Relevant bibliographies by topics / Relation extractor

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Relation extractor'

Author: Grafiati
Published: 30 May 2022
Last updated: 19 July 2025

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Journal articles on the topic "Relation extractor"

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Yuan, Yujin, Liyuan Liu, Siliang Tang, et al. "Cross-Relation Cross-Bag Attention for Distantly-Supervised Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 419–26. http://dx.doi.org/10.1609/aaai.v33i01.3301419.

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Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C2SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or m
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Zhang, Congle, Raphael Hoffmann, and Daniel Weld. "Ontological Smoothing for Relation Extraction with Minimal Supervision." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 157–63. http://dx.doi.org/10.1609/aaai.v26i1.8102.

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Relation extraction, the process of converting natural language text into structured knowledge, is increasingly important. Most successful techniques use supervised machine learning to generate extractors from sentences that have been manually labeled with the relations' arguments. Unfortunately, these methods require numerous training examples, which are expensive and time-consuming to produce. This paper presents ontological smoothing, a semi-supervisedtechnique that learns extractors for a set of minimally-labeledrelations. Ontological smoothing has three phases. First, itgenerates a mappin
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Peng, Rao, Litian Huang, and Xinguo Yu. "Solving Arithmetic Word Problems by Synergizing Large Language Model and Scene-Aware Syntax–Semantics Method." Applied Sciences 14, no. 18 (2024): 8184. http://dx.doi.org/10.3390/app14188184.

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Developing Arithmetic Word Problem (AWP) -solving algorithms has recently become one of the hottest research areas because it can simultaneously advance general artificial intelligence and the application of AI technology in education. This paper presents a novel algorithm for solving AWPs by synergizing Large Language Models (LLMs) with the Scene-Aware Syntax–Semantics (S2) method. The innovation of this algorithm lies in leveraging the LLM to divide problems into multiple scenes, thereby enhancing the relation-flow approach in the processes of relation extraction and reasoning. Our algorithm
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Li, Bo, Jiyu Wei, Yang Liu, Yuze Chen, Xi Fang, and Bin Jiang. "Few-Shot Relation Extraction on Ancient Chinese Documents." Applied Sciences 11, no. 24 (2021): 12060. http://dx.doi.org/10.3390/app112412060.

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Traditional humanity scholars’ inefficient method of utilizing numerous unstructured data has hampered studies on ancient Chinese writings for several years. In this work, we aim to develop a relation extractor for ancient Chinese documents to automatically extract the relations by using unstructured data. To achieve this goal, we proposed a tiny ancient Chinese document relation classification (TinyACD-RC) dataset annotated by historians and contains 32 types of general relations in ShihChi (a famous Chinese history book). We also explored several methods and proposed a novel model that works
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Halike, Ayiguli, Aishan Wumaier, and Tuergen Yibulayin. "Zero-Shot Relation Triple Extraction with Prompts for Low-Resource Languages." Applied Sciences 13, no. 7 (2023): 4636. http://dx.doi.org/10.3390/app13074636.

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Although low-resource relation extraction is vital in knowledge construction and characterization, more research is needed on the generalization of unknown relation types. To fill the gap in the study of low-resource (Uyghur) relation extraction methods, we created a zero-shot with a quick relation extraction task setup. Each triplet extracted from an input phrase consists of the subject, relation type, and object. This paper suggests generating structured texts by urging language models to provide related instances. Our model consists of two modules: relation generator and relation and triple
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Kim, Kuekyeng, Yuna Hur, Gyeongmin Kim, and Heuiseok Lim. "GREG: A Global Level Relation Extraction with Knowledge Graph Embedding." Applied Sciences 10, no. 3 (2020): 1181. http://dx.doi.org/10.3390/app10031181.

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In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations—usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level ‘local’ relati
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Cao, Han, Lingwei Wei, Wei Zhou, and Songlin Hu. "Enhancing Multi-Hop Fact Verification with Structured Knowledge-Augmented Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 22 (2025): 23514–22. https://doi.org/10.1609/aaai.v39i22.34520.

