Including Information Extraction Research Articles

AI for industrial: automate the network design for 5G URLLC services

Fifth generation (5G) mobile networks enable ultra-reliable low-latency communication (URLLC) applications, ushering in an era of endless possibilities for 5G. URLLC supports emerging 5G services and applications with stringent requirements for latency and reliability. Factory automation (FA) is a URLLC application that automates and optimizes workflows and processes in factories. To accommodate diversified FA services, 5G networks employ the “network slicing” technique, which divides the network into slices tailored to different service requirements. Designing a sliced network and translating diversified service-level agreements (SLAs) into network attributes necessitates advanced automation techniques to enhance human–machine collaboration, increase efficiency, minimize manual errors, reduce operating costs, and, most importantly, provide adequate service quality economically and reliably. To apply autonomic computing to FA network design, new architectures and software components have been envisioned. These include information extraction, domain knowledge representation, rule-based reasoning, performance model calculation, and querying using simulators and neural networks (NNs), among others. This paper proposes an innovative approach to network slicing design using advanced automation methods. This approach can be easily extended to include new services or to integrate cutting-edge 5G techniques.

Challenges in AI-supported Process Analysis in the Italian Judicial System: what After Digitalization?

In this commentary article, we outline research challenges and possible directions for the potential applications of AI in the judicial domain by specifically considering process analysis in the Italian context. Applying AI to process analysis poses several challenges, including information extraction from legacy information systems and analysis of legal documents, process modeling with a particular emphasis on temporal analysis, real-time process monitoring, conformance and compliance checking, predictive techniques for accurate predictions, and analysis of judges’ workload. Solutions to these challenges include methods and tools for data identification and collection, innovative approaches to process modeling, reactive techniques for real-time monitoring, conformance checking with explainability, language models adapted to specific domains, and the identification of suitable indicators for the analysis of case handling efficiency and case classification.

Deep Learning-based POS Tagger and Chunker for Odia Language Using Pre-trained Transformers

Developing effective natural language processing (NLP) tools for low-resourced languages poses significant challenges. This article centers its attention on the task of Part-of-speech (POS) tagging and chunking, which pertains to the identification and categorization of linguistic units within sentences. POS tagging and Chunking have already produced positive results in English and other European languages. However, in Indian languages, particularly in Odia language, it is not yet well explored because of the lack of supporting tools, resources, and its complex linguistic morphology. This study presents the building of a manually annotated dataset for Odia phrase chunking task and the development of a deep learning-based model specifically tailored to accommodate the distinctive properties of the language. The process of annotating the Odia chunking corpus involved the utilization of inside-outside-begin labels, which were tagged by using designed Odia chunking tagset. We utilize the constructed Odia chunking dataset to build Odia chunker based on deep learning techniques, employing state-of-the-art architectures. Various techniques, such as Recurrent Neural Networks, Convolutional Neural Networks, and transformer-based models, are investigated to determine the most effective approach for Odia POS tagging and chunking. In addition, we conduct experiments utilizing diverse input representations, including Odia word embeddings, character-level representations, and sub-word units, to effectively capture the complex linguistic characteristics of the Odia language. Numerous experiments are conducted that evaluate the performance of our Odia POS tagger and chunker, employing standard evaluation metrics and making comparisons with existing approaches. The results demonstrate that our transformer-based tagger and chunker achieves superior accuracy and robustness in identifying and categorizing linguistic POS tags and chunks within Odia sentences. It outperforms existing work and exhibits consistent performance across diverse linguistic contexts and sentence structures. The developed Odia POS tagger and chunker have enormous potential for a variety of NLP applications, including information extraction, syntactic parsing, and machine translation, all of which are tailored to the low-resource Odia language. This work contributes to developing NLP tools and technologies for low-resource languages, thereby facilitating enhanced language processing capabilities in various linguistic contexts.

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey.

This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.

Collaborative optimization with PSO for named entity recognition-based applications

Named entity recognition (NER) as a crucial technology is widely used in many application scenarios, including information extraction, information retrieval, text summarization, and machine translation assisted in AI-based smart communication and networking systems. As people pay more and more attention to NER, it has gradually become an independent and important research field. Currently, most of the NER models need to manually adjust their hyper-parameters, which is not only time-consuming and laborious, but also easy to fall into a local optimal situation. To deal with such problem, this paper proposes a machine learning-guided model to achieve NER, where the hyper-parameters of model are automatically adjusted to improve the computational performance. Specifically, the proposed model is implemented by using bi-directional encoder representation from transformers (BERT) and conditional random field (CRF). Meanwhile, the collaborative computing paradigm is also fused in the model, while utilizing the particle swarm optimization (PSO) to automatically search for the best value of hyper-parameters in a collaborative way. The experimental results demonstrate the satisfactory performance of our proposed model.

