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Articles de revues sur le sujet « Entity-level »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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1

Yaghoobzadeh, Yadollah, Heike Adel, and Hinrich Schuetze. "Corpus-Level Fine-Grained Entity Typing." Journal of Artificial Intelligence Research 61 (April 17, 2018): 835–62. http://dx.doi.org/10.1613/jair.5601.

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Extracting information about entities remains an important research area. This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class, such as "food" or "artist". The application of entity typing we are interested in is knowledge base completion, specifically, to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding-based and combines (i) a global model that computes scores based on global information of an entity and (ii) a context model that first evaluates th

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Xu, Zhengfei, Sijia Zhao, Yanchao Hao, et al. "Reverse Region-to-Entity Annotation for Pixel-Level Visual Entity Linking." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12981–89. https://doi.org/10.1609/aaai.v39i12.33416.

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Visual Entity Linking (VEL) is a crucial task for achieving fine-grained visual understanding, matching objects within images (visual mentions) to entities in a knowledge base. Previous VEL tasks rely on textual inputs, but writing queries for complex scenes can be challenging. Visual inputs like clicks or bounding boxes offer a more convenient alternative. Therefore, we propose a new task, Pixel-Level Visual Entity Linking (PL-VEL), which uses pixel masks from visual inputs to refer to objects, supplementing reference methods for VEL. To facilitate research on this task, we have constructed t

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Unnikrishnan, Vishnu, Christian Beyer, Pawel Matuszyk, et al. "Entity-level stream classification: exploiting entity similarity to label the future observations referring to an entity." International Journal of Data Science and Analytics 9, no. 1 (2019): 1–15. http://dx.doi.org/10.1007/s41060-019-00177-1.

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Yu, Jianfei, Jing Jiang, and Rui Xia. "Entity-Sensitive Attention and Fusion Network for Entity-Level Multimodal Sentiment Classification." IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020): 429–39. http://dx.doi.org/10.1109/taslp.2019.2957872.

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Beyer, Christian, Vishnu Unnikrishnan, Robert Brüggemann, et al. "Resource management for model learning at entity level." Annals of Telecommunications 75, no. 9-10 (2020): 549–61. http://dx.doi.org/10.1007/s12243-020-00800-4.

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Abstract Many current and future applications plan to provide entity-specific predictions. These range from individualized healthcare applications to user-specific purchase recommendations. In our previous stream-based work on Amazon review data, we could show that error-weighted ensembles that combine entity-centric classifiers, which are only trained on reviews of one particular product (entity), and entity-ignorant classifiers, which are trained on all reviews irrespective of the product, can improve prediction quality. This came at the cost of storing multiple entity-centric models in prim

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Partridge, C., and G. Trewitt. "The high-level entity management system (HEMS)." IEEE Network 2, no. 2 (1988): 37–42. http://dx.doi.org/10.1109/65.3257.

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Ribeiro, Clarissa. "Molmedia: Communication at the elementary entity level." Technoetic Arts 16, no. 2 (2018): 153–64. http://dx.doi.org/10.1386/tear.16.2.153_1.

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Qian, Tao, Meishan Zhang, Yinxia Lou, and Daiwen Hua. "A Joint Model for Named Entity Recognition With Sentence-Level Entity Type Attentions." IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021): 1438–48. http://dx.doi.org/10.1109/taslp.2021.3069295.

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Bai, Qingchun, Kai Wei, Jie Zhou, et al. "Entity-level sentiment prediction in Danmaku video interaction." Journal of Supercomputing 77, no. 9 (2021): 9474–93. http://dx.doi.org/10.1007/s11227-021-03652-4.

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Yuan, Changsen, Heyan Huang, Chong Feng, Ge Shi, and Xiaochi Wei. "Document-level relation extraction with Entity-Selection Attention." Information Sciences 568 (August 2021): 163–74. http://dx.doi.org/10.1016/j.ins.2021.04.007.

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Papadakis, George, Georgia Koutrika, Themis Palpanas, and Wolfgang Nejdl. "Meta-Blocking: Taking Entity Resolutionto the Next Level." IEEE Transactions on Knowledge and Data Engineering 26, no. 8 (2014): 1946–60. http://dx.doi.org/10.1109/tkde.2013.54.

