Publications tagged with Named entity recognition
Published:
Publications tagged with "Named entity recognition"
- DetailsCampanile, L., de Biase, M. S., Marrone, S., Marulli, F., Raimondo, M., & Verde, L. (2022). Sensitive Information Detection Adopting Named Entity Recognition: A Proposed Methodology [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13380 LNCS, 377–388. https://doi.org/10.1007/978-3-031-10542-5_26
Abstract
Protecting and safeguarding privacy has become increasingly important, especially in recent years. The increasing possibilities of acquiring and sharing personal information and data through digital devices and platforms, such as apps or social networks, have increased the risks of privacy breaches. In order to effectively respect and guarantee the privacy and protection of sensitive information, it is necessary to develop mechanisms capable of providing such guarantees automatically and reliably. In this paper we propose a methodology able to automatically recognize sensitive data. A Named Entity Recognition was used to identify appropriate entities. An improvement in the recognition of these entities is achieved by evaluating the words contained in an appropriate context window by assessing their similarity to words in a domain taxonomy. This, in fact, makes it possible to refine the labels of the recognized categories using a generic Named Entity Recognition. A preliminary evaluation of the reliability of the proposed approach was performed. In detail, texts of juridical documents written in Italian were analyzed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. - DetailsMarulli, F., Verde, L., & Campanile, L. (2021). Exploring data and model poisoning attacks to deep learning-based NLP systems [Conference paper]. Procedia Computer Science, 192, 3570–3579. https://doi.org/10.1016/j.procs.2021.09.130
Abstract
Natural Language Processing (NLP) is being recently explored also to its application in supporting malicious activities and objects detection. Furthermore, NLP and Deep Learning have become targets of malicious attacks too. Very recent researches evidenced that adversarial attacks are able to affect also NLP tasks, in addition to the more popular adversarial attacks on deep learning systems for image processing tasks. More precisely, while small perturbations applied to the data set adopted for training typical NLP tasks (e.g., Part-of-Speech Tagging, Named Entity Recognition, etc..) could be easily recognized, models poisoning, performed by the means of altered data models, typically provided in the transfer learning phase to a deep neural networks (e.g., poisoning attacks by word embeddings), are harder to be detected. In this work, we preliminary explore the effectiveness of a poisoned word embeddings attack aimed at a deep neural network trained to accomplish a Named Entity Recognition (NER) task. By adopting the NER case study, we aimed to analyze the severity of such a kind of attack to accuracy in recognizing the right classes for the given entities. Finally, this study represents a preliminary step to assess the impact and the vulnerabilities of some NLP systems we adopt in our research activities, and further investigating some potential mitigation strategies, in order to make these systems more resilient to data and models poisoning attacks. © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.