ReTaT is a dataset that can be used to train and evaluate systems for extracting relations whose expression spans the two modalities and its quality and suitability were assessed for the joint table-text relation extraction task using Large Language Models (LLMs).
This team investigates how far a lightweight, interpretable system can go without any pretrained language model at the relation classification stage on the HIPE-2026 shared task, finding that minimum character distance alone captures most of the classification signal.
With the rapid growth of digital text data, automatically extracting meaningful information from unstructured text has become essential for applications such as information retrieval, knowledge extraction, and question answering. Named Entity Recognition (NER) is a key task in Natural Language Processing (NLP) that identifies and classifies entities such as persons, organizations, locations, dates, and monetary values into predefined categories. A large portion of real-world data is unstructured, making NER essential for extracting structured information. This study evaluates two widely used Python-based NLP libraries, spaCy and NLTK, on a benchmark dataset (CoNLL-2003) and a custom news dataset from Kaggle. Performance was measured using precision, recall, and F1-score. Results show that spaCy outperforms NLTK in both accuracy and efficiency, especially for real-world text data, demonstrating its suitability for robust and scalable NER applications. BERT-based and transformer-based models are discussed in the related work section to provide insight into recent advancements in Named Entity Recognition techniques.
Ilakkiya.B, Hemanandhini.I.G· 2026 4th International Confe...· 0 citations
The automatic structural analysis of legal texts is a cornerstone of legal technology, yet the extraction of their logical components remains a significant challenge. In this paper, we introduce the task of identifying and segmenting legal conditions (Tatbestand) and legal consequences (Rechtsfolge) within German statutory texts. To support this task, we present ANNOTARES (Annotations of Tatbestand-Rechtsfolge Sequences), a novel dataset comprising German law texts with span-level annotations. Spanning three distinct legal codes, the dataset is designed to evaluate both domain-specific performance and cross-statute generalizability. We benchmark diverse architectural approaches: a rule-based baseline, CRFs, BiLSTMs, BiLSTM-CRF, and modern Transformer-based models, including BERT variants and LLM-based methods. Our results demonstrate that BERT and LLM-based models achieve superior performance in capturing the complex syntactic structures of legal language. We release our dataset to facilitate further research in automated legal reasoning.
Wikipedia and Wikidata are widely used for information access, LLM pre-training, and retrieval-augmented generation. Their knowledge is deeply connected but scattered across text, tables, and knowledge graphs. This raises a practical question: when these modalities disagree, how can we detect and explain the conflict? We study this problem as modality-level inconsistency detection. We first introduce a taxonomy of cross-modal knowledge inconsistencies, covering information granularity differences, direct conflicts, temporal changes, and KG incompleteness. We then present Kontrast, an automatic framework that uses Text-to-SPARQL and LLM reasoning to compare table-based answers with KG evidence and categorize the resulting inconsistencies. Experiments on various Table-QA datasets show that cross-modal inconsistencies are common and informative. They reveal not only true knowledge conflicts, but also missing KG structure and temporal mismatches while being limited by Text-to-SPARQL errors and noise. Our analysis shows that text, tables, and KGs can complement and correct one another through systematic comparison. Kontrast provides a practical tool for large-scale knowledge auditing and establishes a benchmark for future work on cross-modal knowledge consistency. Code and data are available at https://github.com/ECLADATTA/KONTRAST.
Relation extraction (RE) identifies semantic relations between entities in text, with existing methods falling into two main paradigms: discriminative and generative . Discriminative models encode sentences and entities into relation representations and classify the most likely relation, whereas generative models directly produce relation labels through sequence generation. Although the latter have benefited from recent advances in large language models (LLMs), their performance remains limited by bottlenecks. In this work, we present the systematic investigation of how discriminative models can support generative RE . We pro-pose the D iscriminative-to-G enerative ( D2G ) framework, which first leverages discriminative models to produce a top-k set of candidate relations, and then integrates this knowledge into generative models via in-context or prompt learning. Extensive experiments on five benchmarks demonstrate that D2G consistently achieves state-of-the-art performance, with notable gains on long-tailed relation classes.
Guozheng Li, Peng Wang, Zijie Xu et al.· Annual Meeting of the Associ...· 0 citations