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Gomoi, Bogdan Cosmin. "Funding Strategies at the Level of an Entity." CECCAR Business Review 4, no. 9 (2023): 8–19. http://dx.doi.org/10.37945/cbr.2023.09.02.

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Xiong, Ying, Shuai Chen, Qingcai Chen, Jun Yan, and Buzhou Tang. "Using Character-Level and Entity-Level Representations to Enhance Bidirectional Encoder Representation From Transformers-Based Clinical Semantic Textual Similarity Model: ClinicalSTS Modeling Study." JMIR Medical Informatics 8, no. 12 (2020): e23357. http://dx.doi.org/10.2196/23357.

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Background With the popularity of electronic health records (EHRs), the quality of health care has been improved. However, there are also some problems caused by EHRs, such as the growing use of copy-and-paste and templates, resulting in EHRs of low quality in content. In order to minimize data redundancy in different documents, Harvard Medical School and Mayo Clinic organized a national natural language processing (NLP) clinical challenge (n2c2) on clinical semantic textual similarity (ClinicalSTS) in 2019. The task of this challenge is to compute the semantic similarity among clinical text s

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Gridach, Mourad. "Character-level neural network for biomedical named entity recognition." Journal of Biomedical Informatics 70 (June 2017): 85–91. http://dx.doi.org/10.1016/j.jbi.2017.05.002.

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Wang, Limin, Shoushan Li, Qian Yan, and Guodong Zhou. "Domain-specific Named Entity Recognition with Document-Level Optimization." ACM Transactions on Asian and Low-Resource Language Information Processing 17, no. 4 (2018): 1–15. http://dx.doi.org/10.1145/3213544.

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Mao, Hongli, Xian-Ling Mao, Hanlin Tang, Yu-Ming Shang, and Heyan Huang. "Span Graph Transformer for Document-Level Named Entity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 18769–77. http://dx.doi.org/10.1609/aaai.v38i17.29841.

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Named Entity Recognition (NER), which aims to identify the span and category of entities within text, is a fundamental task in natural language processing. Recent NER approaches have featured pre-trained transformer-based models (e.g., BERT) as a crucial encoding component to achieve state-of-the-art performance. However, due to the length limit for input text, these models typically consider text at the sentence-level and cannot capture the long-range contextual dependency within a document. To address this issue, we propose a novel Span Graph Transformer (SGT) method for document-level NER,

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Mascha, Maureen Francis, Melvin A. Lamboy-Ruiz, and Diane J. Janvrin. "PCAOB inspections: An analysis of entity-level and application-level control audit deficiencies." International Journal of Accounting Information Systems 30 (September 2018): 19–39. http://dx.doi.org/10.1016/j.accinf.2018.06.002.

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Li, Weijun, Jianping Ding, Shixia Liu, Xueyang Liu, Yilei Su, and Ziyi Wang. "Chinese Named Entity Recognition Based on Multi-Level Representation Learning." Applied Sciences 14, no. 19 (2024): 9083. http://dx.doi.org/10.3390/app14199083.

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Named Entity Recognition (NER) is a crucial component of Natural Language Processing (NLP). When dealing with the high diversity and complexity of the Chinese language, existing Chinese NER models face challenges in addressing word sense ambiguity, capturing long-range dependencies, and maintaining robustness, which hinders the accuracy of entity recognition. To this end, a Chinese NER model based on multi-level representation learning is proposed. The model leverages a pre-trained word-based embedding to capture contextual information. A linear layer adjusts dimensions to fit an Extended Long

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Hu, Anwen, Zhicheng Dou, Jian-Yun Nie, and Ji-Rong Wen. "Leveraging Multi-Token Entities in Document-Level Named Entity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7961–68. http://dx.doi.org/10.1609/aaai.v34i05.6304.

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Most state-of-the-art named entity recognition systems are designed to process each sentence within a document independently. These systems are easy to confuse entity types when the context information in a sentence is not sufficient enough. To utilize the context information within the whole document, most document-level work let neural networks on their own to learn the relation across sentences, which is not intuitive enough for us humans. In this paper, we divide entities to multi-token entities that contain multiple tokens and single-token entities that are composed of a single token. We

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Shamaev, A. A. "Modeling of income distribution on the level of business entity." Вестник Белорусско-Российского университета, no. 3 (2009): 190–97. http://dx.doi.org/10.53078/20778481_2009_3_190.