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The rapid development of social platforms exacerbates the dissemination of misinformation, which stimulates the research in fact verification. Recent studies tend to leverage semantic features to solve this problem as a single-hop task. However, the process of verifying a claim requires several pieces of evidence with complicated inner logic and relations to verify the given claim in real-world situations. Recent studies attempt to improve both understanding and reasoning abilities to enhance the performance, but they overlook the crucial relations between entities that benefit models to under
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Oliveira Neto, Waldemar de, Antonio Saraiva Muniz, Maria Anita Gonçalves da Silva, Cesar de Castro, and Clovis Manuel Borkert. "Boron extraction and vertical mobility in Paraná State oxisol, Brazil." Revista Brasileira de Ciência do Solo 33, no. 5 (2009): 1259–67. http://dx.doi.org/10.1590/s0100-06832009000500019.

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The deficiency or excess of micronutrients has been determined by analyses of soil and plant tissue. In Brazil, the lack of studies that would define and standardize extraction and determination methods, as well as lack of correlation and calibration studies, makes it difficult to establish limits of concentration classes for analysis interpretation and fertilizer recommendations for crops. A specific extractor for soil analysis is sometimes chosen due to the ease of use in the laboratory and not in view of its efficiency in determining a bioavailable nutrient. The objectives of this study wer
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Zhang, Congle, Stephen Soderland, and Daniel S. Weld. "Exploiting Parallel News Streams for Unsupervised Event Extraction." Transactions of the Association for Computational Linguistics 3 (December 2015): 117–29. http://dx.doi.org/10.1162/tacl_a_00127.

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Most approaches to relation extraction, the task of extracting ground facts from natural language text, are based on machine learning and thus starved by scarce training data. Manual annotation is too expensive to scale to a comprehensive set of relations. Distant supervision, which automatically creates training data, only works with relations that already populate a knowledge base (KB). Unfortunately, KBs such as FreeBase rarely cover event relations ( e.g. “person travels to location”). Thus, the problem of extracting a wide range of events — e.g., from news streams — is an important, open
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Marcheggiani, Diego, and Ivan Titov. "Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations." Transactions of the Association for Computational Linguistics 4 (December 2016): 231–44. http://dx.doi.org/10.1162/tacl_a_00095.

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We present a method for unsupervised open-domain relation discovery. In contrast to previous (mostly generative and agglomerative clustering) approaches, our model relies on rich contextual features and makes minimal independence assumptions. The model is composed of two parts: a feature-rich relation extractor, which predicts a semantic relation between two entities, and a factorization model, which reconstructs arguments (i.e., the entities) relying on the predicted relation. The two components are estimated jointly so as to minimize errors in recovering arguments. We study factorization mod
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Dissertations / Theses on the topic "Relation extractor"

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Філоненко, О. В., Олена Петрівна Черних та Олександр Миколайович Шеін. "Фільтрування інтернет спаму за допомогою обробки природної мови". Thesis, Національний технічний університет "Харківський політехнічний інститут", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/43684.

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Scheible, Silke. "Computational treatment of superlatives." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/4153.

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The use of gradable adjectives and adverbs represents an important means of expressing comparison in English. The grammatical forms of comparatives and superlatives are used to express explicit orderings between objects with respect to the degree to which they possess some gradable property. While comparatives are commonly used to compare two entities (e.g., “The blue whale is larger than an African elephant”), superlatives such as “The blue whale is the largest mammal” are used to express a comparison between a target entity (here, the blue whale) and its comparison set (the set of mammals),
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Hachey, Benjamin. "Towards generic relation extraction." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3978.

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A vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g., PersonW works for OrganisationX, GeneY encodes ProteinZ) and recode it in a format such as a relational database that can be more effectively used for querying and automated reasoning. However, adapting conventional relation extraction systems to new domains or tasks requires significant effort from annotators and developers. Furthermore, previous adaptation approaches based on bootstrapping start from example instances of the target relat
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NUNES, THIAGO RIBEIRO. "BUILDING RELATION EXTRACTORS THROUGH DISTANT SUPERVISION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=21588@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Um problema conhecido no processo de construção de extratores de relações semânticas supervisionados em textos em linguagem natural é a disponibilidade de uma quantidade suficiente de exemplos positivos para um conjunto amplo de relações-alvo. Este trabalho apresenta uma abordagem supervisionada a distância para construção de extratores de relações a um baixo custo combinando duas das maiores fontes de informação estruturada e não estruturada disponíveis na Web, o DBpedia e a
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Minard, Anne-Lyse. "Extraction de relations en domaine de spécialité." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00777749.

