A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database

Constraint-based pattern discovery is at the core of numerous data mining tasks. Patterns are extracted with respect to a given set of constraints (frequency, closedness, size, etc). In the context of sequential pattern mining, a large number of devoted techniques have been developed for solving particular classes of constraints. The aim of this paper is to investigate the use of Constraint Programming (CP) to model and mine sequential patterns in a sequence database. Our CP approach offers a natural way to simultaneously combine in a same framework a large set of constraints coming from various origins. Experiments show the feasibility and the interest of our approach

Fouille de données pour associer des noms de sessions aux articles scientifiques

National audienceIn this paper, we present a proposition based on data mining to tackle the DEFT 2014 challenge. We focus on task 4 which consists of identifying the right conference session for scientific papers. The proposed approach is based on a combination of two data mining techniques. Sequence mining extracts frequent phrases in scientific papers in order to build paper and session descriptions. Then, those descriptions of papers and sessions are used to create a graph which represents shared descriptions. A graph mining technique is applied on the graph in order to extract a collection of homogenous sub-graphs corresponding to sets of papers associated to sessions.Nous décrivons dans cet article notre participation à l'édition 2014 de DEFT. Nous nous intéressons à la tâche consistant à associer des noms de session aux articles d'une conférence. Pour ce faire, nous proposons une approche originale, symbolique et non supervisée, de découverte de connaissances. L'approche combine des méthodes de fouille de données séquentielles et de fouille de graphes. La fouille de séquences permet d'extraire des motifs fréquents dans le but de construire des descriptions des articles et des sessions. Ces descriptions sont ensuite représentées par un graphe. Une technique de fouille de graphes appliquée sur ce graphe permet d'obtenir des collections de sous-graphes homogènes, correspondant à des collections d'articles et de noms de sessions

Graph Mining under Linguistic Constraints to Explore Large Texts

https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/1529International audienceIn this paper, we propose an approach to explore large texts by highlighting coherent sub-parts. The exploration method relies on a graph representation of the text according to Hoey's linguistic model which allows the selection and the binding of adjacent and non-adjacent sentences. The main contribution of our work consists in proposing a method based on both Hoey's linguistic model and a special graph mining technique, called CoHoP mining, to extract coherent sub-parts of the graph representation of the text. We have conducted some experiments on several English texts showing the interest of the proposed approach

Détection et classification non supervisées de relations sémantiques dans des articles scientifiques

International audienceDans cet article, nous abordons une tâche encore peu explorée, consistant à extraire automatiquement l'état de l'art d'un domaine scientifique à partir de l'analyse d'articles de ce domaine. Nous la ramenons à deux sous-tâches élémentaires : l'identification de concepts et la reconnaissance de relations entre ces concepts. Une extraction terminologique permet d'identifier les concepts candidats, qui sont ensuite alignés à des ressources externes. Dans un deuxième temps, nous cherchons à reconnaître et classifier automatiquement les relations sémantiques entre concepts de manière non-supervisée, en nous appuyant sur différentes techniques de clustering et de biclustering. Nous mettons en oeuvre ces deux étapes dans un corpus extrait de l'archive de l'ACL Anthology. Une analyse manuelle nous a permis de proposer une typologie des relations sémantiques, et de classifier un échantillon d'instances de relations. Les premières évaluations suggèrent l'intérêt du biclustering pour détecter de nouveaux types de relations dans le corpus. ABSTRACT Unsupervised Classification of Semantic Relations in Scientific Papers In this article, we tackle the yet unexplored task of automatically building the "state of the art" of a scientific domain from a corpus of research papers. This task is defined as a sequence of two basic steps : finding concepts and recognizing the relations between them. First, candidate concepts are identified using terminology extraction, and subsequently linked to external resources. Second, semantic relations between entities are categorized with different clustring and biclustering algorithms. Experiences were carried out on the ACL Anthology Corpus. Results are evaluated against a hand-crafted typology of semantic relations and manually categorized examples. The first results indicate that biclustering techniques may indeed be useful for detecting new types of relations. MOTS-CLÉS : analyse de la littérature scientifique, extraction de relations, clustering, biclustering

GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer

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 entity types. In contrast, Large Language Models (LLMs) can extract arbitrary entities through natural language instructions, offering greater flexibility. However, their size and cost, particularly for those accessed via APIs like ChatGPT, make them impractical in resource-limited scenarios. In this paper, we introduce a compact NER model trained to identify any type of entity. Leveraging a bidirectional transformer encoder, our model, GLiNER, facilitates parallel entity extraction, an advantage over the slow sequential token generation of LLMs. Through comprehensive testing, GLiNER demonstrate strong performance, outperforming both ChatGPT and fine-tuned LLMs in zero-shot evaluations on various NER benchmarks.Comment: Work in progres

An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction

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 conventional generative information extraction models that are left-to-right token-level generators, our approach is \textit{span-based}. It generates a linearized graph where nodes represent text spans and edges represent relation triplets. Our method employs a transformer encoder-decoder architecture with pointing mechanism on a dynamic vocabulary of spans and relation types. Our model can capture the structural characteristics and boundaries of entities and relations through span representations while simultaneously grounding the generated output in the original text thanks to the pointing mechanism. Evaluation on benchmark datasets validates the effectiveness of our approach, demonstrating competitive results. Code is available at https://github.com/urchade/ATG.Comment: AAAI 2024 (camera ready version

Filtered Semi-Markov CRF

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 text segmentation as token-level prediction, Semi-CRF considers segments as the basic unit, making it more expressive. However, Semi-CRF suffers from two major drawbacks: (1) quadratic complexity over sequence length, as it operates on every span of the input sequence, and (2) inferior performance compared to CRF for sequence labeling tasks like NER. In this paper, we introduce Filtered Semi-Markov CRF, a variant of Semi-CRF that addresses these issues by incorporating a filtering step to eliminate irrelevant segments, reducing complexity and search space. Our approach is evaluated on several NER benchmarks, where it outperforms both CRF and Semi-CRF while being significantly faster. The implementation of our method is available on \href{https://github.com/urchade/Filtered-Semi-Markov-CRF}{Github}.Comment: EMNLP 2023 (Findings

Calcul de réseaux phrastiques pour l'analyse et la navigation textuelle

International audienceIn this paper, we present an automatic process based on lexical repetition introduced by Hoey. The application of that kind of approaches on large texts is difficult to do by hand. In the paper, we propose an automatic process to treat large texts. We have conducted some experiments on different kinds of texts (narrative, expositive) to show the benefits of the approach.Le travail présente une méthode de navigation dans les textes, fondée sur la répétition lexicale. La méthode choisie est celle développée par le linguiste Hoey. Son application manuelle à des textes de grandeur conséquente est problématique. Nous proposons dans cet article un processus automatique qui permet d'analyser selon cette méthode des textes de grande taille ; des expériences ont été menées appliquant le processus à différents types de textes (narratif, expositif) et montrant l'intérêt de l'approche

Fouille de motifs séquentiels pour la découverte de relations entre gènes et maladies rares

National audienceOrphanet est un organisme dont l'objectif est notamment de rassembler des collections d'articles traitant de maladies rares. Cependant, l'acquisition de nouvelles connaissances dans ce domaine est actuellement réalisée manuellement. Dès lors, obtenir de nouvelles informations relatives aux maladies rares est un processus chronophage. Permettre d'obtenir ces informations de manière automatique est donc un enjeu important. Dans ce contexte, nous proposons d'aborder la question de l'extraction de relations entre gènes et maladies rares en utilisant des approches de fouille de données, plus particulièrement de fouille de motifs séquentiels sous contraintes. Nos expérimentations montrent l'intérêt de notre approche pour l'extraction de relations entre gènes et maladies rares à partir de résumés d'articles de PubMe

Sequence Classification Based on Delta-Free Sequential Pattern

International audienceSequential pattern mining is one of the most studied and challenging tasks in data mining. However, the extension of well-known methods from many other classical patterns to sequences is not a trivial task. In this paper we study the notion of δ-freeness for sequences. While this notion has extensively been discussed for itemsets, this work is the first to extend it to sequences. We define an efficient algorithm devoted to the extraction of δ-free sequential patterns. Furthermore, we show the advantage of the δ-free sequences and highlight their importance when building sequence classifiers, and we show how they can be used to address the feature selection problem in statistical classifiers, as well as to build symbolic classifiers which optimizes both accuracy and earliness of predictions