News

@article{Chen:et:al:2026,
  doi       = {10.1371/journal.pone.0346096},
  author    = {Chen, Yanran and Zhao, Wei and Breitbarth, Anne and Stoeckel, Manuel
               and Mehler, Alexander and Schlechtweg, Dominik and Eger, Steffen},
  journal   = {PLOS ONE},
  publisher = {Public Library of Science},
  title     = {Syntactic language change in English and German: Metrics, parsers,
               and convergences},
  year      = {2026},
  month     = {04},
  volume    = {21},
  url       = {https://doi.org/10.1371/journal.pone.0346096},
  pages     = {1-33},
  abstract  = {Syntactic language change has gained increasing attention in recent
               years. Previous computational work based on dependency relations
               has focused on diachronic trends in dependency distance, which
               measures the linear distance between dependent words, using dependency
               trees automatically predicted by a dependency parser (mostly the
               Stanford CoreNLP parser). In this work, we introduce a set of
               15 syntax metrics that extend the analysis beyond linear distance
               by incorporating both linear and tree graph properties of dependency
               trees, such as tree height and degree. Besides, we propose a multi-parser
               approach to reduce the impact of using specific parsers, thereby
               increasing the robustness of the detected language changes. Through
               a cross-lingual investigation of English and German in parliamentary
               debates from the last 160 years, using 6 different parsers (CoreNLP
               and five newer alternatives), we demonstrate that: (1) Relying
               on one single parser can be problematic, as the agreement on predicted
               trends can be low across parsers. (2) Our set of metrics can capture
               subtle patterns of syntactic changes. Our analysis shows that
               syntactic change over the time period inspected is largely similar
               between English and German, with only 2.2% of cases yielding opposite
               trends in these metrics. (3) We also show that changes in syntactic
               metrics seem to be more frequent at the tails of sentence length
               distributions and often move in opposite directions for short
               and long sentences. To our best knowledge, ours is the most comprehensive
               computational analysis of syntactic language change using modern
               NLP technology in recent corpora of English and German.},
  number    = {4}
}

@inproceedings{Missaoui:et:al:2026,
  title     = {SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis},
  author    = {Missaoui, Yahya and Kebede, Solomon and Marreddy, Mounika and Mehler, Alexander},
  booktitle = {Proceedings of the International Workshop on Semantic Evaluation (SemEval-2026)},
  year      = {2026},
  publisher = {Association for Computational Linguistics},
  note      = {accepted}
}

@inproceedings{Tratzsch:et:al2026,
  title     = {SemEval-2026 Task 11: Reducing Content Effects Using Layered Activation Steering},
  author    = {Tratzsch, Noah and Al-Raian, Asmaa and Marreddy, Mounika and Mehler, Alexander},
  booktitle = {Proceedings of the International Workshop on Semantic Evaluation (SemEval-2026)},
  year      = {2026},
  publisher = {Association for Computational Linguistics},
  note      = {accepted}
}

@inproceedings{Richer:et:al:2026,
  title     = {TTLab at SemEval-2026 Task 10: Transformer-based Approaches for
               Psycholinguistic Conspiracy Detection in Social Media Discourse},
  author    = {Richer, Samuel and Marreddy, Mounika and Mehler, Alexander},
  booktitle = {Proceedings of the International Workshop on Semantic Evaluation (SemEval-2026)},
  year      = {2026},
  publisher = {Association for Computational Linguistics},
  note      = {accepted}
}

@inproceedings{Abusaleh:et:al:2026:sarf,
  title     = {TTLab at AraSentEval: SARF (صرف) Sentiment Analysis via Root-based
               Fusion for Multi-Dialectal Arabic},
  author    = {Abusaleh, Ali and Verma, Bhuvanesh and Mehler, Alexander},
  booktitle = {Proceedings of the 7th Workshop on Open-Source Arabic Corpora
               and Processing Tools (OSACT7), co-located with the Language Resources
               and Evaluation Conference (LREC 2026)},
  eventdate = {May, 2026},
  location  = {Palma, Mallorca, Spain},
  year      = {2026},
  keywords  = {NLP, Sentiment Analysis, Arabic analysis, new-data-spaces, circlet, satek},
  abstract  = {Arabic sentiment analysis is challenged by morphological complexity
               and lexical variation across Arabic dialects, compounded by subjectivity
               in how speakers and writers express sentiment. In this paper,
               we present our submission for the AraSentEval 2026 Shared Task
               on Arabic Dialect Sentiment Analysis. We propose SARF (صرف) a
               multi-view architectural framework that integrates surface-level
               context with stemmed and rooted morphological perspectives using
               a shared MARBERTv2 encoder. Our system employs a hybrid BERT-CNN-BiLSTM-Attention
               architecture to capture both local sentiment n-grams and global
               sequential dependencies. Experimental results show that while
               individual morphological normalization strategies (stemming or
               rooting) may degrade performance, their joint integration via
               cross-morphological attention provides robust features across
               diverse dialects. Our final system achieved a competitive macro-F1-score
               of 0.9263, ranking 2nd out of 15 participating teams.},
  note      = {accepted}
}

@inproceedings{Hammerla:Mehler:2026:a,
  title     = {{Gutenberg+}: A More Temporally Faithful Corpus for Diachronic {NLP}},
  author    = {Leon Hammerla and Alexander Mehler},
  booktitle = {Proceedings Workshop on Structured Linguistic Data and Evaluation
               (SLiDE 2026), co-located with the Language Resources and Evaluation
               Conference (LREC 2026)},
  address   = {Palma de Mallorca (Spain)},
  year      = {2026},
  keywords  = {neglab},
  note      = {accepted}
}