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La quantité d'information disponible dans le domaine biomédical ne cesse d'augmenter. Pour que cette information soit facilement utilisable par les experts d'un domaine, il est nécessaire de l'extraire et de la structurer. Pour avoir des données structurées, il convient de détecter les relations existantes entre les entités dans les textes. Nos recherches se sont focalisées sur la question de l'extraction de relations complexes représentant des résultats expérimentaux, et sur la détection et la catégorisation de relations binaires entre des entités biomédicales. Nous nous sommes intéressée aux
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Augenstein, Isabelle. "Web relation extraction with distant supervision." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/13247/.

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Being able to find relevant information about prominent entities quickly is the main reason to use a search engine. However, with large quantities of information on the World Wide Web, real time search over billions of Web pages can waste resources and the end user’s time. One of the solutions to this is to store the answer to frequently asked general knowledge queries, such as the albums released by a musical artist, in a more accessible format, a knowledge base. Knowledge bases can be created and maintained automatically by using information extraction methods, particularly methods to extrac
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Simon, Etienne. "Deep Learning for Unsupervised Relation Extraction." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS198.

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Détecter les relations exprimées dans un texte est un problème fondamental de la compréhension du langage naturel. Il constitue un pont entre deux approches historiquement distinctes de l'intelligence artificielle, celles à base de représentations symboliques et distribuées. Cependant, aborder ce problème sans supervision humaine pose plusieurs problèmes et les modèles non supervisés ont des difficultés à faire écho aux avancées des modèles supervisés. Cette thèse aborde deux lacunes des approches non supervisées : le problème de la régularisation des modèles discriminatifs et le problème d'ex
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Yeh, Hui-Syuan. "Prompt-based Relation Extraction for Pharmacovigilance." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG097.

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L'extraction de connaissances à jour à partir de sources textuelles diverses est importante pour la santé publique. Alors que les sources professionnelles, notamment les revues scientifiques et les notes cliniques, fournissent les connaissances les plus fiables, les observations apportées dans les forums de patients et les médias sociaux permettent d'obtenir des informations complémentaires pour certains thèmes. Détecter les entités et leurs relations dans ces sources variées est particulièrement précieux. Nous nous concentrons sur l'extraction de relations dans le domaine médical. Nous commen
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Jean-Louis, Ludovic. "Approches supervisées et faiblement supervisées pour l’extraction d’événements et le peuplement de bases de connaissances." Thesis, Paris 11, 2011. http://www.theses.fr/2011PA112288/document.

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La plus grande partie des informations disponibles librement sur le Web se présentent sous une forme textuelle, c'est-à-dire non-structurée. Dans un contexte comme celui de la veille, il est très utile de pouvoir présenter les informations présentes dans les textes sous une forme structurée en se focalisant sur celles jugées pertinentes vis-à-vis du domaine d'intérêt considéré. Néanmoins, lorsque l'on souhaite traiter ces informations de façon systématique, les méthodes manuelles ne sont pas envisageables du fait du volume important des données à considérer.L'extraction d'information s'inscrit
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Afzal, Naveed. "Unsupervised relation extraction for e-learning applications." Thesis, University of Wolverhampton, 2011. http://hdl.handle.net/2436/299064.

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In this modern era many educational institutes and business organisations are adopting the e-Learning approach as it provides an effective method for educating and testing their students and staff. The continuous development in the area of information technology and increasing use of the internet has resulted in a huge global market and rapid growth for e-Learning. Multiple Choice Tests (MCTs) are a popular form of assessment and are quite frequently used by many e-Learning applications as they are well adapted to assessing factual, conceptual and procedural information. In this thesis, we pre
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Books on the topic "Relation extractor"

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Hudson, R. A. Extraction and grammatical relations. The author, 1987.

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Xu, Fei-Yu. Bootstrapping relation extraction from semantic seeds. German Research Center for Artificial Intelligence, 2008.

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Petrucci, Alessandra, and Rosanna Verde, eds. SIS 2017. Statistics and Data Science: new challenges, new generations. Firenze University Press, 2017. http://dx.doi.org/10.36253/978-88-6453-521-0.

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The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from
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1976-, Lawn Katherine E., and Salvucci Claudio R, eds. Women in New France: Extracts from the Jesuit relations. Evolution Pub., 2005.

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R, Salvucci Claudio, ed. American languages in New France: Extracts from The Jesuit relations. Evolution Pub., 2002.

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Dinh, Ly. Text-Based Network Construction of Crisis Response Networks Using Entity and Relation Extraction. SAGE Publications Ltd, 2025. https://doi.org/10.4135/9781036217280.

