Two publications accepted at IJCNLP-AACL

@inproceedings{Hammerla:et:al:2025a,
  author    = {Hammerla, Leon and Mehler, Alexander and Abrami, Giuseppe},
  title     = {Standardizing Heterogeneous Corpora with {DUUR}: A Dual Data-
               and Process-Oriented Approach to Enhancing NLP Pipeline Integration},
  editor    = {Inui, Kentaro and Sakti, Sakriani and Wang, Haofen and Wong, Derek F.
               and Bhattacharyya, Pushpak and Banerjee, Biplab and Ekbal, Asif and Chakraborty, Tanmoy
               and Singh, Dhirendra Pratap},
  booktitle = {Proceedings of the 14th International Joint Conference on Natural
               Language Processing and the 4th Conference of the Asia-Pacific
               Chapter of the Association for Computational Linguistics},
  month     = {dec},
  year      = {2025},
  address   = {Mumbai, India},
  publisher = {The Asian Federation of Natural Language Processing and The Association for Computational Linguistics},
  url       = {https://aclanthology.org/2025.findings-ijcnlp.87/},
  pages     = {1410--1425},
  isbn      = {979-8-89176-303-6},
  abstract  = {Despite their success, LLMs are too computationally expensive
               to replace task- or domain-specific NLP systems. However, the
               variety of corpus formats makes reusing these systems difficult.
               This underscores the importance of maintaining an interoperable
               NLP landscape. We address this challenge by pursuing two objectives:
               standardizing corpus formats and enabling massively parallel corpus
               processing. We present a unified conversion framework embedded
               in a massively parallel, microservice-based, programming language-independent
               NLP architecture designed for modularity and extensibility. It
               allows for the integration of external NLP conversion tools and
               supports the addition of new components that meet basic compatibility
               requirements. To evaluate our dual data- and process-oriented
               approach to standardization, we (1) benchmark its efficiency in
               terms of processing speed and memory usage, (2) demonstrate the
               benefits of standardized corpus formats for NLP downstream tasks,
               and (3) illustrate the advantages of incorporating custom formats
               into a corpus format ecosystem.},
  keywords  = {neglab,duui}
}

@inproceedings{Hammerla:et:al:2025b,
  author    = {Hammerla, Leon and Lücking, Andy and Reinert, Carolin and Mehler, Alexander},
  title     = {{D}-Neg: Syntax-Aware Graph Reasoning for Negation Detection},
  editor    = {Inui, Kentaro and Sakti, Sakriani and Wang, Haofen and Wong, Derek F.
               and Bhattacharyya, Pushpak and Banerjee, Biplab and Ekbal, Asif and Chakraborty, Tanmoy
               and Singh, Dhirendra Pratap},
  booktitle = {Proceedings of the 14th International Joint Conference on Natural
               Language Processing and the 4th Conference of the Asia-Pacific
               Chapter of the Association for Computational Linguistics},
  month     = {dec},
  year      = {2025},
  address   = {Mumbai, India},
  publisher = {The Asian Federation of Natural Language Processing and The Association for Computational Linguistics},
  url       = {https://aclanthology.org/2025.findings-ijcnlp.89/},
  pages     = {1432--1454},
  isbn      = {979-8-89176-303-6},
  abstract  = {Despite the communicative importance of negation, its detection
               remains challenging. Previous approaches perform poorly in out-of-domain
               scenarios, and progress outside of English has been slow due to
               a lack of resources and robust models. To address this gap, we
               present D-Neg: a syntax-aware graph reasoning model based on a
               transformer that incorporates syntactic embeddings by attention-gating.
               D-Neg uses graph attention to represent syntactic structures,
               emulating the effectiveness of rule-based dependency approaches
               for negation detection. We train D-Neg using 7 English resources
               and their translations into 10 languages, all aligned at the annotation
               level. We conduct an evaluation of all these datasets in in-domain
               and out-of-domain settings. Our work represents a significant
               advance in negation detection, enabling more effective cross-lingual
               research.},
  keywords  = {neglab}
}