Probabilistic joint models incorporating logic and learning via structured variational approximation for information extraction

Extracting relational triplets aims at detecting entity pairs and their semantic relations. Compared with pipeline models, joint models can reduce error propagation and achieve better performance. However, all of these models require large amounts of training data, therefore performing poorly on many long-tail relations in reality with insufficient data. In this article, we propose a novel end-to-end model, called TGIN, for few-shot triplet extraction. The core of TGIN is a multilayer heterogeneous graph with two types of nodes (entity node and relation node) and three types of edges (relation-entity edge, entity-entity edge, and relation-relation edge). On the one hand, this heterogeneous graph with entities and relations as nodes can intuitively extract relational triplets jointly, thereby reducing error propagation. On the other hand, it enables the triplet information of limited labeled data to interact better, thus maximizing the advantage of this information for few-shot triplet extraction. Moreover, we devise a graph aggregation and update method that utilizes translation algebraic operations to mine semantic features while retaining structure features between entities and relations, thereby improving the robustness of the TGIN in a few-shot setting. After updating the node and edge features through layers, TGIN propagates the label information from a few labeled examples to unlabeled examples, thus inferring triplets from these unlabeled examples. Extensive experiments on three reconstructed datasets demonstrate that TGIN can significantly improve the accuracy of triplet extraction by 2.34%~10.74% compared with the state-of-the-art baselines. To the best of our knowledge, we are the first to introduce a heterogeneous graph for few-shot relational triplet extraction.

DWIE: An entity-centric dataset for multi-task document-level information extraction

Relation Extraction (RE) is one of the fundamental tasks in Information Extraction. The goal of this task is to find the semantic relations between entity mentions in text. It has been shown in many previous work that the structure of the sentences (i.e., dependency trees) can provide important information/features for the RE models. However, the common limitation of the previous work on RE is the reliance on some external parsers to obtain the syntactic trees for the sentence structures. On the one hand, it is not guaranteed that the independent external parsers can offer the optimal sentence structures for RE and the customized structures for RE might help to further improve the performance. On the other hand, the quality of the external parsers might suffer when applied to different domains, thus also affecting the performance of the RE models on such domains. In order to overcome this issue, we introduce a novel method for RE that simultaneously induces the structures and predicts the relations for the input sentences, thus avoiding the external parsers and potentially leading to better sentence structures for RE. Our general strategy to learn the RE-specific structures is to apply two different methods to infer the structures for the input sentences (i.e., two views). We then introduce several mechanisms to encourage the structure and semantic consistencies between these two views so the effective structure and semantic representations for RE can emerge. We perform extensive experiments on the ACE 2005 and SemEval 2010 datasets to demonstrate the advantages of the proposed method, leading to the state-of-the-art performance on such datasets.

Understanding the biology underpinning the natural regeneration of plant species in order to make plans for effective reforestation is a complex task. This can be aided by providing access to databases that contain long-term and wide-scale geographical information on species distribution, habitat, and reproduction. Although there exists widely-used biodiversity databases that contain structured information on species and their occurrences, such as the Global Biodiversity Information Facility (GBIF) and the Atlas of Living Australia (ALA), the bulk of knowledge about biodiversity still remains embedded in textual documents. Unstructured information can be made more accessible and useful for large-scale studies if there are tools and services that automatically extract meaningful information from text and store it in structured formats, e.g., open biodiversity databases, ready to be consumed for analysis (Thessen et al. 2022). We aim to enrich biodiversity occurrence databases with information on species reproductive condition and habitat, derived from text. In previous work, we developed unsupervised approaches to extract related habitats and their locations, and related reproductive condition and temporal expressions (Gabud and Batista-Navarro 2018). We built a new unsupervised hybrid approach for relation extraction (RE), which is a combination of classical rule-based pattern-matching methods and transformer-based language models that framed our RE task as a natural language inference (NLI) task. Using our hybrid approach for RE, we were able to extract related biodiversity entities from text even without a large training dataset. In this work, we implement an information extraction (IE) pipeline comprised of a named entity recognition (NER) tool and our hybrid relation extraction (RE) tool. The NER tool is a transformer-based language model that was pretrained on scientific text and then fine-tuned using COPIOUS (Conserving Philippine Biodiversity by Understanding big data; Nguyen et al. 2019), a gold standard corpus containing named entities relevant to species occurrence. We applied the NER tool to automatically annotate geographical location, temporal expression and habitat information contained within sentences. A dictionary-based approach is then used to identify mentions of reproductive conditions in text (e.g., phrases such as "fruited heavily" and "mass flowering"). We then use our hybrid RE tool to extract reproductive condition - temporal expression and habitat - geographical location entity pairs. We test our IE pipeline on the forestry compendium available in the CABI Digital Library (Centre for Agricultural and Biosciences International), and show that our work enables the enrichment of descriptive information on reproductive and habitat conditions of species. This work is a step towards enhancing a biodiversity database with the inclusion of habitat and reproductive condition information extracted from text.