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1971-, Schiavo Anthony P., and Salvucci Claudio R, eds. Iroquois wars: Extracts from the Jesuit relations and other primary sources. Evolution Pub., 2003.

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Rietbergen, Simon. Conservation concerns relating to the diversification of species extracted for timber. The Institute, 1991.

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Higginson, Francis. Extracts from Francis Higginson: A brief relation of the irreligion of the northern Quakers. E. Warren, 1999.

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Botero, Luz Victoria Calle. La familia: Extracto de su historia, estructura, funciones, elementos esenciales y problemática jurídica. Editorial Kelly, 1985.

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Book chapters on the topic "Relation extractor"

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Tang, Wei, Zexin Wang, Xun Mao, et al. "Relation Inquiry: A Novel Synchronous Joint Extractor for Entities and Relations." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1809-5_12.

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Cai, Hua, Qing Xu, and Weilin Shen. "Complex Relative Position Encoding for Improving Joint Extraction of Entities and Relations." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_66.

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AbstractRelative position encoding (RPE) is important for transformer based pretrained language model to capture sequence ordering of input tokens. Transformer based model can detect entity pairs along with their relation for joint extraction of entities and relations. However, prior works suffer from the redundant entity pairs, or ignore the important inner structure in the process of extracting entities and relations. To address these limitations, in this paper, we first use BERT with complex relative position encoding (cRPE) to encode the input text information, then decompose the joint extraction task into two interrelated subtasks, namely head entity extraction and tail entity relation extraction. Owing to the excellent feature representation and reasonable decomposition strategy, our model can fully capture the semantic interdependence between different steps, as well as reduce noise from irrelevant entity pairs. Experimental results show that the F1 score of our method outperforms previous baseline work, achieving a better result on NYT-multi dataset with F1 score of 0.935.
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Rossiello, Gaetano, Alfio Gliozzo, Nicolas Fauceglia, and Giovanni Semeraro. "Latent Relational Model for Relation Extraction." In The Semantic Web. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21348-0_19.

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Denecke, Kerstin. "Relation Extraction." In Health Web Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20582-3_9.

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Castelli, Vittorio, and Imed Zitouni. "Relation Extraction." In Natural Language Processing of Semitic Languages. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45358-8_9.

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Devarakonda, Murthy V., Kalpana Raja, and Hua Xu. "Relation Extraction." In Cognitive Informatics in Biomedicine and Healthcare. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55865-8_5.

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Gromann, Dagmar, Lennart Wachowiak, Christian Lang, and Barbara Heinisch. "Extracting Terminological Concept Systems from Natural Language Text." In European Language Grid. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17258-8_18.

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AbstractTerminology denotes a language resource that structures domain-specific knowledge by means of conceptual grouping of terms and their interrelations. Such structured domain knowledge is vital to various specialised communication settings, from corporate language to crisis communication. However, manually curating a terminology is both labour- and time-intensive. Approaches to automatically extract terminology have focused on detecting domain-specific single- and multi-word terms without taking terminological relations into consideration, while knowledge extraction has specialised on named entities and their relations. We present the Text2TCS method to extract single- and multi-word terms, group them by synonymy, and interrelate these groupings by means of a pre-specified relation typology to generate a Terminological Concept System (TCS) from domain-specific text in multiple languages. To this end, the method relies on pre-trained neural language models.
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Soni, Ameet, Dileep Viswanathan, Jude Shavlik, and Sriraam Natarajan. "Learning Relational Dependency Networks for Relation Extraction." In Inductive Logic Programming. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63342-8_7.

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Paaß, Gerhard, and Sven Giesselbach. "Foundation Models for Information Extraction." In Artificial Intelligence: Foundations, Theory, and Algorithms. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23190-2_5.

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AbstractIn the chapter we consider Information Extraction approaches that automatically identify structured information in text documents and comprise a set of tasks. The Text Classification task assigns a document to one or more pre-defined content categories or classes. This includes many subtasks such as language identification, sentiment analysis, etc. The Word Sense Disambiguation task attaches a predefined meaning to each word in a document. The Named Entity Recognition task identifies named entities in a document. An entity is any object or concept mentioned in the text and a named entity is an entity that is referred to by a proper name. The Relation Extraction task aims to identify the relationship between entities extracted from a text. This covers many subtasks such as coreference resolution, entity linking, and event extraction. Most demanding is the joint extraction of entities and relations from a text. Traditionally, relatively small Pre-trained Language Models have been fine-tuned to these task and yield high performance, while larger Foundation Models achieve high scores with few-shot prompts, but usually have not been benchmarked.
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Rendle, Steffen, Christine Preisach, and Lars Schmidt-Thieme. "Learning to Extract Relations for Relational Classification." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_114.

