23 papers · ranked by Valyu relevance
Xuan Liu, Wanru Du, Xiaoyin Wang, Ruiqun Li + 3 more
'Xiaochuan Jing' 'Sergio Consoli'] Joint extraction from unstructured text aims to extract relational triples composed of entity pairs and their relations. However, most existing works fail to process the overlapping issues that occur when the same entities are utilized to generate different relational triples in a…
Huihui Han, Jian Wang, Xiaowen Wang
The entity relation extraction in the form of triples from unstructured text is a key step for self-learning knowledge graph construction. Two main methods have been proposed to extract relation triples, namely, the pipeline method and the joint learning approach. However, these models do not deal with the overlapping…
Seongsik Park, Harksoo Kim, Željko Stević
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…
Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang + 5 more
'Hong Cheng' 'Wai Lam' 'Ying Shen' 'Ruifeng Xu'] XIAOYAN ZHAO, The Chinese University of Hong Kong, China YANG DENG, National University of Singapore, Singapore MIN YANG∗ , Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China LINGZHI WANG, The Chinese University of Hong Kong, China RUI ZHANG…
Möller, Cedric, Usbeck, Ricardo
This paper introduces a novel method for closed information extraction. The method employs a discriminative approach that incorporates type and entity-specific information to improve relation extraction accuracy, particularly benefiting long-tail relations. Notably, this method demonstrates superior performance…
Yeawon Lee, Jinseok Son, Min Song
The relationship between biomedical entities is complex, and many of them have not yet been identified. For many biomedical research areas including drug discovery, it is of paramount importance to identify the relationships that have already been established through a comprehensive literature survey. However, manually…
Oumaima El Khettari, Daniel Batteux, Solen Quiniou, Samuel Chaffron
Biomedical knowledge curation relies on a variety of Natural Language Processing tasks, including biomedical entity recognition and document-level relation extraction. With the growing size and capabilities of Language Models, effectively deploying them in specific and specialised domains remains a persistent…
Anushka Swarup, Avanti Bhandarkar, Olivia P. Dizon-Paradis, Ronald S. Wilson + 1 more
Challenges and Opportunities Authors: ['Anushka Swarup' 'Avanti Bhandarkar' 'Olivia P. Dizon-Paradis' 'Ronald S. Wilson' 'Damon L. Woodard'] Abstract—Relation extraction is a Natural Language Processing task that aims to extract relationships from textual data. It is a critical step for information extraction. Due to…
Chenghong Sun, Weidong Ji, Guohui Zhou, Hui Guo + 2 more
'Yuqi Yue'] Relation extraction is one of the important steps in building a knowledge graph. Its main objective is to extract semantic relationships from identified entity pairs in sentences, playing a crucial role in semantic understanding and knowledge graph construction. Remote supervised relation extraction aligns…
Roselyn Gabud, Portia Lapitan, Vladimir Mariano, Eduardo Mendoza + 3 more
'Nelson Pampolina' 'Maria Art Antonette Clariño' 'Riza Batista-Navarro'] Introduction Fine-grained, descriptive information on habitats and reproductive conditions of plant species are crucial in forest restoration and rehabilitation efforts. Precise timing of fruit collection and knowledge of species' habitat…
Quim Motger, Xavier Franch
In the context of requirements engineering, relation extraction is the task of documenting the traceability between requirements artefacts. When dealing with textual requirements (i.e., requirements expressed using natural language), relation extraction becomes a cognitively challenging task, especially in terms of…
Michał Olek
This document contains a discussion of the F1 score evaluation used in the article "Relation Classification with Entity Type Restriction" by Shengfei Lyu, Huanhuan Chen published on Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. The authors created a system named RECENT and claim it…
Nicolas Gutehrlé, Iana Atanassova
The task of Information Extraction (IE) involves automatically converting unstructured textual content into structured data. Most research in this field concentrates on extracting all facts or a specific set of relationships from documents. In this paper, we present a method for the extraction and categorisation of an…
Ahmad Aghaebrahimian, Maria Anisimova, Manuel Gil
Automatically extracting relationships from biomedical texts among multiple sorts of entities is an essential task in biomedical natural language processing with numerous applications, such as drug development or repurposing, precision medicine, and other biomedical tasks requiring knowledge discovery. Current Relation…
Christos Theodoropoulos, Andrei Catalin Coman, James Henderson, Marie-Francine Moens
'Marie-Francine Moens'] Background Knowledge discovery in scientific literature is hindered by the increasing volume of publications and the scarcity of extensive annotated data. To tackle the challenge of information overload, it is essential to employ automated methods for knowledge extraction and processing. Finding…
Hasin Rehana, Junguk Hur
Pharmacovigilance relies on accurate extraction of structured biomedical entities and their semantic relationships from scientific literature. However, most biomedical information extraction systems address named entity recognition (NER) and relation extraction as separate tasks trained on corpus-specific…
Mei Yoshikawa, Tadahaya Miuzuno, Yohei Ohto, Hiromi Fujimoto + 1 more
Extracting cell–cell relations from biomedical literature is essential for understanding intercellular communication in immunity, inflammation, and tissue biology. However, cell–cell relation extraction has not been established as a standalone biomedical relation extraction task, and no benchmark corpus or systematic…
Dustin Wright, Anna Lisa Gentile, Noel Faux, Kristen L. Beck
Creating and curating knowledge resources has been a paramount activity in the biomedical domain. In recent years, automated methods for knowledge base construction have flourished and have enabled large scale construction and curation of such resources. In the biological domain, techniques such as next generation…
Timothy E. Chapman, Timo Laßmann
Identifying interactions between biological entities is a cornerstone of molecular research, but assembling such lists from the literature is slow and tedious. For many research questions, no curated database exists, leaving researchers to survey the relevant literature themselves. We present interaction-finder, a tool…
Qianxiang Ai, Fanwang Meng, Jiale Shi, Brenden Pelkie + 1 more
The popularity of data-driven approaches and machine learning (ML) techniques in the field of organic chemistry and its various subfields has increased the value of structured reaction data. Most data in chemistry is represented by unstructured text, and due to the vastness of the organic chemistry literature (papers…
Authors not listed
Natural language processing with the help of large language models such as ChatGPT has become ubiquitous in many software applications and allows users to interact even with complex hardware or software in an intuitive way. The recent concepts of Self-Driving Labs and Material Acceleration Platforms stand to benefit…
Authors not listed
The scarcity and expense of fatigue data limits optimal design of components and constrains companies to a few well qualified materials when safety-critical applications are concerned. This research investigates different strategies to improve extraction of structured information from unstructured scientific…
Authors not listed
The materials-science literature is the richest reservoir of domain knowledge, yet converting its unstructured text—especially narrative passages and complex tables—into machine-readable data for analysis and ML model training remains challenging. To address this, we present KnowMat, an agentic, multi-stage pipeline…