Articles
Information Sciences (00200255)692
Open relation extraction is a critical task in natural language processing aimed at automatically extracting relations between entities in open-domain corpora. Most existing systems focus on extracting binary relations (relations between two entities) while extracting more complex n-ary relations (involving more than two entities) remains a significant challenge. Additionally, many previous systems rely on hand-crafted patterns and natural language processing tools, which result in error accumulation and reduced accuracy. The current study proposes a novel approach to open n-ary relation extraction that leverages recent advancements in deep learning architectures. This approach addresses the limitations of existing open relation extraction systems, particularly their reliance on hand-crafted patterns and their focus on binary relations. It utilizes SpanBERT to capture relational patterns from text data directly and introduces entity embedding vectors to create distinct representations of entities within sentences. These vectors enhance the proposed system's understanding of the entities within the input sentence, leading to more accurate relation extraction. Notably, the proposed system in the present study achieves an F1-score of 89.79 and 92.67 on the LSOIE-wiki and OpenIE4 datasets, outperforming the best existing models by over 12% and 10%, respectively. These results highlight the effectiveness of the proposed approach in addressing the challenges of open n-ary relation extraction. © 2024 Elsevier Inc.
Neurocomputing (09252312)616
Open Information Extraction (Open IE) is the task of identifying structured and machine-readable information from natural language text within an open domain context. This research area has gained significant importance in the field of natural language processing (NLP), attracting considerable attention for its potential to extract valuable information from unstructured textual data. Previous investigations heavily relied on manual extraction patterns and various NLP tools. While these methods often produce errors that accumulate and propagate throughout the systems, ultimately affecting the accuracy of the results. Moreover, recent Open IE studies have focused on extracting binary relations involving two entities. However, these binary approaches occasionally lead to the omission of essential information in the text, preventing a deeper comprehension of the content. This limitation arises from the fact that real-world relations often involve multiple entities, but binary approaches may oversimplify these relations and miss additional details crucial for a thorough understanding of text. To address these challenges, our study introduces an innovative system called “Open N-ary Information EXtraction (ONIEX).” This system incorporates two novel techniques: multihead relation attention mechanism and relation embedding. Multihead relation attention, in combination with relation embedding, enables the system to focus on relations extracted through the SpanBERT model and accurately identify associated entities for each relation. The ONIEX system's superior performance is substantiated through extensive experiments conducted on the OpenIE4 and LSOIE datasets, benchmark datasets for Open n-ary Information Extraction (Open n-ary IE). The results demonstrate the superiority of the ONIEX system over the existing state-of-the-art systems. © 2024 Elsevier B.V.
Expert Systems with Applications (09574174)275
Entity search websites enable consumers to purchase products and entities. These websites commonly provide predefined search filters and contain entity information and user-created reviews. Consumers commonly exploit filters to eliminate irrelevant entities. Consequently, they explore reviews about the purified entities to identify an entity centered on their preferences. Inspired by this consumer-centric process, this paper introduces a mechanized model for entity search. In the presented model, consumer preferences are considered as queries comprising configurations for predefined filters and a textual part containing more explanations and constrains. Previous works on entity retrieval have not employed capabilities of structured entity information, and complete potential of insights in user reviews. In contrast, the presented model, considering this limitations, exploits structured entity information of predefined_slots to filter out many irrelevant entities. Consequently, it ranks entities by comparing text_part of consumer preferences with the entire core content of user reviews, indicating the order of corresponding entities centered on consumer preferences. The entity search model presented in this paper employs cutting-edge natural language processing (NLP) methods. Moreover, this paper introduces a model to curate entity search dataset, considering structured entity information and objective information in user reviews and exploit it to create a dataset by extracting and labeling restaurant information from Tripadvisor. The created entity search model incorporates a monoBERT-based text_ranker, fine-tuned employing the created training dataset, and evaluations indicate perceptible improvements in mean reciprocal rank (MRR), mean average precision (MAP), and normalized discounted cumulative gain (nDCG). © 2025 Elsevier Ltd
PLoS ONE (19326203)20(5 May)
The extraction of subjective comparative relations is essential in the field of question answering systems, playing a crucial role in accurately interpreting and addressing complex questions. To tackle this challenge, we propose the SCQRE model, specifically designed to extract subjective comparative relations from questions by focusing on entities, aspects, constraints, and preferences. Our approach leverages multi-task learning, the Natural Language Inference (NLI) paradigm, and a specialized adapter integrated into RoBERTa_base_go_emotions to enhance performance in Element Extraction (EE), Compared Elements Identification (CEI), and Comparative Preference Classification (CPC). Key innovations include handling X- and XOR-type preferences, capturing implicit comparative nuances, and the robust extraction of constraints often neglected in existing models. We also introduce the Smartphone-SCQRE dataset, along with another domain-specific dataset, BrandsCompSent-19-SCQRE, both structured as subjective comparative questions. Experimental results demonstrate that our model outperforms existing approaches across multiple question-level and sentence-level datasets and surpasses recent language models, such as GPT-3.5-turbo-0613, Llama-2-70b-chat, and Qwen-1.5-7B-Chat, showcasing its effectiveness in question-based comparative relation extraction. © 2025 Babaali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
