The paper introduces a multilingual, multi-functional framework for disambiguating funder names in scientific publications, using a training dataset that merges the Research Organization Registry with Web of Science and Crossref Open Funder Registry data. By applying multi-task learning with contrastive and multiple negatives ranking losses, the authors fine‑tune open‑weight embedding models from the Sentence Transformer, Gemma, and Qwen3 families, achieving over 90% accuracy in matching Web of Science funder names to ROR identifiers and surpassing general‑purpose LLMs by more than 0.1. For funders not present in ROR, a similarity network is constructed to identify clusters, and the study discusses challenges related to smaller and non‑English‑speaking funders.
arXiv:2609.00228v1 Announce Type: new Abstract: Scientific domain entity linking (EL) differs from general domain EL because mentions and entity names often lack lexical overlap. Another challenge is...
arXiv:2608. 19201v1 Announce Type: cross Abstract: Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale.
arXiv:2608. 08636v1 Announce Type: cross Abstract: Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts.
The paper demonstrates that Matryoshka Representation Learning (MRL) embeddings contain a natural coarse‑to‑fine hierarchy, with prefix length aligning to gold taxonomy granularity. By training a simple order‑embedding probe called PEEL on these frozen embeddings, the authors can recover high‑fidelity taxonomic DAGs from a concept vocabulary alone, achieving strong results on two OLLM benchmarks and transferring to new domains without target‑edge supervision. The study shows that pre‑trained retrieval embeddings encode a decodable granularity gradient, enabling a minimal probe to extract explicit parent‑child (is‑a) structure.
BioELX is a retrieve‑rerank framework for cross‑lingual biomedical entity linking that tackles two key problems: the English‑biased UMLS alias training data and the degradation caused by naïvely adding context. It fine‑tunes SapBERT_multi with Wikidata‑derived cross‑lingual alias supervision to create shared concept neighborhoods, and then reranks candidates using pretrained LLMs with mention‑anchored prompting to focus on the target mention. Experiments demonstrate state‑of‑the‑art performance on four benchmarks, improving Recall@1 by 4.8–18.2 percentage points without task‑specific annotations.
The paper introduces a weakly supervised framework for extracting dataset mentions from forced displacement and Fragile, Conflict, and Violence (FCV) documents. It uses a lightweight model trained on general research literature to generate candidate mentions, which are then refined by a large language model that validates or rejects them and corrects boundaries. The refined annotations are augmented with synthetic and contrastive examples to fine‑tune the model, achieving 74.1% precision and 70.5% recall on a benchmark of 1,706 passages, with higher precision (89.5%) on passages that contain dataset references.
arXiv:2609.23231v1 Announce Type: new Abstract: Cross-lingual information retrieval (CLIR) is increasingly important in multi-national industries, where critical technical evidence may exist in a dif...
This thesis explores how to select and adapt NLP models for global health literature when annotated data and computational resources are scarce. It compares skip‑gram word2vec models trained on increasingly large specialized corpora with BioWordVec for semantic tag discovery, finding that larger coverage does not always yield more useful domain associations. The study also evaluates convolutional spaCy models versus a RoBERTa transformer for named entity recognition, noting a trade‑off between higher F1 scores and longer inference time, and investigates MiniLM few‑shot versus BART‑MNLI zero‑shot classification for multi‑label topic classification, highlighting practical constraints of inference cost. "whyItMatters":"The work provides empirical guidance on balancing model accuracy and resource demands for building knowledge systems in low‑resource global health settings."
The paper introduces a large corpus of about 160,000 Brazilian Portuguese financial news articles to support Natural Language Processing research in this domain. It presents initial benchmarks for a weakly supervised multi‑class classification task using semantic similarity, evaluating cosine similarity, K‑Nearest Neighbors (KNN), and FAISS‑based nearest‑neighbor search with multilingual embeddings. Results show that KNN performs best on manually labeled data (47.3% accuracy, 31.9% F1), while LLM‑annotated data yields similar accuracy but much lower F1 scores due to label imbalance and noise.
BELXTR is a new biomedical entity linking model that uses a multi‑vector (late interaction) architecture to preserve token‑level matching information, unlike traditional embedding‑based approaches that compress mentions into a single vector. By extending the XTR model with a task‑specific training objective and active query expansion, BELXTR achieves state‑of‑the‑art performance on half of ten evaluated corpora, with an average 5‑percentage‑point gain in recall@1. The model shows especially strong results on cross‑species gene disambiguation, outperforming an LLM‑powered retrieve‑and‑rerank pipeline and approaching a specialized rule‑based system.
The paper introduces Multi-Negative Direct Preference Optimisation (MDPO), a pairwise objective that compares the correct entity with all valid rejected candidates for each mention, extending the single-negative approach used in prior work. MDPO retains the Bradley‑Terry formulation of Direct Preference Optimisation while leveraging the full candidate set through masked, length‑normalised sequence scores. Experiments on French, German, English, Swedish, and Finnish historical newspaper datasets (hipe‑2020 and newseye) show that MDPO outperforms both supervised fine‑tuning and single‑negative DPO, especially for NIL mentions, semantic ambiguity, OCR noise, and historically challenging names, and highlight candidate retrieval as a key bottleneck.
arXiv:2608. 07254v1 Announce Type: cross Abstract: The increasing specialization of scientific research challenges existing classification systems, which provide effective representations of broad disciplines and research topics but often fail to capture the fine-grained conceptual structure of contemporary science.