A full-process intelligent trial system for smart court

In constructing a smart court, to provide intelligent assistance for achieving more efficient, fair, and explainable trial proceedings, we propose a full-process intelligent trial system (FITS). In the proposed FITS, we introduce essential tasks for constructing a smart court, including information extraction, evidence classification, question generation, dialogue summarization, judgment prediction, and judgment document generation. Specifically, the preliminary work involves extracting elements from legal texts to assist the judge in identifying the gist of the case efficiently. With the extracted attributes, we can justify each piece of evidence’s validity by establishing its consistency across all evidence. During the trial process, we design an automatic questioning robot to assist the judge in presiding over the trial. It consists of a finite state machine representing procedural questioning and a deep learning model for generating factual questions by encoding the context of utterance in a court debate. Furthermore, FITS summarizes the controversy focuses that arise from a court debate in real time, constructed under a multi-task learning framework, and generates a summarized trial transcript in the dialogue inspectional summarization (DIS) module. To support the judge in making a decision, we adopt first-order logic to express legal knowledge and embed it in deep neural networks (DNNs) to predict judgments. Finally, we propose an attentional and counterfactual natural language generation (AC-NLG) to generate the court’s judgment.

A new model for coreference resolution based on knowledge representation and multi-criteria ranking

Coreference resolution is critical for improving the performance of all text-based systems including information extraction, document summarization, machine translation, and question-answering. Most of coreference resolution solutions rely on using knowledge resources like lexical knowledge, syntactic knowledge, world knowledge and semantic knowledge. This paper presents a new knowledge-based coreference resolution model using neural network architecture. It uses XLNet embeddings as input and does not rely on any syntactic or dependency parsers. For more efficient span representation and mention detection, we used entity-level information. Mentions were extracted from the text with an unhand engineered mention detector, and the features were extracted from a deep neural network. We also propose a nonlinear multi-criteria ranking model to rank the candidate antecedents. This model simultaneously determines the total score of alternatives and the weight of the features in order to speed up the process of ranking alternatives. Compared to the state-of-the-art models, the simulation results showed significant improvements on the English CoNLL-2012 shared task (+6.4 F1). Moreover, we achieved 96.1% F1 score on the n2c2 medical dataset.

A novel text mining approach for scholar information extraction from web content in Chinese

Text mining is the process of deriving high-quality information from text so that it can focus on extracting useful information from text or web documents. IoT devices generate massive structured or unstructured data including text data. The opportunity coming behind big data and unstructured data is a great impulse for governments or companies to choose solutions based on text mining approaches to improve strategic business activities and boost decision making. Expert information is an important reference information for decision making. How to collect the expert information from text or web documents is a problem. In this paper, a text mining approach is introduced to crawl and extract expert information from Internet. We build a basic framework and main modules including information extraction, data cleaning and deduplication, expert recommendation model to cope with text data from Web content. We also define several metrics, data structures and propose some algorithms to help text mining. Finally, the experiment is implemented with datasets and the results show that our text mining approach can extract expert attributes accurately.

A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.

Comparative Analysis for Topic Classification in Juz Al-Baqarah

<p>In Islam, Quran is the holy book that was revealed to the Prophet Muhammad. It functions as complete code of life for the Muslims. Remarks from Allah which contains more than 77,000 words that was passed down through Prophet Muhammad to the mankind for 23 years started in 610 ce. The Quran was divided into 114 chapters. Arabic language is the original text. The need for the Muslims across the world to find the meaning to understand the content in the Quran is necessary. Nevertheless, understanding the Quran is an interest for the Muslims as well as the attention of millions of people from the faiths. Following the generation, lots of content that related to the Quran has been broadcast by Muslims scholars in the way of the tafsirs, translation and the book of hadiths. Problem has happened at current is most Muslim in Malaysia do not understand sentences in the Quran due to language barrier. The purpose of this research is classified topic in each verses of the Quran sentence based on its specific theme. It involves the objective of text mining which are based on linguistic information and domain. The usage of corpus helps to perform various data mining tasks including information extraction, text categorization, the relationship of concepts, association discovery, the evaluation of pattern and assessed. This research project is aiming to create computing environment that enable us use to text mining the Quran. The classification experiment is using the Support Vector Machine to find themes in Juz’ Baqarah. The SVM performance is then compared against other classification algorithms such as Naive Bayes, J48 Decision Tree and K-Nearest Neighbours. This research project aims at creating an enabling computational environment for text mining the Qur’an and to facilitate users to understand every verse in Juz’ Baqarah.</p>

Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis.

