Korpuslinguistik
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In this paper, we present an overview of freely available web applications providing online access to spoken language corpora. We explore and discuss various solutions with which the corpus providers and corpus platform developers address the needs of researchers who are working with spoken language. The paper aims to contribute to the long-overdue exchange and discussion of methods and best practices in the design of online access to spoken language corpora.
In diesem Beitrag beschäftigen wir uns mit moralisierenden Sprachhandlungen, worunter wir diskursstrategische Verfahren verstehen, in denen die Beschreibung von Streitfragen und erforderlichen Handlungen mit moralischen Begriffen enggeführt werden. Auf moralische Werte verweisendes Vokabular (wie beispielsweise „Freiheit“, „Sicherheit“ oder „Glaubwürdigkeit“) wird dabei verwendet, um eine Forderung durchzusetzen, die auf diese Weise unhintergehbar erscheint und keiner weiteren Begründung oder Rechtfertigung bedarf. Im Fokus unserer Betrachtungen steht dementsprechend das aus pragma-linguistischer Sicht auffällige Phänomen einer spezifischen Redepraxis der Letztbegründung oder Unhintergehbarkeit, die wir als Pragmem auffassen und beschreiben. Hierfür skizzieren wir zunächst den in der linguistischen Pragmatik verorteten Zugang zu Praktiken der Moralisierung, betrachten sprachliche Formen des Moralisierens und deren kotextuellen und insbesondere pragma-syntaktischen Struktureinbettungen, um anschließend Hypothesen zu kontextuellen Wirkungsfunktionen aufzustellen. Darauf basierend leiten wir schließlich anhand von exemplarischen Korpusbelegen Strukturmuster des Moralisierens ab, die wir in dem Terminus „Pragmem“ verdichten und mittels qualitativer und quantitativer Analysen operationalisieren.
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.
We investigate the optional omission of the infinitival marker in a Swedish future tense construction. During the last two decades the frequency of omission has been rapidly increasing, and this process has received considerable attention in the literature. We test whether the knowledge which has been accumulated can yield accurate predictions of language variation and change. We extracted all occurrences of the construction from a very large collection of corpora. The dataset was automatically annotated with language-internal predictors which have previously been shown or hypothesized to affect the variation. We trained several models in order to make two kinds of predictions: whether the marker will be omitted in a specific utterance and how large the proportion of omissions will be for a given time period. For most of the approaches we tried, we were not able to achieve a better-than-baseline performance. The only exception was predicting the proportion of omissions using autoregressive integrated moving average models for one-step-ahead forecast, and in this case time was the only predictor that mattered. Our data suggest that most of the language-internal predictors do have some effect on the variation, but the effect is not strong enough to yield reliable predictions.
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.