This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset. The proposed dual-source model outperforms the current state-of-the-art by a large margin. Next, we introduce WikiReading Recycled-a newly developed public dataset and the task of multiple property extraction. It uses the same data as WikiReading but does not inherit its predecessor's identified disadvantages. In addition, we provide a human-annotated test set with diagnostic subsets for a detailed analysis of model performance.
In this paper, we investigate the Dual-source Transformer architecture on the WikiReading information extraction and machine reading comprehension dataset. The proposed model outperforms the current state-of-the-art by a large margin. Next, we introduce WikiReading Recycled - a newly developed public dataset, supporting the task of multiple property extraction. It keeps the spirit of the original WikiReading but does not inherit the identified disadvantages of its predecessor.
L3S Research Center, Leibniz University Hannover（莱布尼茨汉诺威大学L3S研究中心）；Leibniz Information Centre for Science and Technology（莱布尼茨科学技术信息中心）
Information extraction from scholarly articles is a challenging task due to the sizable document length and implicit information hidden in text, figures, and citations. Scholarly information extraction has various applications in exploration, archival, and curation services for digital libraries and knowledge management systems. We present MORTY, an information extraction technique that creates structured summaries of text from scholarly articles. Our approach condenses the article's full-text to property-value pairs as a segmented text snippet called structured summary. We also present a siza…
National University of Singapore（新加坡国立大学）；University of Southern California（南加州大学）；Nanyang Technological University（南洋理工大学）
Entity types and textual context are essential properties for sentence-level relation extraction (RE). Existing work only encodes these properties within individual instances, which limits the performance of RE given the insufficient features in a single sentence. In contrast, we model these properties from the whole dataset and use the dataset-level information to enrich the semantics of every instance. We propose the GRAPHCACHE (Graph Neural Network as Caching) module, that propagates the features across sentences to learn better representations for RE. GRAPHCACHE aggregates the features fro…
Utilizing Large Language Models for Information Extraction from Real Estate Transactions
Rotman School of Management University of Toronto（罗特曼管理学院 东京大学）；ECE Department Carnegie Mellon University（电子工程系 卡内基梅隆大学）；Department of Computer Science San Francisco State University（计算机科学系 旧金山州立大学）；New York University（纽约大学）
Techniques for automatically extracting important content elements from business documents such as contracts, statements, and filings have the potential to make business operations more efficient. This problem can be formulated as a sequence labeling task, and we demonstrate the adaption of BERT to two types of business documents: regulatory filings and property lease agreements. There are aspects of this problem that make it easier than "standard" information extraction tasks and other aspects that make it more difficult, but on balance we find that modest amounts of annotated data (less than…
Distant supervised relation extraction has been successfully applied to large corpus with thousands of relations. However, the inevitable wrong labeling problem by distant supervision will hurt the performance of relation extraction. In this paper, we propose a method with neural noise converter to alleviate the impact of noisy data, and a conditional optimal selector to make proper prediction. Our noise converter learns the structured transition matrix on logit level and captures the property of distant supervised relation extraction dataset. The conditional optimal selector on the other hand…
Relation ties, defined as the correlation and mutual exclusion between different relations, are critical for distant supervised relation extraction. Existing approaches model this property by greedily learning local dependencies. However, they are essentially limited by failing to capture the global topology structure of relation ties. As a result, they may easily fall into a locally optimal solution. To solve this problem, in this paper, we propose a novel force-directed graph based relation extraction model to comprehensively learn relation ties. Specifically, we first build a graph accordin…
Beihang University（北京航空航天大学）；PLA Academy of Military Science（中国人民解放军军事科学院）
Knowledge base provides a potential way to improve the intelligence of information retrieval (IR) systems, for that knowledge base has numerous relations between entities which can help the IR systems to conduct inference from one entity to another entity. Relation extraction is one of the fundamental techniques to construct a knowledge base. Distant supervision is a semi-supervised learning method for relation extraction which learns with labeled and unlabeled data. However, this approach suffers the problem of relation overlapping in which one entity tuple may have multiple relation facts. W…
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
Building document-grounded dialogue systems have received growing interest as documents convey a wealth of human knowledge and commonly exist in enterprises. Wherein, how to comprehend and retrieve information from documents is a challenging research problem. Previous work ignores the visual property of documents and treats them as plain text, resulting in incomplete modality. In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to gene…
We propose an Ontology-Based Information Extraction (OBIE) system to automate the extraction of the criteria and values applied in Land Use Suitability Analysis (LUSA) from bylaw and regulation documents related to the geographic area of interest. The results obtained by our proposed LUSA OBIE system (land use suitability criteria and their values) are presented as an ontology populated with instances of the extracted criteria and property values. This latter output ontology is incorporated into a Multi-Criteria Decision Making (MCDM) model applied for constructing suitability maps for differe…
Beijing University of Posts and Telecommunications（北京邮电大学）
Patent texts contain a large amount of entity information. Through named entity recognition, intellectual property entity information containing key information can be extracted from it, helping researchers to understand the patent content faster. Therefore, it is difficult for existing named entity extraction methods to make full use of the semantic information at the word level brought about by professional vocabulary changes. This paper proposes a method for extracting intellectual property entities based on Transformer and technical word information , and provides accurate word vector repr…
Beihang University（北京航空航天大学）；China Defense Science and Technology Information Center（中国国防科技信息中心）
Connections between relations in relation extraction, which we call class ties, are common. In distantly supervised scenario, one entity tuple may have multiple relation facts. Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction. However, previous models are not effective or ignore to model this property. In this work, to effectively leverage class ties, we propose to make joint relation extraction with a unified model that integrates convolutional neural network (CNN) with a general pairwise ranking framework, in which thr…
Automated Extraction of Material Properties using LLM-based AI Agents
Mehta Family School of Data Science and Artificial Intelligence（梅塔家族数据科学与人工智能学院）；Indian Institute of Technology Roorkee（印度理工学院罗尔基分校）；Department of Metallurgical and Materials Engineering（冶金与材料工程系）
Beyond Text and Tables: Vision-Language Model Integration in ComProScanner for Extracting Materials Data from Scientific Figures with High Accuracy
Energy, Materials and Environment Research Centre, London South Bank University, London SE1 0AA, UK（能源、材料与环境研究中心，伦敦南银行大学）；School of Engineering and Design, London South Bank University, London SE1 0AA, UK（工程与设计学院，伦敦南银行大学）；Bioscience and Bioengineering Research Centre, London South Bank University, London SE1 0AA, UK（生物科学与生物工程研究中心，伦敦南银行大学）；Department of Physics, Kings College London, London WC2R 2LS, UK（物理系，伦敦国王学院）
Large language model-enabled automated data extraction for concrete materials informatics
Department of Civil and Environmental Engineering, Rice University（Rice大学土木与环境工程系）；Rice Advanced Materials Institute, Rice University（Rice大学先进材料研究所）；Ken Kennedy Institute, Rice University（Rice大学肯尼迪研究所）；Independent researcher（独立研究员）
