8 citations · 8 across the 13 of their papers we have counts for
14 papers
Local Evidence and Geometric Readout Repair in Trained GNNs
Nadi Tomeh, Hugo Attali
Many node-classification GNNs apply a linear classifier to a nonnegative mixture of local messages. An error can reflect either poor mixture weights or a reachable logit set poorly…
Semantic-Guided Reading Order Reconstruction in Historical Armenian Newspapers with LLMs
Chahan Vidal-Gorène, Nadi Tomeh, Victoria Khurshudyan
This paper addresses reading order reconstruction in historical Armenian newspapers, which combine complex layouts with limited language resources. We introduce a new annotated dat…
Diffusion-MF: Approximate Structured Diffusion for Sequence Labelling
Nicolas Floquet, Joseph Le Roux, Nadi Tomeh
We introduce Diffusion-MF, a discrete diffu- sion sequence labeller that places a linear-chain conditional random field (LCRF) inside the denoising loop. Unlike prior diffusion lab…
A Voronoi Cell Formulation for Principled Token Pruning in Late-Interaction Retrieval Models
Yash Kankanampati, Yuxuan Zong, Nadi Tomeh +2
Late-interaction models such as ColBERT offer competitive performance across various retrieval tasks but require storing a dense embedding for each document token, leading to a sub…
Sparsity-Aware Evolution for Model Merging
Huan Zhang, Yanjian Zhang, Guillaume Wisniewski +2
We propose a sparsity-aware evolutionary (SAE) framework for model merging that involves iterative pruning-merging cycles to act as a novel mutation operator. We incorporate the sp…
ToMMeR -- Efficient Entity Mention Detection from Large Language Models
Victor Morand, Nadi Tomeh, Josiane Mothe +1
Identifying which text spans refer to entities - mention detection - is both foundational for information extraction and a known performance bottleneck. We introduce ToMMeR, a ligh…
Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?
Yanjian Zhang, Guillaume Wisniewski, Nadi Tomeh +1
Human reasoning involves different strategies, each suited to specific problems. Prior work shows that large language model (LLMs) tend to favor a single reasoning strategy, potent…
Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement
Nicolas Floquet, Joseph Le Roux, Nadi Tomeh +1
We propose a novel architecture for graph-based dependency parsing that explicitly constructs vectors, from which both arcs and labels are scored. Our method addresses key limitati…
Manual Verbalizer Enrichment for Few-Shot Text Classification
Quang Anh Nguyen, Nadi Tomeh, Mustapha Lebbah +3
With the continuous development of pre-trained language models, prompt-based training becomes a well-adopted paradigm that drastically improves the exploitation of models for many…
EnriCo: Enriched Representation and Globally Constrained Inference for Entity and Relation Extraction
Urchade Zaratiana, Nadi Tomeh, Yann Dauxais +2
Joint entity and relation extraction plays a pivotal role in various applications, notably in the construction of knowledge graphs. Despite recent progress, existing approaches oft…
GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction
Urchade Zaratiana, Nadi Tomeh, Niama El Khbir +2
Information extraction (IE) is an important task in Natural Language Processing (NLP), involving the extraction of named entities and their relationships from unstructured text. In…
An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction
Urchade Zaratiana, Nadi Tomeh, Pierre Holat +1
In this paper, we propose a novel method for joint entity and relation extraction from unstructured text by framing it as a conditional sequence generation problem. In contrast to…
Filtered Semi-Markov CRF
Urchade Zaratiana, Nadi Tomeh, Niama El Khbir +2
Semi-Markov CRF has been proposed as an alternative to the traditional Linear Chain CRF for text segmentation tasks such as Named Entity Recognition (NER). Unlike CRF, which treats…
GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer
Urchade Zaratiana, Nadi Tomeh, Pierre Holat +1
Named Entity Recognition (NER) is essential in various Natural Language Processing (NLP) applications. Traditional NER models are effective but limited to a set of predefined entit…