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Zhou, Yinle, Eric Nelson, Fumiko Kobayashi, and John R. Talburt. "A Graduate-Level Course on Entity Resolution and Information Quality." Journal of Data and Information Quality 4, no. 2 (2013): 1–10. http://dx.doi.org/10.1145/2435221.2435226.

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Syed, Arif, and Kali Kalirajan. "Benchmarking tax compliance efficiency (risk) at the business entity level." Benchmarking: An International Journal 7, no. 3 (2000): 206–22. http://dx.doi.org/10.1108/14635770010331379.

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Alec, Céline, and Marc Spaniol. "Fine-grainedWeb Content Classificationvia Entity-level Analytics:The Case ofSemantic Fingerprinting." Journal of Web Engineering 17, no. 6 (2019): 449–82. http://dx.doi.org/10.13052/jwe1540-9589.17673.

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Zhou, Zhenhong, Jiuyang Xiang, Chaomeng Chen, and Sen Su. "Quantifying and Analyzing Entity-Level Memorization in Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 19741–49. http://dx.doi.org/10.1609/aaai.v38i17.29948.

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Large language models (LLMs) have been proven capable of memorizing their training data, which can be extracted through specifically designed prompts. As the scale of datasets continues to grow, privacy risks arising from memorization have attracted increasing attention. Quantifying language model memorization helps evaluate potential privacy risks. However, prior works on quantifying memorization require access to the precise original data or incur substantial computational overhead, making it difficult for applications in real-world language models. To this end, we propose a fine-grained, en

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Yu, Yiting, Zanbo Wang, Wei Wei, et al. "Exploiting global contextual information for document-level named entity recognition." Knowledge-Based Systems 284 (January 2024): 111266. http://dx.doi.org/10.1016/j.knosys.2023.111266.

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Irawan, Didik, and Alia Ariesanti. "ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI TINGKAT KEPATUHAN PAJAK WAJIB PAJAK BADAN PADA PERUSAHAAN INDUSTRI MANUFAKTUR DI DAERAH ISTIMEWA YOGYAKARTA." Jurnal REKSA: Rekayasa Keuangan, Syariah dan Audit 4, no. 1 (2018): 20. http://dx.doi.org/10.12928/j.reksa.v4i1.38.

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The purpose of the research is to analyze the behavior of tax compliance of corporate taxpayers, particularly large industrial manufacturing company that exists through out Daerah Istimewa Yogyakarta, represented by professional tax who work at the company. The results of this study indicate that: (1) the strength or weakness of a professional tax attitude does not affect the level of tax compliance in a corporate entity, (2) subjective norm has no effect on the level of corporate tax compliance, (3) strong or weak moral obligation of professional tax does not affect the level of tax complianc

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Nowak, Christian, and Anna Elisabeth Freiin von Preuschen-von Lewinski. "How to make a SPE resolution strategy work in the EU." Zeitschrift für Bankrecht und Bankwirtschaft 36, no. 1 (2024): 16–20. http://dx.doi.org/10.15375/zbb-2024-0106.

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Abstract When dealing with the resolution of a banking group under EU regulation there are two different approaches: Single point of entry (SPE) versus Multiple Point of Entry (MPE). In a MPE strategy, the resolution authority is taking separate resolution actions at the level of individual legal entities of the group. In a SPE strategy, the resolution authority is taking a resolution action at the level of a single entity, usually the parent undertaking, which, consequently, becomes the so called “resolution entity” building together with its subsidiaries the “resolution group”. The general i

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Liao, Tao, Rongmei Huang, Shunxiang Zhang, et al. "Nested Named Entity Recognition Based on Dual Stream Feature Complementation." Entropy 24, no. 10 (2022): 1454. http://dx.doi.org/10.3390/e24101454.

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Named entity recognition is a basic task in natural language processing, and there is a large number of nested structures in named entities. Nested named entities become the basis for solving many tasks in NLP. A nested named entity recognition model based on dual-flow features complementary is proposed for obtaining efficient feature information after text coding. Firstly, sentences are embedded at both the word level and the character level of the words, then sentence context information is obtained separately via the neural network Bi-LSTM; Afterward, two vectors perform low-level feature c

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Jia, Qi, Dezheng Zhang, Haifeng Xu, and Yonghong Xie. "Extraction of Traditional Chinese Medicine Entity: Design of a Novel Span-Level Named Entity Recognition Method With Distant Supervision." JMIR Medical Informatics 9, no. 6 (2021): e28219. http://dx.doi.org/10.2196/28219.

