Since dictionaries are mostly based on written language data, creating a dictionary of spoken language requires new types of lexicographic descriptions and an elaborate microstructure. When analyzing spoken language material, a remarkable part of lexicographic work consists in analyzing interactional contributions of one or more speakers, and focusing on the lexicalized units used for organizing conversation as well as expressing one’s attitude and reacting to other speakers’ turns. In the project Lexicon of Spoken German (LeGeDe: Lexikon des gesprochenen Deutsch), we are creating a prototype for a lexical resource with the aim of describing the common practices and preferences in spoken German by exploring lexicographic representations for interjections, multiword expressions, such as passt schon and mal gucken, and delexicalized verb forms. None of these have been extensively elaborated in German lexicographic tradition.
Representing pragmatic information
One of the biggest challenges of describing spoken language data is determining the function of lexical units in interactional setting. For instance, the expression oh Gott (en: oh my God) can have multiple functions in an interaction: it can express surprise, astonishment, horror, indignation, annoyance, pain, excitement, etc. and it can be used, for example, as a mean of confirming or agreeing with the interlocutor’s position.
As an attempt to describe these functions in a TEI representation within the Lexical Data Masterclass in Berlin in December 2017, I defined an <usg> element with a type “commFunc” for every information regarding word usage or communicative function that was described in my entry drafts. For sake of example, I categorized talk organization, speaker alignment and speaker attitude as some of the possible subtypes of communicative functions. Given that the TEI-attribute @subtype is not yet allowed in this specification, I used the @value attribute to specify the subcategories of communicative functions.
<entry> <form type="lemma"> <orth>Gott</orth> </form> <sense n="2"> <gramGrp> <pos>NG</pos> </gramGrp> <usg type="commFunc" value="speakerAtt">Aufregung</usg> <usg type="commFunc" value="speakerAtt">Überraschung</usg> <usg type="commFunc" value="speakerAtt">Entsetzen</usg> <usg type="commFunc" value="speakerAtt">Erstaunen</usg> <usg type="commFunc" value="speakerAtt">Ungeduld</usg> <usg type="commFunc" value="speakerAlign">Kooperation</usg> <usg type="commFunc" value="talkOrg">Response</usg> <usg type="commFunc" value="talkOrg">Diskursmarker</usg> </sense> </entry>
Since each <usg> element contains only one item, this structure would allow querying the dictionary according to the onomasiological features. For future references, the conventions in ISO 24617-2 Dialogue Acts (See Bunt et al., 2010) may be a good basis for further work on systematizing communicative functions.
An immediate issue that arises in this type of representations is the one of inheritance and cross-reference. On the one hand, one must consider representing the uses of multiword expressions that are related to a particular sense and do not inherit all of its functions. On the other hand, it is also inevitable having to represent those, which inherit all of the communicative functions defined for the parent node and which can have other functions as well. One way of resolving the issue of inheritance would be to specify whether the features in the dictionary are to be interpreted as cumulative, overwriting or local (Ide, Kilgarriff, Romary 2000).
Although the questions of inheritance and representation of communicative functions become apparent in an attempt of XML dictionary modeling, they must be discussed in the conceptual part of the lexicographic work. The like can be said in the case of descriptions of multiword expressions and lexicographic descriptions of conversions (schauen, verb > schau, interjection). In TEI terms, the latter can be represented as related entries as well as entries in their own right, and deciding how to model them in the starting phase of a dictionary creation is indispensable for a sustainable development of that dictionary.
Integrating frequency information
Working with corpora of spoken language, which are annotated on multiple levels, allows the use of lexicographic descriptions of metadata information, such as pronunciation, geographical information, gender, age, etc. Annotating and sorting the dictionary entries with these features could be the next development for digital born dictionaries of spoken language.
Corpora of spoken German are still relatively small for allowing a fine-grained description of particular lexical units (for instance, FOLK- the largest corpus of spoken German in interaction contains less than 2 million tokens). However, since interjections are highly frequent in spoken language, a quantitative lexicographic description of their frequency distribution would be something to consider.
With the exception of quantitative description of metadata in dictionary entries, sorting the entries according to the most frequent senses is an issue that can be accessed with the TEI tag <f>, that has also been used for representing corpus frequencies in the dictionaries (Mörth et al., 2015). Other than storing absolute frequencies or ranks regarding corpus counts, the <f> tag can be used to represent the rank of the senses or uses in the entries.
<fs type="corpFreq">
<f name="rank"><numeric value="1"/></f>
</fs>
<fs type="corpFreq">
<f name="rank"><numeric value="2"/></f>
</fs>
Thanks to the extended pos-tags developed for spoken German and integrated into the FOLK corpus (Westpfahl 2014), disambiguating between certain senses as well as checking their corpus frequency has been made possible. For instance, Gott is tagged as either noun (NN) or interjection (non-grammatical-element: NG) and the frequency of Gott as interjection in FOLK surpasses in great measure the frequency of Gott as a noun. Hence, we can set Gott (NG) as the most frequent sense (rank=1). However, for many lexical units having same parts of speech but different senses, the ranking can not be automatized as easily, and it would depend in great measure on the sample size considered in the lexicographic analysis.