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Ancient Chinese poetry is constituted by structured language that deviates from ordinary language usage; its poetic genres impose unique combinatory constraints on linguistic elements. How does the constrained poetic structure facilitate speech segmentation when common linguistic and statistical cues are unreliable to listeners in poems? We generated artificial Jueju, which arguably has the most constrained structure in ancient Chinese poetry, and presented each poem twice as an isochronous sequence of syllables to native Mandarin speakers while conducting magnetoencephalography (MEG) recording. We found that listeners deployed their prior knowledge of Jueju to build the line structure and to establish the conceptual flow of Jueju. Unprecedentedly, we found a phase precession phenomenon indicating predictive processes of speech segmentation—the neural phase advanced faster after listeners acquired knowledge of incoming speech. The statistical co-occurrence of monosyllabic words in Jueju negatively correlated with speech segmentation, which provides an alternative perspective on how statistical cues facilitate speech segmentation. Our findings suggest that constrained poetic structures serve as a temporal map for listeners to group speech contents and to predict incoming speech signals. Listeners can parse speech streams by using not only grammatical and statistical cues but also their prior knowledge of the form of language.
In der Computerlinguistik ist eine kaskadische Prozessierung von Texten üblich. Dabei werden diese zuerst segmentiert (tokenisiert), d.h. Tokens und ggf. Satzgrenzen werden erkannt. Dabei entsteht meist eine Liste bzw. eine einspaltige Tabelle, die sukzessive durch weitere Prozessierungschritte um zusätzliche Spalten – also positionale Annotationen wie z.B. Wortarten und Lemmata für die Tokens in der ersten Spalte – ergänzt wird. Bei der Tokenisierung werden alle Spatien (Leerzeichen) gelöscht. Schon immer problematisch waren dabei Interpunktionszeichen, da diese äußerst ambig sein können, aber auch mehrteilige Namen, die Leerzeichen enthalten und eigentlich zusammengehören. Dieser Beitrag fokussiert auf den Apostroph, der in vielfältiger Weise in den Texten Udo Lindenbergs eingesetzt wird sowie auf mehrteilige Namen, die wir als Tokens erhalten möchten. Wir nutzen dafür das komplette Lindenberg-Archiv des song-korpus.de-Repositoriums, kategorisieren die auftretenden Phänomene, erstellen einen Goldstandard und entwickeln ein teils regel-, teils auf maschinellem Lernen basierendes Segmentierungswerkzeug, das insbesondere die auftretenden Apostrophe, aber auch -lexikonbasiert - mehrteilige Namen nach unseren Vorstellungen erkennt und tokenisiert. Im Anschluss trainieren wir den RNN-Tagger (Schmid, 2019) und zeigen auf, dass ein spezifisch für diese Texte angepasstes Training zu Genauigkeiten ≥ 96% führt. Dabei entsteht nicht nur ein Goldstandard des annotierten Korpus, das dem Songkorpus-Repositorium zur Verfügung gestellt wird, sondern auch eine angepasste Version des RNN-Taggers (verfügbar auf github), die für ähnliche Texte verwendet werden kann.
Automatic division of spoken language transcripts into sentence-like units is a challenging problem, caused by disfluencies, ungrammatical structures and the lack of punctuation. We present experiments on dividing up German spoken dialogues where we investigate the impact of task setup and data representation, encoding of context information as well as different model architectures for this task.
A syntax-based scheme for the annotation and segmentation of German spoken language interactions
(2018)
Unlike corpora of written language where segmentation can mainly be derived from orthographic punctuation marks, the basis for segmenting spoken language corpora is not predetermined by the primary data, but rather has to be established by the corpus compilers. This impedes consistent querying and visualization of such data. Several ways of segmenting have been proposed,
some of which are based on syntax. In this study, we developed and evaluated annotation and segmentation guidelines in reference to the topological field model for German. We can show that these guidelines are used consistently across annotators. We also investigated the influence of various interactional settings with a rather simple measure, the word-count per segment and unit-type. We observed that the word count and the distribution of each unit type differ in varying interactional settings and that our developed segmentation and annotation guidelines are used consistently across annotators. In conclusion, our syntax-based segmentations reflect interactional properties that are intrinsic to the social interactions that participants are involved in. This can be used for further analysis of social interaction and opens the possibility for automatic segmentation of transcripts.
The Partitur Format at BAS
(1997)
Most spoken language resources are produced and disseminated together with symbolic information relating to the speech signal. These are for instance orthographic transcript labeling and segmentation on the phonologic phoneti prosodic phrasal level. Most of the known formats for these symbolic data are defined in a ‘closed form’ that is not fexible enough to allow simple and platform independent processing and easy extensions.
At the Bavarian Archive for Speech Signals (BAS) a new format has been developed and used over the last few years that shows some significant advantages over other existing formats. This paper describes the basic principles behind this format discusses briefly the advantages and gives detailed definitions of the description levels used so far.
We present a method to identify and document a phenomenon on which there is very little empirical data: German phrasal compounds occurring in the form of as a single token (without punctuation between their components). Relying on linguistic criteria, our approach implies to have an operational notion of compounds which can be systematically applied as well as (web) corpora which are large and diverse enough to contain rarely seen phenomena. The method is based on word segmentation and morphological analysis, it takes advantage of a data-driven learning process. Our results show that coarse-grained identification of phrasal compounds is best performed with empirical data, whereas fine-grained detection could be improved with a combination of rule-based and frequency-based word lists. Along with the characteristics of web texts, the orthographic realizations seem to be linked to the degree of expressivity.
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.