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Background Traditional Chinese medicine (TCM) clinical records contain the symptoms of patients, diagnoses, and subsequent treatment of doctors. These records are important resources for research and analysis of TCM diagnosis knowledge. However, most of TCM clinical records are unstructured text. Therefore, a method to automatically extract medical entities from TCM clinical records is indispensable. Objective Training a medical entity extracting model needs a large number of annotated corpus. The cost of annotated corpus is very high and there is a lack of gold-standard data sets for supervis

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Kochetova-Kozloski, Natalia, Thomas M. Kozloski, and William F. Messier. "Auditor Business Process Analysis and Linkages among Auditor Risk Judgments." AUDITING: A Journal of Practice & Theory 32, no. 3 (2013): 123–39. http://dx.doi.org/10.2308/ajpt-50413.

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SUMMARY: This research note examines whether auditors “link” their entity-level risk assessments to their core business process risk assessments. For auditors who performed a business process analysis of the core business process, there is a positive association between the identification of significant process-level business risks and the identification of significant business risks at the entity level. We also find that performing a business process analysis leads to higher assessments of the risk of material misstatement at the process level. With respect to the linkages between risk-relate

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Chen, Yang, and Bowen Shi. "Enhanced Heterogeneous Graph Attention Network with a Novel Multilabel Focal Loss for Document-Level Relation Extraction." Entropy 26, no. 3 (2024): 210. http://dx.doi.org/10.3390/e26030210.

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Recent years have seen a rise in interest in document-level relation extraction, which is defined as extracting all relations between entities in multiple sentences of a document. Typically, there are multiple mentions corresponding to a single entity in this context. Previous research predominantly employed a holistic representation for each entity to predict relations, but this approach often overlooks valuable information contained in fine-grained entity mentions. We contend that relation prediction and inference should be grounded in specific entity mentions rather than abstract entity con

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Park, Seongsik, and Harksoo Kim. "Effective sentence-level relation extraction model using entity-centric dependency tree." PeerJ Computer Science 10 (September 18, 2024): e2311. http://dx.doi.org/10.7717/peerj-cs.2311.

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The syntactic information of a dependency tree is an essential feature in relation extraction studies. Traditional dependency-based relation extraction methods can be categorized into hard pruning methods, which aim to remove unnecessary information, and soft pruning methods, which aim to utilize all lexical information. However, hard pruning has the potential to overlook important lexical information, while soft pruning can weaken the syntactic information between entities. As a result, recent studies in relation extraction have been shifting from dependency-based methods to pre-trained langu

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Zhang, Zhichang, Lin Zhu, and Peilin Yu. "Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation." JMIR Medical Informatics 8, no. 5 (2020): e17637. http://dx.doi.org/10.2196/17637.

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Background Medical entity recognition is a key technology that supports the development of smart medicine. Existing methods on English medical entity recognition have undergone great development, but their progress in the Chinese language has been slow. Because of limitations due to the complexity of the Chinese language and annotated corpora, these methods are based on simple neural networks, which cannot effectively extract the deep semantic representations of electronic medical records (EMRs) and be used on the scarce medical corpora. We thus developed a new Chinese EMR (CEMR) dataset with

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Chen, Shuang, Jinpeng Wang, Feng Jiang, and Chin-Yew Lin. "Improving Entity Linking by Modeling Latent Entity Type Information." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7529–37. http://dx.doi.org/10.1609/aaai.v34i05.6251.

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Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility. However, the latent entity type information in the immediate context of the mention is neglected, which causes the models often link mentions to incorrect entities with incorrect type. To tackle this problem, we propose to inject latent entity type information into the entity embeddings based on pre-trained BERT. In addition, we integrate a BERT-based entity similarity score into

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Sorokin, Anatoly, Nicolas Le Novère, Augustin Luna, et al. "Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2." Journal of Integrative Bioinformatics 12, no. 2 (2015): 281–339. http://dx.doi.org/10.1515/jib-2015-264.

