a relation type could be used by a virtual agent to suggest additional products available in a shop that would potentially complement the items a customer has already in their shopping cart. Our gold standard comprises structural data, i.e. relation tables, which encode relation instances. These tables are vital in order to evaluate natural language processing systems that extract those relations.
In this paper, we examine methods to automatically extract domain-specific knowledge from the food domain from unlabeled natural language text. We employ different extraction methods ranging from surface patterns to co-occurrence measures applied on different parts of a document. We show that the effectiveness of a particular method depends very much on the relation type considered and that there is no single method that works equally well for every relation type. We also examine a combination of extraction methods and also consider relationships between different relation types. The extraction methods are applied both on a domain-specific corpus and the domain-independent factual knowledge base Wikipedia. Moreover, we examine an open-domain lexical ontology for suitability.
Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction
(2014)
We present a weakly-supervised induction method to assign semantic information to food items. We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not. The categorizations are induced by a graph-based algorithm applied on a large unlabeled domain-specific corpus. We show that the usage of a domain-specific corpus is vital. We do not only outperform a manually designed open-domain ontology but also prove the usefulness of these categorizations in relation extraction, outperforming state-of-the-art features that include syntactic information and Brown clustering.