Refine
Year of publication
Document Type
- Part of a Book (8)
- Article (4)
- Conference Proceeding (2)
- Doctoral Thesis (1)
Has Fulltext
- yes (15)
Keywords
- Name (15) (remove)
Publicationstate
- Zweitveröffentlichung (10)
- Veröffentlichungsversion (3)
- Postprint (1)
Reviewstate
- (Verlags)-Lektorat (7)
- Peer-Review (6)
Publisher
- de Gruyter (3)
- European Language Resources Association (2)
- Fundacja Uniwersytetu im. Adama Mickiewicza (1)
- Groos (1)
- Narr (1)
- Olms Verlag (1)
- Rodopi (1)
- The Association for Computational Linguistics (1)
- Winter (1)
- de Gruyter Mouton (1)
Klassische Namen der Offline-Welt sind bei weitem umfangreicher erforscht als die eher kurzlebigen und auch noch sehr jungen Namen der digitalen Welt. Im vorliegenden Beitrag werden virtuelle Namen als eigene Namenklasse postuliert und unter Verweis auf bestehende Namentypologien verortet. Anschließend werden drei unterschiedliche Typen frei wählbarer virtueller Namen in Videospielen am Beispiel des populären Browserspiels ‚Forge of Empires‘ graphematisch und semantisch analysiert: Gilden-, Städte- und Benutzernamen. Hierfür werden drei Korpora mit je 100 Namen des jeweiligen Typs auf unterschiedliche Muster zunächst hinsichtlich Sprachwahl, Zeichenverwendung und graphematischen Besonderheiten untersucht. Anschließend erfolgt eine Untersuchung der den Namen zugrundeliegenden Benennungsmotive durch induktiv-explorative Kategorienbildung. Zwischen den untersuchten Namentypen kristallisiert sich in der Analyse ein funktionaler Unterschied heraus: Gildennamen priorisieren eine kommunikativ-phatische Funktion, wohingegen Benutzernamen primär Individualität ausdrücken. Städtenamen nehmen dabei eine Zwischenposition ein. Insgesamt fügen sich die verschiedenen Teilergebnisse in das Bild der bisherigen spärlichen Studien zur Namenwahl in Videospielen ein und rufen zugleich zur weiteren Erforschung auf.
We present a fine-grained NER annotations scheme with 30 labels and apply it to German data. Building on the OntoNotes 5.0 NER inventory, our scheme is adapted for a corpus of transcripts of biographic interviews by adding categories for AGE and LAN(guage) and also adding label classes for various numeric and temporal expressions. Applying the scheme to the spoken data as well as a collection of teaser tweets from newspaper sites, we can confirm its generality for both domains, also achieving good inter-annotator agreement. We also show empirically how our inventory relates to the well-established 4-category NER inventory by re-annotating a subset of the GermEval 2014 NER coarse-grained dataset with our fine label inventory. Finally, we use a BERT-based system to establish some baselines for NER tagging on our two new datasets. Global results in in-domain testing are quite high on the two datasets, near what was achieved for the coarse inventory on the CoNLLL2003 data. Cross-domain testing produces much lower results due to the severe domain differences.
Entity framing is the selection of aspects of an entity to promote a particular viewpoint towards that entity. We investigate entity framing of political figures through the use of names and titles in German online discourse, enhancing current research in entity framing through titling and naming that concentrates on English only. We collect tweets that mention prominent German politicians and annotate them for stance. We find that the formality of naming in these tweets correlates positively with their stance. This confirms sociolinguistic observations that naming and titling can have a status-indicating function and suggests that this function is dominant in German tweets mentioning political figures. We also find that this status-indicating function is much weaker in tweets from users that are politically left-leaning than in tweets by right leaning users. This is in line with observations from moral psychology that left-leaning and right-leaning users assign different importance to maintaining social hierarchies.
Naming and titling have been discussed in sociolinguistics as markers of status or solidarity. However, these functions have not been studied on a larger scale or for social media data. We collect a corpus of tweets mentioning presidents of six G20 countries by various naming forms. We show that naming variation relates to stance towards the president in a way that is suggestive of a framing effect mediated by respectfulness. This confirms sociolinguistic theory of naming and titling as markers of status.
While good results have been achieved for named entity recognition (NER) in supervised settings, it remains a problem that for low resource languages and less studied domains little or no labelled data is available. As NER is a crucial preprocessing step for many natural language processing tasks, finding a way to overcome this deficit in data remains of great interest. We propose a distant supervision approach to NER that is both language and domain independent where we automatically generate labelled training data using gazetteers that we previously extracted from Wikipedia. We test our approach on English, German and Estonian data sets and contribute further by introducing several successful methods to reduce the noise in the generated training data. The tested models beat baseline systems and our results show that distant supervision can be a promising approach for NER when no labelled data is available. For the English model we also show that the distant supervision model is better at generalizing within the same domain of news texts by comparing it against a supervised model on a different test set.