Proposing a Customised Method for Extratextual Documentative Annotation on Written Text Corpus

  •  Niladri Sekhar Dash    
  •  Kesavan Vadakalur Elumalai    
  •  Mufleh Salem M. Alqahtani    
  •  May Abdulaziz Abumelha    


In this paper, we have made an attempt to portray a perceivable sketch of extratextual documentative annotation which, in the present frame of text annotation, is considered as one of the indispensable processes through which we can add representational information to the texts included in a written corpus. This becomes more important when a corpus is made with a large number of texts obtained from different genres and text types. To develop a workable frame for extratextual annotation, at each stage, we have broadly classified the existing processes of corpus annotation into two broad types. Moreover, we have tried to explain different layers that are embedded with extratextual annotation of texts as well as marked out the applications which can substantially enhance the accessibility of language data from a corpus for the works of text file management, information retrieval, lexical items extraction, and language processing. The techniques that we have proposed and described in this paper are unique in the sense that these are highly useful for expanding the utility of data of a written text corpus beyond the immediate horizons of language processing to the realms of theoretical, descriptive, and applied linguistics. In this paper, we have also argued that we should try to annotate all kinds of written text corpora so far developed in different natural languages at the extratextual level in a uniform manner so that the text samples stored in corpora can be uniformly used for various works of descriptive linguistics, theoretical linguistics, language technology, and applied linguistics including grammar writing, dictionary compilation, and language teaching. The annotation scheme proposed here is applied on a sample Bangla text corpus and we have noted that the accessibility of data and information from this kind of corpus is far easier than that of an un-annotated raw corpus.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1923-869X
  • ISSN(Online): 1923-8703
  • Started: 2011
  • Frequency: bimonthly

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