Although Large language Model (LLM)-powered information extraction (IE) systems have shown impressive capabilities, current fine-tuning paradigms face two major limitations: high training costs and difficulties in aligning with LLM preferences. To address these issues, we propose a novel universal IE paradigm—the Self-Correcting Iterative Refinement (SCIR) framework—along with a Multi-task Bilingual (Chinese-English) Self-Correcting (MBSC) dataset containing over 100,000 entries. The SCIR framework achieves plug-and-play compatibility with existing LLMs and IE systems through its Dual-Path Self-Correcting module and feedback-driven optimization, thereby significantly reducing training costs. Concurrently, the MBSC dataset tackles the challenge of preference alignment by indirectly distilling GPT-4's capabilities into IE result detection models. Experimental results demonstrate that SCIR outperforms state-of-the-art IE methods across three key tasks— named entity recognition, relation extraction, and event extraction—achieving a 5.27 percent average improvement in span-based Micro-F1 while reducing training costs by 87 percent compared to baseline approaches. These advancements not only enhance the flexibility and accuracy of IE systems but also pave the way for lightweight and efficient IE paradigms.

A region-based hypergraph network for joint entity-relation extraction

Entity relation extraction (RE) is an very important research domain in information extraction, we can regard RE as a classification problem in this paper, RE is still original study field in Chinese language now, maximum entropy (ME)-based machine learning is the first time to be used to extract entity relations between named entities from Chinese texts, Thirteen features have been designed for entity relation extraction, which includes morphology, grammar and semantic feature. The system architecture for RE has been constructed. Experiment shows that the performance is promising. So it is useful for ME-based machine learning to solve RE problem

Maximum-likelihood (ML), also given its connection to least-squares (LS), is widely adopted in parameter estimation of physiological system models, i.e., assigning numerical values to the unknown model parameters from the experimental data. A more sophisticated but less used approach is maximum a posteriori (MAP) estimation. Conceptually, while ML adopts a Fisherian approach, i.e., only experimental measurements are supplied to the estimator, MAP estimation is a Bayesian approach, i.e., a priori available statistical information on the unknown parameters is also exploited for their estimation. In this paper, after a brief review of the theory behind ML and MAP estimators, we compare their performance in the solution of a case study concerning the determination of the parameters of a sum of exponential model which describes the impulse response of C-peptide (CP), a key substance for reconstructing insulin secretion. The results show that MAP estimation always leads to parameter estimates with a precision (sometimes significantly) higher than that obtained through ML, at the cost of only a slightly worse fit. Thus, a three exponential model can be adopted to describe the CP impulse response model in place of the two exponential model usually identified in the literature by the ML/LS approach. Simulated case studies are also reported to evidence the importance of taking into account a priori information in a data poor situation, e.g., when a few or too noisy measurements are available. In conclusion, our results show that, when a priori information on the unknown model parameters is available, Bayes estimation can be of relevant interest, since it can significantly improve the precision of parameter estimates with respect to Fisher estimation. This may also allow the adoption of more complex models than those determinable by a Fisherian approach.