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Conference papers on the topic "Relation extractor"

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Hogan, William P., and Jingbo Shang. "Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.165.

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Han, Xu, Tianyu Gao, Yankai Lin, et al. "More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction." In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.aacl-main.75.

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Liu, Lihan, and Pengfei Li. "Transformer with Local-feature Extractor for Relation Extraction." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534183.

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Yang, Huifan, Da-Wei Li, Zekun Li, Donglin Yang, and Bin Wu. "Open Relation Extraction with Non-existent and Multi-span Relationships." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/37.

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Open relation extraction (ORE) aims to assign semantic relationships among arguments, essential to the automatic construction of knowledge graphs (KG). The previous ORE methods and some benchmark datasets consider a relation between two arguments as definitely existing and in a simple single-span form, neglecting possible non-existent relationships and flexible, expressive multi-span relations. However, detecting non-existent relations is necessary for a pipelined information extraction system (first performing named entity recognition then relation extraction), and multi-span relationships co
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Yang, Dongdong, Senzhang Wang, and Zhoujun Li. "Ensemble Neural Relation Extraction with Adaptive Boosting." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/630.

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Relation extraction has been widely studied to extract new relational facts from open corpus. Previous relation extraction methods are faced with the problem of wrong labels and noisy data, which substantially decrease the performance of the model. In this paper, we propose an ensemble neural network model - Adaptive Boosting LSTMs with Attention, to more effectively perform relation extraction. Specifically, our model first employs the recursive neural network LSTMs to embed each sentence. Then we import attention into LSTMs by considering that the words in a sentence do not contribute equall
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Yu, Bowen, Zhenyu Zhang, Tingwen Liu, Bin Wang, Sujian Li, and Quangang Li. "Beyond Word Attention: Using Segment Attention in Neural Relation Extraction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/750.

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Relation extraction studies the issue of predicting semantic relations between pairs of entities in sentences. Attention mechanisms are often used in this task to alleviate the inner-sentence noise by performing soft selections of words independently. Based on the observation that information pertinent to relations is usually contained within segments (continuous words in a sentence), it is possible to make use of this phenomenon for better extraction. In this paper, we aim to incorporate such segment information into neural relation extractor. Our approach views the attention mechanism as lin
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Zhang, Yang, Hao Bai, Yuan Xu, Yanlin He, Qunxiong Zhu, and Hao Sheng. "A Dual Relation Extractor for Object Detection." In 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2023. http://dx.doi.org/10.1109/ictai59109.2023.00147.

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Takahashi, Hideharu, Hiroshige Kikura, Kenji Takeshita, and Masanori Aritomi. "Visualization of Dispersed Phase Flow in Centrifugal Extractor Using Taylor-Couette Vortex Flow." In ASME/JSME 2011 8th Thermal Engineering Joint Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ajtec2011-44403.

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For studying the designs and running operations of an extractor which uses Taylor-Couette vortex flow, we focused on a metal extraction system as one of the extraction models of heat generating nuclides and observed the flow patterns of dispersed phase by dyeing the phase in the extractor, and we investigated the effects of hydrophobic coating applied to the inner cylinder surface on the flow characteristics. Moreover, for the quantitative measurement and analysis of the flow field, we evaluated the applicability of Ultrasonic Velocity Profiler (UVP) to flow field measurement. Thorough these v
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Chen, Xiang, Ningyu Zhang, Lei Li, et al. "Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction." In Findings of the Association for Computational Linguistics: NAACL 2022. Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-naacl.121.

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Ding, Gang, Yu Wu, Jiabao Jin, Hao Qi, Yinran Chen, and Xiongbiao Luo. "Towards A Relation Extractor Nested U-Architecture for Accurate Pulmonary Airway Segmentation in CT images." In ICCCV 2022: 2022 The 5th International Conference on Control and Computer Vision. ACM, 2022. http://dx.doi.org/10.1145/3561613.3561618.

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Reports on the topic "Relation extractor"

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Do, Quang X. Background Knowledge in Learning-Based Relation Extraction. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada565270.

