24 papers · ranked by Valyu relevance
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…
Anuj Sharma, Vassilis Virvilis, Tina Lekka, Christos Andronis
The goal of Biomedical relation extraction is to uncover high-quality relations from life science literature with diverse applications in the fields of Biology and Medicine. In the last decade, several methods can be found in published literature ranging from binary to complex relation extraction. In this work, we…
Antonio Jimeno Yepes, David Martinez Iraola, Pieter Barnard, Tinu Theckel Joy
We have created a corpus for the extraction of information related to diagnosis from scientific literature focused on eye diseases. It was shown that the annotation of entities has a relatively large agreement among annotators, which translates into strong performance of the trained methods, mostly BioBERT. Furthermore…
Ji Young Lee, Franck Dernoncourt, Peter Szolovits
Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concepts, such as synonyms and hyponyms. Artificial neural networks have been recently explored for relation extraction. In this work, we continue…
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…
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…
Yifan Peng, Manabu Torii, Cathy H Wu, K Vijay-Shanker
Background Text mining is increasingly used in the biomedical domain because of its ability to automatically gather information from large amount of scientific articles. One important task in biomedical text mining is relation extraction, which aims to identify designated relations among biological entities reported in…
Slavko Žitnik, Marinka Žitnik, Blaž Zupan, Marko Bajec
Background Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To…
Angus Roberts, Robert Gaizauskas, Mark Hepple, Yikun Guo
Background The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order to support clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. One part of this system is the…
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…
Guiduo Duan, Jiayu Miao, Tianxi Huang, Wenlong Luo + 1 more
Relation extraction is a popular subtask in natural language processing (NLP). In the task of entity relation joint extraction, overlapping entities and multi-type relation extraction in overlapping triplets remain a challenging problem. The classification of relations by sharing the same probability space will ignore…
Liyuan Liu, Xiang Ren, Qi Zhu, Shi Zhi + 3 more
'Jiawei Han'] Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHESSION, to conduct relation extractor learning using…
David N. Nicholson, Daniel S. Himmelstein, Casey S. Greene
Knowledge graphs support multiple research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via some form of manual curation, which is difficult to scale in the context of an…
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…
Epaminondas Kapetanios, Vijayan Sugumaran, Anastassia Angelopoulou
Given the recent advances and progress in Natural Language Processing (NLP), extraction of semantic relationships has been at the top of the research agenda in the last few years. This work has been mainly motivated by the fact that building knowledge graphs (KG) and/or bases (KB), as a key ingredient of intelligent…
Nicolas Turenne, Tien Phan
Relation extraction with accurate precision is still a challenge when processing full text databases. We propose an approach based on cooccurrence analysis in each document for which we used document organization to improve accuracy of relation extraction. This approach is implemented in a R package called x.ent.…
Thomas Lavergne, Cyril Grouin, Pierre Zweigenbaum
Background The acquisition of knowledge about relations between bacteria and their locations (habitats and geographical locations) in short texts about bacteria, as defined in the BioNLP-ST 2013 Bacteria Biotope task, depends on the detection of co-reference links between mentions of entities of each of these three…
Andre Lamurias, Luka A. Clarke, Francisco M. Couto
Recent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources…
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…