The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHRs). While primarily designed for archiving patient information and performing administrative healthcare tasks like billing, many researchers have found secondary use of these records for various clinical informatics applications. Over the same period, the machine learning community has seen widespread advances in the field of deep learning. In this review, we survey the current research on applying deep learning to clinical tasks based on EHR data, where we find a variety of deep learning techniques and frameworks being applied to several types of clinical applications including information extraction, representation learning, outcome prediction, phenotyping, and deidentification. We identify several limitations of current research involving topics such as model interpretability, data heterogeneity, and lack of universal benchmarks. We conclude by summarizing the state of the field and identifying avenues of future deep EHR research.

Research and development of indoor positioning

Indoor positioning systems have been sufficiently researched to provide location information of persons and devices. This paper is focused on the current research and further development of indoor positioning. The standard evolution and industry development are summarized. There are various positioning systems according to the scenarios, cost and accuracy. However, there is a basic positioning system framework including information extraction, measurement and calculation. In particular, the detailed positioning technologies mainly including cellular positioning and Local Area Network (LAN) positioning are listed and compared to provide a reference for practical applications. Finally, we summarize the challenges of indoor positioning and give a 3-phase evolution route.

Automating the generation of lexical patterns for processing free text in clinical documents.

Many tasks in natural language processing utilize lexical pattern-matching techniques, including information extraction (IE), negation identification, and syntactic parsing. However, it is generally difficult to derive patterns that achieve acceptable levels of recall while also remaining highly precise. We present a multiple sequence alignment (MSA)-based technique that automatically generates patterns, thereby leveraging language usage to determine the context of words that influence a given target. MSAs capture the commonalities among word sequences and are able to reveal areas of linguistic stability and variation. In this way, MSAs provide a systemic approach to generating lexical patterns that are generalizable, which will both increase recall levels and maintain high levels of precision. The MSA-generated patterns exhibited consistent F1-, F.5-, and F2- scores compared to two baseline techniques for IE across four different tasks. Both baseline techniques performed well for some tasks and less well for others, but MSA was found to consistently perform at a high level for all four tasks. The performance of MSA on the four extraction tasks indicates the method's versatility. The results show that the MSA-based patterns are able to handle the extraction of individual data elements as well as relations between two concepts without the need for large amounts of manual intervention. We presented an MSA-based framework for generating lexical patterns that showed consistently high levels of both performance and recall over four different extraction tasks when compared to baseline methods.

Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions

The large number of potential applications from bridging web data with knowledge bases have led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledge base. Potential applications include information extraction, information retrieval, and knowledge base population. However, this task is challenging due to name variations and entity ambiguity. In this survey, we present a thorough overview and analysis of the main approaches to entity linking, and discuss various applications, the evaluation of entity linking systems, and future directions.

Natural language processing systems and Big Data analytics

Natural language processing (NLP) systems have been used in text mining including information extraction, topic models, text summarisation, classification, clustering, and opinion mining, etc. Big Data has great impacts on scientific discoveries and value creation. NLP and Big Data analytics can work together and form powerful systems for handling Big Data at massive scales to discover knowledge, perform text analysis and opinion mining, and facilitate decision-making for business and management. This paper introduces methods of NLP, technologies related to Big Data, and some applications of NLP and Big Data. The challenges of NLP and Big Data are discussed. Some technology progress of NLP and Big Data are also presented.

A Machine Learning Approach to Anaphora Resolution in Arabic

Anaphora resolution is a commonly studied research area of Natural Language Processing (NLP). It is crucial for many application areas of Natural Language Processing including information extraction, question answering and text summarization. Most of the earlier work done in the field of anaphora resolution is for English and other European languages. Arabic language is not sufficiently studied with respect to anaphora resolution and rarely being subjected to machine learning experiments. In this paper we present a machine learning approach to resolve the pronominal anaphora in Arabic language. In this work we determine the appropriate features to be used in this task. We consider a number of classifier namely naive Bayes, K-nearest neighbors and linear logistic regression are employed as base-classifiers for each of the feature sets. In this paper, an in-depth study has been conducted on different of feature sets for exploiting effective features and investigating their effect on performance of the Anaphora resolution. Finally, a wide range of comparative experiments on Quranic datasets are conducted, The experimental results on the Arabic Quran training corpus demonstrate that the proposed method is feasible for the pronominal anaphora resolution of Arabic.