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Kupina, Steve, Mark Kelm, Maria Monagas, and STEFAN GAFNER. Grape Seed Extract Laboratory Guidance Document. ABC-AHP-NCNPR Botanical Adulterants Prevention Program, 2019. http://dx.doi.org/10.59520/bapp.lgd/dozo2637.

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Grape Seed Extract (GSE) has received acceptance almost globally as an ingredient for human consumption. It is one of the more widely used botanical extracts, due to increasing scientific findings supporting health benefits. However, it remains a specialty item relative to global commodities. In the United States, GSE has ranked among the top 20 best-selling dietary supplements in the Food, Drug and Mass Market channel. The motivation behind purposeful adulteration in commercial products is financial gain (also known as economically motivated adulteration) and to increase the concentration in
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Ma, Yue, and Felix Distel. Learning Formal Definitions for Snomed CT from Text. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.193.

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Snomed CT is a widely used medical ontology which is formally expressed in a fragment of the Description Logic EL++. The underlying logics allow for expressive querying, yet make it costly to maintain and extend the ontology. Existing approaches for ontology generation mostly focus on learning superclass or subclass relations and therefore fail to be used to generate Snomed CT definitions. In this paper, we present an approach for the extraction of Snomed CT definitions from natural language texts, based on the distance relation extraction approach. By benefiting from a relatively large amount
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Ward, Katrina, Jonathan Bisila, and Kelsey Cairns. Survey of Current State of the Art Entity-Relation Extraction Tools. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1630263.

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Ward, Katrina, Jonathan Bisila, and Kelsey Cairns. Survey of Current State of the Art Entity-Relation Extraction Tools. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1662019.

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Dorr, Bonnie, and Terry Gaasterland. Summarization-Inspired Temporal-Relation Extraction: Tense-Pair Templates and Treebank-3 Analysis. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada460392.

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Powell, Kimonique. Strengthening Caribbean-EU Economic and Trade Relations Post-COVID. Commonwealth Secretariat, 2021. https://doi.org/10.14217/comsec.346.

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This issue of the Commonwealth’s Trade Hot Topics takes stock of Caribbean-EU relations to date with the intent of assessing what recent developments signal for their economic relations in the future. It begins by revisiting the historical linkages between the two regions and how their partnership evolved from Lomé to the Cotonou agreement and the EPA. Thereafter, it examines the implications of Brexit and the new EU-OACPS Partnership Agreement for future economic relations. It concludes with policy recommendations for Caribbean countries to strengthen and extract value from their partnership
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Cardellina II, John. Turmeric Raw Material and Products Laboratory Guidance Document. ABC-AHP-NCNPR Botanical Adulterants Prevention Program, 2020. http://dx.doi.org/10.59520/bapp.lgd/wcyh6498.

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Turmeric (Curcuma longa L.) dietary supplements, including standardized or partially purified extracts with high concentrations of curcumin, have enjoyed sustained sales growth in the United States over the past 5-6 years, while turmeric powder continues to be an important spice, flavor, and colorant in many regions of the world. There is considerable evidence that both powdered root and rhizome, as well as root and rhizome extracts, have been subjected to adulteration. This document should be viewed in relation to the corresponding Botanical Adulterants Prevention Bulletin on turmeric publish
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Dioguardi, Mario, and Diego Sovereto. Application of the Extracts of Pomegranate in Oral Cancer. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.9.0027.

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Review question / Objective: In this scoping review we will focus on identifying those studies that investigated the effects of Punica granatum extracts on oral cancer and more specifically on OSCCs, summarizing the main results and the state of the research at the present time. Eligibility criteria: All studies investigating Punica granatum L. in association with oral and precancerous cancer were considered potentially admissible, no restrictions were applied in relation to the year of publication and based on the language provided that an abstract in English is available. literature reviews
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Adam, Gaelen P., Melinda Davies, Jerusha George, et al. Machine Learning Tools To (Semi-) Automate Evidence Synthesis. Agency for Healthcare Research and Quality (AHRQ), 2025. https://doi.org/10.23970/ahrqepcwhitepapermachine.

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Introduction. Tools that leverage machine learning, a subset of artificial intelligence, are becoming increasingly important for conducting evidence synthesis as the volume and complexity of primary literature expands exponentially. In response, we have created a living rapid review and evidence map to understand existing research and identify available tools. Methods. We searched PubMed, Embase, and the ACM Digital Library from January 1, 2021, to April 3, 2024, for comparative studies, and identified older studies using the reference lists of existing evidence synthesis products (ESPs). We p
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