Circularity effects in corpus studies – why annotations sometimes go round in circles

Consten, Manfred; Loll, Annegret

doi:10.1016/j.langsci.2012.04.010

Cited by 5 publications

(2 citation statements)

References 5 publications

(5 reference statements)

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“…The annotated data gained in this way can be used as (a part of) a training corpus for large-scale ML methods for automatic text processing (Ahmed et al, 2019), methods which can be regarded a standard in bioinformatic contexts by now (Blaschke et al, 2002). The development of an annotation scheme has to be put to test and should be regimented by annotation guidelines, since annotation is a data generating rather than a data documenting process (Consten & Loll, 2012). Accordingly, in BIOfid, annotation guidelines are collected as part of an annotation manual (Lücking et al, 2020).…”

Section: Developing the Biofid Annotation Schemementioning

confidence: 99%

Multiple annotation for biodiversity: developing an annotation framework among biology, linguistics and text technology

Lücking

Driller²,

Stoeckel

et al. 2021

Lang Resources & Evaluation

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Biodiversity information is contained in countless digitized and unprocessed scholarly texts. Although automated extraction of these data has been gaining momentum for years, there are still innumerable text sources that are poorly accessible and require a more advanced range of methods to extract relevant information. To improve the access to semantic biodiversity information, we have launched the BIOfid project (www.biofid.de) and have developed a portal to access the semantics of German language biodiversity texts, mainly from the 19th and 20th century. However, to make such a portal work, a couple of methods had to be developed or adapted first. In particular, text-technological information extraction methods were needed, which extract the required information from the texts. Such methods draw on machine learning techniques, which in turn are trained by learning data. To this end, among others, we gathered the bio text corpus, which is a cooperatively built resource, developed by biologists, text technologists, and linguists. A special feature of bio is its multiple annotation approach, which takes into account both general and biology-specific classifications, and by this means goes beyond previous, typically taxon- or ontology-driven proper name detection. We describe the design decisions and the genuine Annotation Hub Framework underlying the bio annotations and present agreement results. The tools used to create the annotations are introduced, and the use of the data in the semantic portal is described. Finally, some general lessons, in particular with multiple annotation projects, are drawn.

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Section: Developing the Biofid Annotation Schemementioning

confidence: 99%

Multiple annotation for biodiversity: developing an annotation framework among biology, linguistics and text technology

Lücking

Driller²,

Stoeckel

et al. 2021

Lang Resources & Evaluation

View full text Add to dashboard Cite

show abstract

“…as [A_A1 V_A2] with an underscore separating the two analytical alternatives of each element), and the CLU is categorised as indefinite until such a time when further annotation of other tiers may (or may not) help to disambiguate the analysis, at a structural level. It is difficult (if not impossible) to differentiate between ambiguity that arises from the acts of interpretation required of annotators as they identify and tag corpus data at this structural level, and ambiguity that may have been perceived and experienced by interactants in the discourse event as it occurred in real time (Consten & Loll 2012). For now, it would be true to say that these indefinite CLUs appear to lack clearly defined structure at this level with respect to the categories used in our initial analysis.…”

Section: Identifying and Annotating Core Elements Of Clause-like Unitsmentioning

confidence: 99%

Points, Depictions, Gestures and Enactment: Partly Lexical and Non-Lexical Signs as Core Elements of Single Clause-Like Units in Auslan (Australian Sign Language)

Hodge

Johnston

2014

Australian Journal of Linguistics

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Pointing signs, depicting signs and manual gestures are all used for meaningful expression in Auslan, as are full or partial body enactments to demonstrate action or dialogue (Johnston 2012; Ferrara & Johnston 2013). This article outlines a corpus-driven approach to identifying clause-like units in a native signed language and investigates the use of pointing signs, depicting signs, gestures and enactments to express core elements of possible clause-like units in Auslan narratives. We explore the frequency and distribution of the core argument and predicate elements of single clause-like units that were identified in elicited retellings of an Aesop's fable which have been archived in the Auslan Corpus. Core elements of these units are described according to sign type, the order in which they appear, and handedness (articulation with the strong or weak hand). We find that one third of core elements of the single clause-like units in these Auslan narratives are expressed via pointing signs, depicting signs, gestures and enactments, in various orders. This study uses empirical corpus-based data to contribute insights into the use of composite utterances in a signed language and therefore on the way meaning is negotiated between interactants.

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The fabulous engine: strengths and flaws of psycholinguistic experiments

Rákosi¹

2012

Language Sciences

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Circularity effects in corpus studies – why annotations sometimes go round in circles

Cited by 5 publications

References 5 publications

Multiple annotation for biodiversity: developing an annotation framework among biology, linguistics and text technology

Multiple annotation for biodiversity: developing an annotation framework among biology, linguistics and text technology

Points, Depictions, Gestures and Enactment: Partly Lexical and Non-Lexical Signs as Core Elements of Single Clause-Like Units in Auslan (Australian Sign Language)

The fabulous engine: strengths and flaws of psycholinguistic experiments

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