A Generic Web Service for Ad‐hoc Statistical Spatio‐Temporal Aggregation

AbstractFor the analysis of geospatial data, statistical spatio‐temporal aggregation (SSTA) is a frequently used functionality. Possible applications include information extraction, data fusion, generalization and schema transformation. On the basis of spatial and temporal references, SSTA transfers thematic attribute values into a coarser spatio‐temporal resolution using descriptive statistical operations. Reusable geoprocessing functions are increasingly offered in Spatial Data Infrastructures (SDIs) through standardized interfaces using mainstream network technologies. Due to the variety of use cases, a Web service for SSTA promises a high potential for reuse. However, offering functionalities for geoprocessing within an SDI raises some challenges. The aggregation process itself, as well as the encapsulation of the aggregation functionalities within an SDI, require thorough design considerations. This article addresses these challenges by developing a generic framework for SSTA services within an SDI. A prototype of the framework is realized using several open‐source software components. Following a modular approach, the communication between those loosely‐coupled components is enabled through open, standardized interfaces. The result of the proposed work is a framework that enables users to easily perform a statistical ad‐hoc aggregation on distributed spatio‐temporal data.

Subpixel Target Enhancement in Hyperspectral Images

Hyperspectral images due to their higher spectral resolution are increasingly being used for various remote sensing applications including information extraction at subpixel level. Typically whenever an object gets spectrally resolved but not spatially, mixed pixels in the images result. Numerous man made and/or natural disparatetar gets may thus occur inside such mixed pixels giving rise to subpixel target detection problem. Various spectral unmixing models such as linear mixture modeling (LMM) are in vogue to recover components of a mixed pixel. Spectral unmixing outputs both the endmember spectrum and their corresponding a bundance fractions inside the pixel. It, however, does not provide spatial distribution of these abundance fractions within a pixel. This limits the applicability of hyperspectral data for subpixel target detection. In this paper, a new inverse Euclidean distance based super-resolution mapping method has been presented. In this method, the subpixel target detection is performed by adjusting spatial distribution of abundance fraction within a pixel of an hyperspectral image. Results obtainedat different resolutions indicate that super-resolution mapping may effectively be utilized in enhancing the target detection at sub-pixel level. Defence Science Journal, 2013, 63(1), pp.63 -68 , DOI:http://dx.doi.org/10.14429/dsj. 63.3765

Tuffy

Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, and text mining. Current implementations of MLNs do not scale to large real-world data sets, which is preventing their widespread adoption. We present Tuffy that achieves scalability via three novel contributions: (1) a bottom-up approach to grounding that allows us to leverage the full power of the relational optimizer, (2) a novel hybrid architecture that allows us to perform AI-style local search efficiently using an RDBMS, and (3) a theoretical insight that shows when one can (exponentially) improve the efficiency of stochastic local search. We leverage (3) to build novel partitioning, loading, and parallel algorithms. We show that our approach outperforms state-of-the-art implementations in both quality and speed on several publicly available datasets.

A Refinement System for Medical Information Extraction from Text.based Bilingual Electronic Medical Records

Objective: Applications to extract medical information from electronic medical records(EMRs) confront some serious obstacles such as spelling errors, ambiguous abbreviations, and unrecognizable words. Those obstacles hinder the process of finding medical entities, relations, and events. We present an efficient EMR refinement system for the purpose of medical information extraction from EMRs, not just for traditional text error correction. Methods: The EMR refinement system has been designed and implemented through following steps: 1) Build domain constrained dictionary database, 2) Correct spelling errors in Korean-English EMR documents, 3) Resolve ambiguous abbreviations in the bilingual documents. The resulting EMR documents are now machine readable and can be applied to various applications including information extraction. Result: Precision rate of the refinement system for spelling error correction is 80.4% and for disambiguating abbreviations/acronyms is 94.7%. Conclusion: We developed an EMR refinement system to correct spelling errors and resolve ambiguous abbreviations as well as unrecognizable words. Our system can enhance the reliability of medical records and contribute to develop further application systems in the field of text mining and information extraction.