publication

@inproceedings{Luecking:Mehler:Henlein:2024-classifier,
  title     = {The Linguistic Interpretation of Non-emblematic Gestures Must
               be agreed in Dialogue: Combining Perceptual Classifiers and Grounding/Clarification
               Mechanisms},
  author    = {Lücking, Andy and Mehler, Alexander and Henlein, Alexander},
  year      = {2024},
  booktitle = {Proceedings of the 28th Workshop on The Semantics and Pragmatics of Dialogue},
  series    = {SemDial'24 -- TrentoLogue},
  location  = {Università di Trento, Palazzo Piomarta, Rovereto}
}

@inproceedings{Ginzburg:Eliasmith:Luecking:2024-swann,
  title     = {Swann's name: {Towards} a Dialogical Brain Semantics},
  author    = {Ginzburg, Jonathan and Eliasmith, Chris and Lücking, Andy},
  year      = {2024},
  booktitle = {Proceedings of the 28th Workshop on The Semantics and Pragmatics of Dialogue},
  series    = {SemDial'24 -- TrentoLogue},
  location  = {Università di Trento, Palazzo Piomarta, Rovereto}
}

@inproceedings{Boenisch:Mehler:2024,
  title     = {Finding Needles in Emb(a)dding Haystacks: Legal Document Retrieval
               via Bagging and SVR Ensembles},
  author    = {B\"{o}nisch, Kevin and Mehler, Alexander},
  year      = {2024},
  booktitle = {Proceedings of the 2nd Legal Information Retrieval meets Artificial
               Intelligence Workshop LIRAI 2024},
  location  = {Poznan, Poland},
  publisher = {CEUR-WS.org},
  address   = {Aachen, Germany},
  series    = {CEUR Workshop Proceedings},
  note      = {accepted},
  abstract  = {We introduce a retrieval approach leveraging Support Vector Regression
               (SVR) ensembles, bootstrap aggregation (bagging), and embedding
               spaces on the German Dataset for Legal Information Retrieval (GerDaLIR).
               By conceptualizing the retrieval task in terms of multiple binary
               needle-in-a-haystack subtasks, we show improved recall over the
               baselines (0.849 > 0.803 | 0.829) using our voting ensemble, suggesting
               promising initial results, without training or fine-tuning any
               deep learning models. Our approach holds potential for further
               enhancement, particularly through refining the encoding models
               and optimizing hyperparameters.},
  keywords  = {legal information retrieval, support vector regression, word embeddings, bagging ensemble}
}