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Summary The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different asp

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Menon, Ram K., Dattaraya Muzumdar, Abhidha Shah, and Atul Goel. "Blood-fluid level in cortical venous thrombosis—a rare diagnostic entity." Surgical Neurology 71, no. 1 (2009): 111–14. http://dx.doi.org/10.1016/j.surneu.2007.06.093.

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Limon, Onder, Erkan Sahin, Funda Ugur Kantar, Deniz Oray, and Asli Aydinoglu Ugurhan. "A rare entity in ED: Normal lipase level in acute pancreatitis." Turkish Journal of Emergency Medicine 16, no. 1 (2016): 32–34. http://dx.doi.org/10.1016/j.tjem.2014.09.001.

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Huang, Lei, and Yuxin Peng. "Cross-media retrieval by exploiting fine-grained correlation at entity level." Neurocomputing 236 (May 2017): 123–33. http://dx.doi.org/10.1016/j.neucom.2016.07.067.

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Han, Dongchen, Zhaoqian Zheng, Hui Zhao, Shanshan Feng, and Haiting Pang. "Span-based single-stage joint entity-relation extraction model." PLOS ONE 18, no. 2 (2023): e0281055. http://dx.doi.org/10.1371/journal.pone.0281055.

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Extracting entities and relations from the unstructured text has attracted increasing attention in recent years. The existing work has achieved considerable results, yet it is difficult to solve entity overlap and exposure bias. To address cascading errors, exposure bias, and entity overlap in existing entity relation extraction approaches, we propose a joint entity relation extraction model (SMHS) based on a span-level multi-head selection mechanism, transforming entity relation extraction into a span-level multi-head selection problem. Our model uses span-tagger and span-embedding to constru

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Xu, Benfeng, Quan Wang, Yajuan Lyu, Yong Zhu, and Zhendong Mao. "Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14149–57. http://dx.doi.org/10.1609/aaai.v35i16.17665.

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Entities, as the essential elements in relation extraction tasks, exhibit certain structure. In this work, we formulate such entity structure as distinctive dependencies between mention pairs. We then propose SSAN, which incorporates these structural dependencies within the standard self-attention mechanism and throughout the overall encoding stage. Specifically, we design two alternative transformation modules inside each self-attention building block to produce attentive biases so as to adaptively regularize its attention flow. Our experiments demonstrate the usefulness of the proposed entit

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Huang, Wenzhi, Tao Qian, Chen Lyu, et al. "A Multitask Learning Approach for Named Entity Recognition by Exploiting Sentence-Level Semantics Globally." Electronics 11, no. 19 (2022): 3048. http://dx.doi.org/10.3390/electronics11193048.

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Named entity recognition (NER) is one fundamental task in natural language processing, which is usually viewed as a sequence labeling problem and typically addressed by neural conditional random field (CRF) models, such as BiLSTM-CRF. Intuitively, the entity types contain rich semantic information and the entity type sequence in a sentence can globally reflect the sentence-level semantics. However, most previous works recognize named entities based on the feature representation of each token in the input sentence, and the token-level features cannot capture the global-entity-type-related seman

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Liu, Shuang, Man Xu, Yufeng Qin, and Niko Lukač. "Knowledge Graph Alignment Network with Node-Level Strong Fusion." Applied Sciences 12, no. 19 (2022): 9434. http://dx.doi.org/10.3390/app12199434.

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Entity alignment refers to the process of discovering entities representing the same object in different knowledge graphs (KG). Recently, some studies have learned other information about entities, but they are aspect-level simple information associations, and thus only rough entity representations can be obtained, and the advantage of multi-faceted information is lost. In this paper, a novel node-level information strong fusion framework (SFEA) is proposed, based on four aspects: structure, attribute, relation and names. The attribute information and name information are learned first, then s

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Lv, Yana, Jiaqi Tao, and Xiuli Du. "Beyond Isolated Features: Group-Level Feature-Driven Multimodal Fusion for Entity Relationship Extraction." Electronics 14, no. 8 (2025): 1682. https://doi.org/10.3390/electronics14081682.

