Open Relation Extraction (OpenRE) seeks to identify and extract novel relational facts between named entities from unlabeled data without pre-defined relation schemas. Traditional OpenRE methods typically assume that the unlabeled data consists solely of novel relations or is pre-divided into known and novel instances. However, in real-world scenarios, novel relations are arbitrarily distributed. In this paper, we propose a generalized OpenRE setting that considers unlabeled data as a mixture of both known and novel instances. To address this, we propose MixORE, a two-phase framework that integrates relation classification and clustering to jointly learn known and novel relations. Experiments on three benchmark datasets demonstrate that MixORE consistently outperforms competitive baselines in known relation classification and novel relation clustering. Our findings contribute to the advancement of generalized OpenRE research and real-world applications.

Distantly-Supervised Named Entity Recognition (DS-NER) uses knowledge bases or dictionaries for annotations, reducing manual efforts but rely on large human labeled validation set. In this paper, we introduce a real-life DS-NER dataset, QTL, where the training data is annotated using domain dictionaries and the test data is annotated by domain experts. This dataset has a small validation set, reflecting real-life scenarios. Existing DS-NER approaches fail when applied to QTL, which motivate us to re-examine existing DS-NER approaches. We found that many of them rely on large validation sets and some used test set for tuning inappropriately. To solve this issue, we proposed a new approach, token-level Curriculum-based Positive-Unlabeled Learning (CuPUL), which uses curriculum learning to order training samples from easy to hard. This method stabilizes training, making it robust and effective on small validation sets. CuPUL also addresses false negative issues using the Positive-Unlabeled learning paradigm, demonstrating improved performance in real-life applications.

This paper presents a novel radio frequency fingerprint (RFF) enhancement strategy by exploiting the physical unclonable function (PUF) to tune the RF hardware impairments in a unique and secure manner, which is exemplified by taking power amplifiers (PAs) in RF chains as an example. This is achieved by intentionally and slightly tuning the PA non-linearity characteristics using the active load-pulling technique. The motivation driving the proposed research is to enlarge the RFF feature differences among wireless devices of same vendor, in order to massively improve their RFF classification accuracy in low to medium signal to noise ratio (SNR) channel conditions. PUF is employed to dynamically tune the PA’s RFF feature which guarantees the security since the PUF response cannot be cloned. Specifically, a ring oscillator (RO)-based PUF is implemented to control the PA non-linearity by selecting unique but random configuration parameters. This approach is proposed to amplify the distinctions across same model PAs, thereby enhancing the RFF classification performance. In the meantime, our innovative strategy of PUF-assisted RFF does not noticeably compromise communication link performance which is experimentally tested. The resulting RFF features can be extracted from the received distorted constellation diagrams with the help of image recognition-based machine learning classification algorithms. Extensive experimental evaluations are carried out using both cable-connected and over-the-air (OTA) measurements. Our proposed approach, when classifying eight PAs from a same vendor, achieves 11% to 24% average classification accuracy improvement by enlarging the RFF feature differences arising from the PA non-linearity.

To reduce human annotations for relation extraction (RE) tasks, distantly supervised approaches have been proposed, while struggling with low performance. In this work, we propose a novel DSRE-NLI framework, which considers both distant supervision from existing knowledge bases and indirect supervision from pretrained language models for other tasks. DSRE-NLI energizes an off-the-shelf natural language inference (NLI) engine with a semi-automatic relation verbalization (SARV) mechanism to provide indirect supervision and further consolidates the distant annotations to benefit multi-classification RE models. The NLI-based indirect supervision acquires only one relation verbalization template from humans as a semantically general template for each relationship, and then the template set is enriched by high-quality textual patterns automatically mined from the distantly annotated corpus. With two simple and effective data consolidation strategies, the quality of training data is substantially improved. Extensive experiments demonstrate that the proposed framework significantly improves the SOTA performance (up to 7.73% of F1) on distantly supervised RE benchmark datasets. Our code is available at https://github.com/kangISU/DSRE-NLI.

Unsupervised relation extraction (URE) aims to extract relations between named entities from raw text without requiring manual annotations or pre-existing knowledge bases.In recent studies of URE, researchers put a notable emphasis on contrastive learning strategies for acquiring relation representations.However, these studies often overlook two important aspects: the inclusion of diverse positive pairs for contrastive learning and the exploration of appropriate loss functions.In this paper, we propose AugURE with both within-sentence pairs augmentation and augmentation through crosssentence pairs extraction to increase the diversity of positive pairs and strengthen the discriminative power of contrastive learning.We also identify the limitation of noise-contrastive estimation (NCE) loss for relation representation learning and propose to apply margin loss for sentence pairs.Experiments on NYT-FB and TACRED datasets demonstrate that the proposed relation representation learning and a simple K-Means clustering achieves state-ofthe-art performance.Source code is available 1 .

In this paper, we study the named entity recognition (NER) problem under distant supervision.Due to the incompleteness of the external dictionaries and/or knowledge bases, such distantly annotated training data usually suffer from a high false negative rate.To this end, we formulate the Distantly Supervised NER (DS-NER) problem via Multi-class Positive and Unlabeled (MPU) learning and propose a theoretically and practically novel CONFidence-based MPU (Conf-MPU) approach.To handle the incomplete annotations, Conf-MPU consists of two steps.First, a confidence score is estimated for each token of being an entity token.Then, the proposed Conf-MPU risk estimation is applied to train a multi-class classifier for the NER task.Thorough experiments on two benchmark datasets labeled by various external knowledge demonstrate the superiority of the proposed Conf-MPU over existing DS-NER methods.Our code is available at Github 1 .