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The present thesis introduces KoralQuery, a protocol for the generic representation of queries to linguistic corpora. KoralQuery defines a set of types and operations which serve as abstract representations of linguistic entities and configurations. By combining these types and operations in a nested structure, the protocol may express linguistic structures of arbitrary complexity. It achieves a high degree of neutrality with regard to linguistic theory, as it provides flexible structures that allow for the setting of certain parameters to access several complementing and concurrent sources and layers of annotation on the same textual data. JSON-LD is used as a serialisation format for KoralQuery, which allows for the well-defined and normalised exchange of linguistic queries between query engines to promote their interoperability. The automatic translation of queries issued in any of three supported query languages to such KoralQuery serialisations is the second main contribution of this thesis. By employing the introduced translation module, query engines may also work independently of particular query languages, as their backend technology may rely entirely on the abstract KoralQuery representations of the queries. Thus, query engines may provide support for several query languages at once without any additional overhead. The original idea of a general format for the representation of linguistic queries comes from an initiative called Corpus Query Lingua Franca (CQLF), whose theoretic backbone and practical considerations are outlined in the first part of this thesis. This part also includes a brief survey of three typologically different corpus query languages, thus demonstrating their wide variety of features and defining the minimal target space of linguistic types and operations to be covered by KoralQuery.
The task-oriented and format-driven development of corpus query systems has led to the creation of numerous corpus query languages (QLs) that vary strongly in expressiveness and syntax. This is a severe impediment for the interoperability of corpus analysis systems, which lack a common protocol. In this paper, we present KoralQuery, a JSON-LD based general corpus query protocol, aiming to be independent of particular QLs, tasks and corpus formats. In addition to describing the system of types and operations that Koral- Query is built on, we exemplify the representation of corpus queries in the serialized format and illustrate use cases in the KorAP project.
The availability of electronic corpora of historical stages of languages has been wel- comed as possibly attenuating the inherent problem of diachronic linguistics, i.e. that we only have access to what has chanced to come down to us - the problem which was memorably named by Labov (1992) as one of “Bad Data”. However, such corpora can only give us access to an increased amount ot historical material and this can essentially still only be a partial and possibly distorted picture of the actual language at a particular period of history. Corpora can be improved by taking a more representative sample of extant texts if these are available (as they are in significant number for periods after the invention of printing). But, as examples from the recently compiled GerManC corpus of seventeenth and eighteenth century German show, the evidence from such corpora can still fail to yield definitive answers to our questions about earlier stages of a language. The data still require expert interpretation, and it is important to be realistic about what can legitimately be expected from an electronic historical corpus.
Learning from Errors. Systematic Analysis of Complex Writing Errors for Improving Writing Technology
(2015)
In this paper, we describe ongoing research on writing errors with the ultimate goal to develop error-preventing editing functions in word-processors. Drawing from the state-of-the-art research in errors carried out in various fields, we propose the application of a general concept for action-slips as introduced by Norman. We demonstrate the feasibility of this approach by using a large corpus of writing errors in published texts. The concept of slips considers both the process and the product: some failure in a procedure results in an error in the product, i.e., is visible in the written text. In order to develop preventing functions, we need to determine causes of such visible errors.
Feedback utterances are among the most frequent in dialogue. Feedback is also a crucial aspect of linguistic theories that take social interaction, involving language, into account. This paper introduces the corpora and datasets of a project scrutinizing this kind of feedback utterances in French. We present the genesis of the corpora (for a total of about 16 hours of transcribed and phone force-aligned speech) involved in the project. We introduce the resulting datasets and discuss how they are being used in on-going work with focus on the form-function relationship of conversational feedback. All the corpora created and the datasets produced in the framework of this project will be made available for research purposes.
This paper discusses computational linguistic methods for the semi-automatic analysis of modality interdependencies (the combination of complex resources such as speaking, writing, and visualizing; MID) in professional crosssituational interaction settings. The overall purpose of the approach is to develop models, methods, and a framework for the description and analysis of MID forms and functions. The paper describes work in progress—the development of an annotation framework that allows annotating different data and file formats at various levels, to relate annotation levels and entries independently of the given file format, and to visualize patterns.
We present a quantitative approach to disambiguating flat morphological analyses and producing more deeply structured analyses. Based on existing morphological segmentations, possible combinations of resulting word trees for the next level are filtered first by criteria of linguistic plausibility and then by weighting procedures based on the geometric mean. The frequencies for weighting are derived from three different sources (counts of morphs in a lexicon, counts of largest constituents in a lexicon, counts of token frequencies in a corpus) and can be used either to find the best analysis on the level of morphs or on the next higher constituent level. The evaluation shows that for this task corpus-based frequency counts are slightly superior to counts of lexical data.
In this article, we explore the feasibility of extracting suitable and unsuitable food items for particular health conditions from natural language text. We refer to this task as conditional healthiness classification. For that purpose, we annotate a corpus extracted from forum entries of a food-related website. We identify different relation types that hold between food items and health conditions going beyond a binary distinction of suitability and unsuitability and devise various supervised classifiers using different types of features. We examine the impact of different task-specific resources, such as a healthiness lexicon that lists the healthiness status of a food item and a sentiment lexicon. Moreover, we also consider task-specific linguistic features that disambiguate a context in which mentions of a food item and a health condition co-occur and compare them with standard features using bag of words, part-of-speech information and syntactic parses. We also investigate in how far individual food items and health conditions correlate with specific relation types and try to harness this information for classification.
We examine the combination of pattern-based and distributional similarity for the induction of semantic categories. Pattern-based methods are precise and sparse while distributional methods have a higher recall. Given these particular properties we use the prediction of distributional methods as a back-off to pattern-based similarity. Since our pattern-based approach is embedded into a semi-supervised graph clustering algorithm, we also examine how distributional information is best added to that classifier. Our experiments are carried out on 5 different food categorization tasks.