In the contemporary digital era, the exponential increase in unstructured and semi-structured data has made information extraction a cornerstone of modern data-driven research and application. The ability to transform such raw information into structured knowledge is crucial for enabling later downstream tasks. While traditional rule-based and statistical approaches to information extraction have demonstrated success in narrow, well-defined tasks, they lack the scalability and adaptability required to address the vastness and variability of present-day data. Conversely, deep neural models and especially large language models have shown remarkable capabilities in language understanding, yet they remain constrained by high computational costs and susceptibility to hallucination. <br> This thesis explores the unification of various symbolic, statistical, and neural paradigms into a cohesive hybrid framework. The central hypothesis is that by combining the strengths of data-driven representation learning with structural, rule-based, and multimodal knowledge, one can achieve information extraction systems that are more accurate, efficient, and reliable than their monolithic counterparts. To test this hypothesis, the thesis investigates a range of hybrid architectures across five key application domains. <br> In the financial domain, a hybrid contradiction detection framework integrates syntactic pre-training with transformer-based representations and clustering algorithms to identify inconsistencies within large-scale financial reports. For named entity recognition, the <em>iNERD</em> algorithm introduces rule-based constraints to guide large language models, producing syntactically valid, hallucination-free entity extractions. Thereafter, the anonymisation study leverages knowledge distillation to compress the language understanding capabilities of large decoder-only models into lightweight encoder-only architectures, enabling secure and efficient text anonymisation. In relation extraction, this work presents <em>KPI-BERT</em> and the open-source <em>KPI-EDGAR</em> dataset, combining contextual embedding models with recurrent layers and noise-based regularisation to extract key performance indicators from financial documents. Extending beyond text, the final empirical contribution introduces a multimodal dementia detection framework that fuses linguistic and acoustic representations, offering a robust approach to early, non-invasive diagnosis. <br> Together, these studies provide compelling evidence that hybrid representation learning constitutes an important paradigm for modern information extraction. This research demonstrates that hybrid systems can achieve higher precision, stronger generalisability, and improved efficiency while remaining adaptable to real-world constraints. The findings of this thesis therefore advance the field towards more trustworthy, sustainable, and application-ready artificial intelligence.

A number of issues related to the application of Bayesian learning techniques to speaker adaptation are investigated. It is shown that the seed models required to construct prior densities to obtain the MAP (maximum a posteriori) estimate can be a speaker-independent (SI) model, a set of female and male models, or even a task-independent acoustic model. Speaker-adaptive training algorithms are shown to be effective in improving the performance of both speaker-dependent and speaker-independent speech recognition systems. The segmental MAP estimation formulation is used to perform adaptive acoustic modeling for speaker adaptation applications. Tested on an RM (resource management) task, it was found that supervised speaker adaptation based on two gender-dependent models gave a better result than that obtained with a single SI seed. Compared with speaker-dependent training, speaker adaptation achieved an equal or better performance with the same amount of training/adaptation data.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Coherent processing of synthetic aperture radar (SAR) data makes SAR images highly susceptible to signal‐dependent granular noise known as speckle. For accurate texture estimation, the maximum a posteriori (MAP) approach is found to be efficient among multitude of despeckling approaches reported in the available literature. However, the formulations of MAP estimator for texture are available in ‘intensity format’. The authors formulate ‐MAP and ‐MAP estimators for texture in ‘amplitude format’, considering clutter to be and distributed. The parameters associated with and distributions are estimated using Mellin transform and second‐kind statistics. Furthermore, an MAP‐based filter is proposed here for mixture of Cauchy–Exponential compound Gaussian (CE‐CG) distributed clutter data where the texture is modelled using CE mixture model. For estimation of CE model parameters, they employ expectation–maximisation (EM) algorithm. Finally, the ‐MAP, ‐MAP, and CE‐MAP estimation of texture both in ‘amplitude format’ and ‘intensity format’ are performed on single look real SAR clutter data and also on multilook synthetic data. Experimental results illustrate the superiority of CE‐MAP filter and moreover that the MAP estimation in ‘amplitude format’ achieves better mean preservation and speckle suppression compared with the same in ‘intensity format’.

