Korpuslinguistik
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As the Web ought to be considered as a series of sources rather than as a source in itself, a problem facing corpus construction resides in meta-information and categorization. In addition, we need focused data to shed light on particular subfields of the digital public sphere. Blogs are relevant to that end, especially if the resulting web texts can be extracted along with metadata and made available in coherent and clearly describable collections.
Since 2013 representatives of several French and German CMC corpus projects have developed three customizations of the TEI-P5 standard for text encoding in order to adapt the encoding schema and models provided by the TEI to the structural peculiarities of CMC discourse. Based on the three schema versions, a 4th version has been created which takes into account the experiences from encoding our corpora and which is specifically designed for the submission of a feature request to the TEI council. On our poster we would present the structure of this schema and its relations (commonalities and differences) to the previous schemas.
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.
Introduction
(2019)
The present paper examines a variety of ways in which the Corpus of Contemporary Romanian Language (CoRoLa) can be used. A multitude of examples intends to highlight a wide range of interrogation possibilities that CoRoLa opens for different types of users. The querying of CoRoLa displayed here is supported by the KorAP frontend, through the querying language Poliqarp. Interrogations address annotation layers, such as the lexical, morphological and, in the near future, the syntactical layer, as well as the metadata. Other issues discussed are how to build a virtual corpus, how to deal with errors, how to find expressions and how to identify expressions.
Nearly all of the very large corpora of English are “static”, which allows a wide range of one-time, pre-processed data, such as collocates. The challenge comes with large “dynamic” corpora, which are updated regularly, and where preprocessing is much more difficult. This paper provides an overview of the NOW corpus (News on the Web), which is currently 8.2 billion words in size, and which grows by about 170 million words each month. We discuss the architecture of NOW, and provide many examples that show how data from NOW can (uniquely) be extracted to look at a wide range of ongoing changes in English.
The user interfaces for corpus analysis platforms must provide a high degree of accessibility for ordinary users and at the same time provide the possibility to answer complex research questions. In this paper, we present the design concepts behind the user interface of KorAP, a corpus analysis platform that has evolved into the main gateway to CoRoLa, the Reference Corpus of Contemporary Romanian Language. Based on established principles of user interface design, we show how KorAP addresses the challenge of providing a user-friendly interface for heterogeneous corpus data to a wide range of users with different research questions.
Distributional models of word use constitute an indispensable tool in corpus based lexicological research for discovering paradigmatic relations and syntagmatic patterns (Belica et al. 2010). Recently, word embeddings (Mikolov et al. 2013) have revived the field by allowing to construct and analyze distributional models on very large corpora. This is accomplished by reducing the very high dimensionality of word cooccurrence contexts, the size of the vocabulary, to few dimensions, such as 100-200. However, word use and meaning can vary widely along dimensions such as domain, register, and time, and word embeddings tend to represent only the most prevalent meaning. In this paper we thus construct domain specific word embeddings to allow for systematically analyzing variations in word use. Moreover, we also demonstrate how to reconstruct domain specific co-occurrence contexts from the dense word embeddings.