U ovoj sekciji biće objavljivani radovi sa konferencija i iz naučnih časopisa.

• V.Batanović et al., "Open Resources and Technologies for Serbian Language Processing"

  V.Batanović, N.Ljubešić, T. Samardžić, M. Miličević Petrović, "Open Resources and Technologies for Serbian Language Processing", PSSOH conference, Belgrade, Oct. 2020
  Link: https://zenodo.org/record/4113230#.X6GcaohKiUk
  Apstrakt: Otvorenost jezičkih resursa i alata je od velike važnosti za povećanje kvaliteta i brzine razvoja tehnologija za računarsku obradu prirodnih jezika. U ovom radu predstavljeni su otvoreni resursi za obradu srpskog jezika. Opisani su ručno anotirani korpusi, kao i širi spektar alata i računarskih modela, uključujući i veb servis koji omogućava njihovo jednostavno korišćenje.

• V.Batanović et al., "A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts"

  V. Batanović, M.Cvetanović, B.Nikolić, "A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts", PLoS ONE 15(11): e0242050. https://doi.org/10.1371/journal.pone.0242050
  Link: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242050
  Apstrakt:
  Choosing a comprehensive and cost-effective way of articulating and annotating the sentiment of a text is not a trivial task, particularly when dealing with short texts, in which sentiment can be expressed through a wide variety of linguistic and rhetorical phenomena. This problem is especially conspicuous in resource-limited settings and languages, where design options are restricted either in terms of manpower and financial means required to produce appropriate sentiment analysis resources, or in terms of available language tools, or both. In this paper, we present a versatile approach to addressing this issue, based on multiple interpretations of sentiment labels that encode information regarding the polarity, subjectivity, and ambiguity of a text, as well as the presence of sarcasm or a mixture of sentiments. We demonstrate its use on Serbian, a resource-limited language, via the creation of a main sentiment analysis dataset focused on movie comments, and two smaller datasets belonging to the movie and book domains. In addition to measuring the quality of the annotation process, we propose a novel metric to validate its cost-effectiveness. Finally, the practicality of our approach is further validated by training, evaluating, and determining the optimal configurations of several different kinds of machine-learning models on a range of sentiment classification tasks using the produced dataset.