Entity identification deals with matching records from different datasets or within one dataset that represent the same real-world entity when unique identifiers are not available. Enabling data integration at record level as well as the detection of duplicates, entity identification plays a major role in data preprocessing, especially concerning data quality. This paper presents a framework for statistical entity identification in particular focusing on probabilistic record linkage and string matching and its implementation in R. According to the stages of the entity identification process, the framework is structured into seven core components: data preparation, candidate selection, comparison, scoring, classification, decision, and evaluation. Samples of real-world CRM datasets serve as illustrative examples.

BackgroundNatural Language Processing (NLP) applications have developed over the past years in various fields including its application to clinical free text for named entity recognition and relation extraction. However, there has been rapid developments the last few years that there's currently no overview of it. Moreover, it is unclear how these models and tools have been translated into clinical practice. We aim to synthesize and review these developments. MethodsWe reviewed literature from 2010 to date, searching PubMed, Scopus, the Association of Computational Linguistics (ACL), and Association of Computer Machinery (ACM) libraries for studies of NLP systems performing general-purpose (i.e., not disease- or treatment-specific) information extraction and relation extraction tasks in unstructured clinical text (e.g., discharge summaries). ResultsWe included in the review 94 studies with 30 studies published in the last three years. Machine learning methods were used in 68 studies, rule-based in 5 studies, and both in 22 studies. 63 studies focused on Named Entity Recognition, 13 on Relation Extraction and 18 performed both. The most frequently extracted entities were “problem”, “test” and “treatment”. 72 studies used public datasets and 22 studies used proprietary datasets alone. Only 14 studies defined clearly a clinical or information task to be addressed by the system and just three studies reported its use outside the experimental setting. Only 7 studies shared a pre-trained model and only 8 an available software tool. DiscussionMachine learning-based methods have dominated the NLP field on information extraction tasks. More recently, Transformer-based language models are taking the lead and showing the strongest performance. However, these developments are mostly based on a few datasets and generic annotations, with very few real-world use cases. This may raise questions about the generalizability of findings, translation into practice and highlights the need for robust clinical evaluation.

Relation extraction is to extract the semantic relation between entity pairs in text, and it is a key point in building Knowledge Graphs and information extraction. The rapid development of deep learning in recent years has resulted in rich research results in relation extraction tasks. At present, the accuracy of relation extraction tasks based on pre-trained language models such as BERT exceeds the methods based on Convolutional or Recurrent Neural Networks. This review mainly summarizes the research progress of pre-trained language models such as BERT in supervised learning and distant supervision relation extraction. In addition, the directions for future research and some comparisons and analyses are discussed in our whole survey. The survey may help readers understand and catch some key techniques about the issue, and identify some future research directions.

In today’s big data era, there are a large number of unstructured information resources on the web. Natural language processing researchers have been working hard to figure out how to extract useful information from them. Entity Relation Extraction is a crucial step in Information Extraction and provides technical support for Knowledge Graphs, Intelligent Q&amp;A systems and Intelligent Retrieval. In this paper, we present a comprehensive history of entity relation extraction and introduce the relation extraction methods based on Machine Mearning, the relation extraction methods based on Deep Learning and the relation extraction methods for open domains. Then we summarize the characteristics and representative results of each type of method and introduce the common datasets and evaluation systems for entity relation extraction. Finally, we summarize current entity relation extraction methods and look forward to future technologies.