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Named entity recognition and relation extraction are two crucial techniques in the construction of knowledge graphs, as their performance directly impacts downstream tasks. In scenarios such as social media, where text is short and contains numerous references, relying solely on a single modality often leads to suboptimal entity-relation extraction results. To address this issue, this paper proposes a multimodal entity-relation extraction model. The model incorporates group-level features into visual representations and integrates them into a BERT variant using a dynamic gating mechanism and a

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Mordka, Artur. "A frame as a painting border." Philosophical Discourses 2 (2020): 93–106. http://dx.doi.org/10.16926/pd.2020.02.06.

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The aim of the article is to explain the meaning of a frame as a painting border. Its explanation will be presented on two levels. By taking the first level into consideration I indicate the special place of a frame as a border between entity (reality itself) and non-entity (a painting). The last one is here understood as an object that does not exist in the strict sense of the word “exist”. It means that its frame is the phenomenally given sign of existential negation. As far as the second level is concerned other meaning of a frame will be taken into account: as a border between autonomous a

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Hou, Wenfeng, Qing Liu, and Longbing Cao. "Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge." Applied Sciences 10, no. 14 (2020): 4893. http://dx.doi.org/10.3390/app10144893.

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Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category. In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation. ECKA is a multi-level short text semantic representation mo

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Zhang, Zhao, Fuzhen Zhuang, Hengshu Zhu, Zhiping Shi, Hui Xiong, and Qing He. "Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9612–19. http://dx.doi.org/10.1609/aaai.v34i05.6508.

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The rapid proliferation of knowledge graphs (KGs) has changed the paradigm for various AI-related applications. Despite their large sizes, modern KGs are far from complete and comprehensive. This has motivated the research in knowledge graph completion (KGC), which aims to infer missing values in incomplete knowledge triples. However, most existing KGC models treat the triples in KGs independently without leveraging the inherent and valuable information from the local neighborhood surrounding an entity. To this end, we propose a Relational Graph neural network with Hierarchical ATtention (RGHA

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Xu, Wang, Kehai Chen, and Tiejun Zhao. "Document-Level Relation Extraction with Reconstruction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14167–75. http://dx.doi.org/10.1609/aaai.v35i16.17667.

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In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years. However, the learned graph representation universally models relation information between all entity pairs regardless of whether there are relationships between these entity pairs. Thus, those entity pairs without relationships disperse the attention of the encoder-classifier DocRE for ones with relationships, which may further hind th

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Bansal, Trapit, Pat Verga, Neha Choudhary, and Andrew McCallum. "Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7407–14. http://dx.doi.org/10.1609/aaai.v34i05.6236.

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Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonical concept, and discerning their relationships. These tasks are nearly always viewed as separate components within a pipeline, each requiring a distinct model and training data. While relation extraction can often be trained with readily available weak or distant supervision, entity linkers typically require expensive mention-level supervision – which is not available in many domains. Instead, we propose a model whic

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Heudorfer, Benedikt, Tanja Liesch, and Stefan Broda. "On the challenges of global entity-aware deep learning models for groundwater level prediction." Hydrology and Earth System Sciences 28, no. 3 (2024): 525–43. http://dx.doi.org/10.5194/hess-28-525-2024.

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Abstract. The application of machine learning (ML) including deep learning models in hydrogeology to model and predict groundwater level in monitoring wells has gained some traction in recent years. Currently, the dominant model class is the so-called single-well model, where one model is trained for each well separately. However, recent developments in neighbouring disciplines including hydrology (rainfall–runoff modelling) have shown that global models, being able to incorporate data of several wells, may have advantages. These models are often called “entity-aware models“, as they usually r

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Liu, Yang, Jinpeng Hu, Zhihong Chen, Xiang Wan, and Tsung-Hui Chang. "EASAL: Entity-Aware Subsequence-Based Active Learning for Named Entity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8897–905. http://dx.doi.org/10.1609/aaai.v37i7.26069.

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Active learning is a critical technique for reducing labelling load by selecting the most informative data. Most previous works applied active learning on Named Entity Recognition (token-level task) similar to the text classification (sentence-level task). They failed to consider the heterogeneity of uncertainty within each sentence and required access to the entire sentence for the annotator when labelling. To overcome the mentioned limitations, in this paper, we allow the active learning algorithm to query subsequences within sentences and propose an Entity-Aware Subsequences-based Active Le

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