UD for G erman Poetry (2024)

Abstract: This article deals with the syntactic analysis of German-language poetry from different centuries. We use Universal Dependencies (UD) as our syntactic framework. We discuss particular challenges of the poems in terms of tokenization, sentence boundary recognition and special syntactic constructions. Our annotated corpus currently consists of 20 poems with a total of 2,162 tokens, which originate from the PoeTree.de corpus. We present some statistics on our annotations and also evaluate the automatic UD annotation from PoeTree.de using our annotations.

## introduction for many years, poetry played a rather subordinate role in the field of natural language processing. with the emergence of the field of digital humanities, the interest in computational methods for philological questions has increased considerably. it is often assumed that the particular repetitive structures and patterns of poems serve, among other things, to support memorization. these structures prominently include meter and the rhyme scheme. many computational approaches to the analysis of poetry have focused on these features, such as the work by @xcite ; @xcite ; @xcite , who automatically analyze phonological features like meter and rhyme in english, german and french poetry (also see the overview in de @xcite . on a more abstract level, syntactic patterns can also support memorization, e.g. by using the same syntactic structure several times. @xcite investigate and compare such constructions in poems of two chinese poets under the name 'parallel couplets', which refers to semantic or syntactic correspondences between two lines. another syntactic phenomenon that is specific to poems is unusual word order, which often results from metrical constraints. a special case is the enjambment, in which the elements of a syntactic phrase are separated by spreading them over two lines, whereby special retarding effects can be achieved. enjambment has been examined by ruiz @xcite and @xcite . syntactic annotations of poems are also interesting for other reasons. for instance, dehass (2024) uses them to compare paratactic vs. hypotactic style in latin colloquial texts, poetry, and prose. syntax annotations can in general facilitate access to the content of a text and, e.g., allow for easy retrieval of (syntactically and semantically) related units such as entities and their properties, or events and their participants, as suggested by @xcite for literary texts. for a long time, such studies could only be carried out on a rather small amount of data that has been manually annotated by experts. examples of such manually-created poetry treebanks are the york-helsinki parsed corpus of old english poetry (ycoep) foot_0 @xcite , the ancient greek and latin dependency treebank (agldt) foot_1 @xcite , which comprises a subset of poetic texts, and the treebank of classical chinese poems @xcite . only recently has there been work on the automatic syntax analysis of poems and the first poetry treebanks have been generated automatically, such as the diachronic treebank of spanish sonnets @xcite , the treebank of classical arabic poetry @xcite , or the poetree treebank with poems in ten different languages @xcite . all three treebanks were automatically annotated by generic parsers that were not specifically trained for poems because no training data was available (see section 4.2 for evaluation results). our work aims to contribute to the creation of training and evaluation data for the syntactic analysis of poetry. our paper deals with the analysis of german-language poems from different centuries. we use the framework of universal dependencies (ud, de marneffe et al., 2021) and discuss some difficulties for annotation that arise due to the peculiarities of poems. we manually annotate a pilot corpus of 20 poems with 2,162 tokens, following the guidelines proposed by @xcite , who define a set of customized labels for german. our data comes from the poetree corpus, that has been automatically annotated with ud trees by udpipe @xcite . we evaluate the ud trees by means of our manually created annotations. for the evaluation, we map the customized labels back to ud labels. the main contributions of this paper are: ## data the data for our study comes from the poetree corpus, which consists of more than 330,000 poems with 89,000,000 tokens from 10 european languages @xcite . 4 all poems have been annotated automatically with ud-style dependencies using udpipe 2.0 @xcite . however, only the annotations of the czechlanguage subcorpus have already been evaluated @xcite (see section 4.2). the german-language sub-corpus of the poetree corpus, called poetree.de @xcite consists of 74,000 poems. the automatic annotations comprise lemma, inflection features, universal part-ofspeech (pos) tags, language-specific pos accord3 the corpus is available at https:// gitlab.ruhr-uni-bochum.de/vamos-cl/ ud-for-german-poetry. ## poetry-specific issues in this section, we discuss selected special features of poetry that pose a challenge for automatic language processing. the focus is on germanlanguage poetry; many of the challenges, however, also arise for poetry in other languages. ## results in this section, we present selected statistics from our annotations. in the second part, we evaluate the automatic annotations of poetree.de. ## conclusion we presented an annotation study of germanlanguage poetry of different centuries with udstyle syntactic relations. we showed that currently, the automatic analysis of poetry still seems problematic. at all levels -tokenization, sentence boundary determination, tagging, parsing -poems exhibit special properties that lead to faulty analyses. in this paper, however, only parsing was evaluated quantitatively. one could argue that some or even many of the challenges discussed also apply to other nonstandard language data, e.g. word repetition, anacoluthon and elision occur in spoken language, or deviations from standard spelling is typical of social media data. however, poets use these phenomena intentionally and deliberately in their poems, whereas in other data they often occur due to time pressure or attention deficits. a major problem is that incorrectly determined sentence boundaries tear apart dependency-related phrases. in difficult passages, the correct syntactic relationships often only become clear on closer 16 f1 is calculated as follows: with a1 as the manual annotation and a2 as the system annotation, and l1, l2 as the labels annotated by the human annotator and the system, respectively. possible values are between 0 and 1, where 1 means perfect agreement if @xmath0, and 0 means perfect disagreement if l1 ̸ = l2.