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.

In this paper, we present the description of our proposed system for Subtask A - multilingual track at SemEval-2024 Task 8, which aims to classify if text has been generated by an AI or Human. Our approach treats binary text classification as token-level prediction, with the final classification being the average of token-level predictions. Through the use of rich representations of pre-trained transformers, our model is trained to selectively aggregate information from across different layers to score individual tokens, given that each layer may contain distinct information. Notably, our model demonstrates competitive performance on the test dataset, achieving an accuracy score of 95.8%. Furthermore, it secures the 2nd position in the multilingual track of Subtask A, with a mere 0.1% behind the leading system.

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 Github.

La reconnaissance d’entités nommées est une tâche importante en traitement automatique du langage naturel avec des applications dans de nombreux domaines. Dans cet article, nous décrivons une nouvelle approche pour la reconnaissance d’entités nommées, dans laquelle nous produisons un ensemble de segmentations en maximisant un score global. Pendant l’entraînement, nous optimisons notre modèle en maximisant la probabilité de la segmentation correcte. Pendant l’inférence, nous utilisons la programmation dynamique pour sélectionner la meilleure segmentation avec une complexité linéaire. Nous prouvons que notre approche est supérieure aux modèles champs de Markov conditionnels et semi-CMC pour la reconnaissance d’entités nommées.

There are two main paradigms for Named Entity Recognition (NER): sequence labelling and span classification. Sequence labelling aims to assign a label to each word in an input text using, for example, BIO (Begin, Inside and Outside) tagging, while span classification involves enumerating all possible spans in a text and classifying them into their labels. In contrast to sequence labelling, unconstrained span-based methods tend to assign entity labels to overlapping spans, which is generally undesirable, especially for NER tasks without nested entities. Accordingly, we propose GNNer, a framework that uses Graph Neural Networks to enrich the span representation to reduce the number of overlapping spans during prediction. Our approach reduces the number of overlapping spans compared to strong baseline while maintaining competitive metric performance. Code is available at https://github.com/urchade/GNNer.

Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. While the dominant paradigm of NER is sequence labelling, span-based approaches have become very popular in recent times but are less well understood. In this work, we study different aspects of span-based NER, namely the span representation, learning strategy, and decoding algorithms to avoid span overlap. We also propose an exact algorithm that efficiently finds the set of non-overlapping spans that maximizes a global score, given a list of candidate spans. We performed our study on three benchmark NER datasets from different domains. We make our code publicly available at https://github.com/urchade/span-structured-prediction.

Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. In this paper, we describe a novel approach to named entity recognition, in which we output a set of spans (i.e., segmentations) by maximizing a global score. During training, we optimize our model by maximizing the probability of the gold segmentation. During inference, we use dynamic programming to select the best segmentation under a linear time complexity. We prove that our approach outperforms CRF and semi-CRF models for Named Entity Recognition. We will make our code publicly available.

Dans cet article, nous nous intéressons à l’extraction d’entités médicales de type symptôme dans les textes biomédicaux. Cette tâche est peu explorée dans la littérature et il n’existe pas à notre connaissance de corpus annoté pour entraîner un modèle d’apprentissage. Nous proposons deux approches faiblement supervisées pour extraire ces entités. Une première est fondée sur la fouille de motifs et introduit une nouvelle contrainte de similarité sémantique. La seconde formule la tache comme une tache d’étiquetage de séquences en utilisant les CRF (champs conditionnels aléatoires). Nous décrivons les expérimentations menées qui montrent que les deux approches sont complémentaires en termes d’évaluation quantitative (rappel et précision). Nous montrons en outre que leur combinaison améliore sensiblement les résultats.

En classification de textes, la plupart des méthodes fondées sur des classifieurs statistiques utilisent des mots, ou des combinaisons de mots contigus, comme descripteurs. Si l’on veut prendre en compte plus d’informations le nombre de descripteurs non contigus augmente exponentiellement. Pour pallier à cette croissance, la fouille de motifs séquentiels permet d’extraire, de façon efficace, un nombre réduit de descripteurs qui sont à la fois fréquents et pertinents grâce à l’utilisation de contraintes. Dans ce papier, nous comparons l’utilisation de motifs fréquents sous contraintes et l’utilisation de motifs -libres, comme descripteurs. Nous montrons les avantages et inconvénients de chaque type de motif.