Best Practices for Speech Corpora in Linguistic Research Workshop ...

POS description example transliteration
PTK particle, unspecific Ja/PTK kommst Du denn auch ? yes come you then too ?
PTKFILL filler Ich äh/PTKFILL ich komme auch . I er I come too .
PTKREZ backchannel signal A: Ich komme auch . B: Hm-hm/PTKREZ . A: I come too . B: Uh-huh .
PTKONO onomatopoeia Das Lied ging so lalala/PTKONO . The song went so lalala .
PTKQU question particle Du kommst auch . Ne/PTKQU ? You come too . No ?
XYB unfinished word Ich ko/XYB # I ko #
UI uninterpretable (unverständlich)/UI # (uninterpretable) #
$# unfinished utterance Ich ko #/$# I ko #
Table 1: Additional POS tags in KidKo (the # is used to mark incomplete utterances or utterances not interpretable due to
low quality of the audio)
part-of-speech tagset for German and was also used (with
minor variations) to annotate POS tags in the TIGER treebank
and in the Verbmobil corpus.
The original STTS provides a set of 54 different tags.
Our extended version comprises additional tags for filled
pauses, question particles, backchannel signals, onomatopoeia,
unspecific particles, breaks, uninterpretable material,
and a new punctuation tag to mark unfinished sentences
(Table 1). This allows us to do a more fine-grained
analysis than the one in the Verbmobil corpus, which uses
the tags from the original STTS only. 6 Our part-of-speech
annotation of interjections, discourse markers and fillers is
also more fine-grained than the one in the Switchboard corpus
which combines them all into one tag (INTJ).
In addition, we propose including an extra layer of annotation
which maps our language-specific POS tagset to a
coarse-grained, universal POS tagset (Petrov et al., 2011)
which already provides a mapping for 25 different treebank
tagsets for 22 languages. This will enable a comparison
(admittedly on a very coarse-grained level) of the numbers
for part-of-speech annotation across different corpora.
5.2. Syntactic Annotation
Our syntactic annotation is adapted to the one in the TIGER
treebank (Brants et al., 2002), which is characterised by its
flat tree structure. Like the Verbmobil corpus, TIGER encodes
syntactic categories as well as grammatical functions
in the tree. TIGER uses a set of 25 syntactic category labels
and distinguishes 44 different grammatical functions.
Unlike in Verbmobil, non-local dependencies are expressed
through crossing branches. TIGER neither annotates unary
nodes, nor does it provide annotations for topological
fields. 7
In the remainder of this section we will go into the problem
of segmentation and will describe the syntactic representation
of disfluencies in the KidKo corpus.
5.3. Unit of analysis
Many proposals have been made in theoretical linguistics
for defining the most adequate unit of analysis for spoken
6 Our annotation is not as fine-grained as the one in the Christine
corpus which, for example, distinguishes pauses filled with
nasally produced noises (erm) from vocally filled pauses (er).
7 As stated earlier, we plan to annotate topological fields in the
corpus. This information will be added on a separate annotation
layer instead of including it in the phrase structure tree.
32
language, in analogy to the sentence concept for standard
written text (for an overview see (Crookes, 1990; Foster
et al., 2000)). The ideas of what to use as a basis for linguistic
analysis differ considerably, depending on the theoretical
background of the respective researchers. Although
the issue has been discussed for a long time, there is no
general agreement as to what should be used, and even different
definitions of the same individual unit show substantial
variation. In a thorough review of literature on spoken
language, Foster et al. (2000) looked at more than 80 studies
published in four leading journals on applied linguistics
and second language acquisition, and found that identical
units were either not defined in the same way or not defined
at all, or that the instructions given in the article were not
sufficient to handle real-world, messy spoken data. This
trend is alarming, as the choice of unit will have a significant
impact on the results of the analysis, and also on the
comparability of the results to other studies.
Our main ideas for defining a proper unit of analysis are
as follows. First, we want to maintain interoperability
and comparability with corpora of written language on
sentence-like utterances in the data. Second, we want to
capture different types of non-sentential units which are frequent
in spoken language and which are not, in general,
fragments, even when not following the rules of a standard
grammar for written language. Third, the guidelines on
what should be annotated as a unit of utterance should be
based on a theoretical basis suitable for describing spoken
language, and should enable the annotators to apply them
in a consistent way. We thus base our unit of analysis on
structural properties of the utterance and, if not sufficient,
also include functional aspects of the utterance. The latter
results in what we call the principle of the smallest possible
unit, meaning that when in doubt whether to merge lexical
material into one or more units we consider the speech
act type and discourse function of the segments. 8 Example
(2) illustrates this by showing an utterance including an imperative
(Speak German!) and a check question (Okay?).
It would be perfectly possible to include both in the same
unit, separated by a comma. However, as both reflect different
speech acts, we choose to annotate them as separate
8 Our classification of speech act types and discourse functional
units is inspired by work on shallow discourse-function annotation
(Jurafsky et al., 1997) and on non-sentential units in dialogue
(Fernandez and Ginzburg, 2002; Fernández et al., 2007).