Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and ...Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and events—while also involving reasoning tasks like personnel classification,relationship judgment,and implicit inference.Moreover,utilizing models for extracting information from police incident data poses a significant challenge—data scarcity,which limits the effectiveness of traditional rule-based and machine-learning methods.To address these,we propose TIPS.In collaboration with public security experts,we used de-identified police incident data to create templates that enable large language models(LLMs)to populate data slots and generate simulated data,enhancing data density and diversity.We then designed schemas to efficiently manage complex extraction and reasoning tasks,constructing a high-quality dataset and fine-tuning multiple open-source LLMs.Experiments showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%,nearly 30%higher than the base model,significantly reducing error rates.Manual corrections further improved performance by 9.39%.This study demonstrates that combining largescale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments,offering a new approach for intelligent public security applications.展开更多
Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved inform...Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved information retrieval efficiency to a certain extent,they exhibit significant limitations in adaptability and accuracy when dealing with procurement documents characterized by diverse formats and a high degree of unstructured content.The emergence of Large Language Models(LLMs)offers new possibilities for efficient procurement information processing and extraction.Methods:This study collected procurement transaction documents from public procurement websites,and proposed a procurement Information Extraction(IE)method based on LLMs.Unlike traditional approaches,this study systematically explores the applicability of LLMs in both structured and unstructured entities in procurement documents,addressing the challenges posed by format variability and content complexity.Furthermore,an optimized prompt framework tailored for procurement document extraction tasks is developed to enhance the accuracy and robustness of IE.The aim is to process and extract key information from medical device procurement quickly and accurately,meeting stakeholders'demands for precision and timeliness in information retrieval.Results:Experimental results demonstrate that,compared to traditional methods,the proposed approach achieves an F1 Score of 0.9698,representing a 4.85%improvement over the best baseline model.Moreover,both recall and precision rates are close to 97%,significantly outperforming other models and exhibiting exceptional overall recognition capabilities.Notably,further analysis reveals that the proposed method consistently maintains high performance across both structured and unstructured entities in procurement documents while balancing recall and precision effectively,demonstrating its adaptability in handling varying document formats.The results of ablation experiments validate the effectiveness of the proposed prompting strategy.Conclusion:Additionally,this study explores the challenges and potential improvements of the proposed method in IE tasks and provides insights into its feasibility for real-world deployment and application directions,further clarifying its adaptability and value.This method not only exhibits significant advantages in medical device procurement but also holds promise for providing new approaches to information processing and decision support in various domains.展开更多
The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-hei...The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.展开更多
Information extraction plays a vital role in natural language processing,to extract named entities and events from unstructured data.Due to the exponential data growth in the agricultural sector,extracting significant...Information extraction plays a vital role in natural language processing,to extract named entities and events from unstructured data.Due to the exponential data growth in the agricultural sector,extracting significant information has become a challenging task.Though existing deep learningbased techniques have been applied in smart agriculture for crop cultivation,crop disease detection,weed removal,and yield production,still it is difficult to find the semantics between extracted information due to unswerving effects of weather,soil,pest,and fertilizer data.This paper consists of two parts.An initial phase,which proposes a data preprocessing technique for removal of ambiguity in input corpora,and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer andmultilayer perceptron to find agricultural-based named entity recognition,events,and relations between them.The proposed algorithm has been trained and tested on four input corpora i.e.,agriculture,weather,soil,and pest&fertilizers.The experimental results have been compared with existing techniques and itwas observed that the proposed algorithm outperformsWeighted-SOM,LSTM+RAO,PLR-DBN,KNN,and Na飗e Bayes on standard parameters like accuracy,sensitivity,and specificity.展开更多
Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, whic...Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.展开更多
Synthetic aperture radar （SAR） provides a large amount of image data for the observation and research of oceanic eddies. The use of SAR images to automatically depict the shape of eddies and extract the eddy informa...Synthetic aperture radar （SAR） provides a large amount of image data for the observation and research of oceanic eddies. The use of SAR images to automatically depict the shape of eddies and extract the eddy information is of great significance to the study of the oceanic eddies and the application of SAR eddy images. In this paper, a method of automatic shape depiction and information extraction for oceanic eddies in SAR images is proposed, which is for the research of spiral eddies. Firstly, the skeleton image is got by the skeletonization of SAR image. Secondly, the logarithmic spirals detected in the skeleton image are drawn on the SAR image to depict the shape of oceanic eddies. Finally, the eddy information is extracted based on the results of shape depiction. The sentinel 1 SAR eddy images in the Black Sea area were used for the experiment in this paper. The experimental results show that the proposed method can automatically depict the shape of eddies and extract the eddy information. The shape depiction results are consistent with the actual shape of the eddies, and the extracted eddy information is consistent with the reference information extracted by manual operation. As a result, the validity of the method is verified.展开更多
A two-step information extraction method is presented to capture the specific index-related information more accurately.In the first step,the overall process variables are separated into two sets based on Pearson corr...A two-step information extraction method is presented to capture the specific index-related information more accurately.In the first step,the overall process variables are separated into two sets based on Pearson correlation coefficient.One is process variables strongly related to the specific index and the other is process variables weakly related to the specific index.Through performing principal component analysis(PCA)on the two sets,the directions of latent variables have changed.In other words,the correlation between latent variables in the set with strong correlation and the specific index may become weaker.Meanwhile,the correlation between latent variables in the set with weak correlation and the specific index may be enhanced.In the second step,the two sets are further divided into a subset strongly related to the specific index and a subset weakly related to the specific index from the perspective of latent variables using Pearson correlation coefficient,respectively.Two subsets strongly related to the specific index form a new subspace related to the specific index.Then,a hybrid monitoring strategy based on predicted specific index using partial least squares(PLS)and T2statistics-based method is proposed for specific index-related process monitoring using comprehensive information.Predicted specific index reflects real-time information for the specific index.T2statistics are used to monitor specific index-related information.Finally,the proposed method is applied to Tennessee Eastman(TE).The results indicate the effectiveness of the proposed method.展开更多
Analyzing Research and Development(R&D)trends is important because it can influence future decisions regarding R&D direction.In typical trend analysis,topic or technology taxonomies are employed to compute the...Analyzing Research and Development(R&D)trends is important because it can influence future decisions regarding R&D direction.In typical trend analysis,topic or technology taxonomies are employed to compute the popularities of the topics or codes over time.Although it is simple and effective,the taxonomies are difficult to manage because new technologies are introduced rapidly.Therefore,recent studies exploit deep learning to extract pre-defined targets such as problems and solutions.Based on the recent advances in question answering(QA)using deep learning,we adopt a multi-turn QA model to extract problems and solutions from Korean R&D reports.With the previous research,we use the reports directly and analyze the difficulties in handling them using QA style on Information Extraction(IE)for sentence-level benchmark dataset.After investigating the characteristics of Korean R&D,we propose a model to deal with multiple and repeated appearances of targets in the reports.Accordingly,we propose a model that includes an algorithm with two novel modules and a prompt.A newly proposed methodology focuses on reformulating a question without a static template or pre-defined knowledge.We show the effectiveness of the proposed model using a Korean R&D report dataset that we constructed and presented an in-depth analysis of the benefits of the multi-turn QA model.展开更多
Traditional pattern representation in information extraction lack in the ability of representing domain-specific concepts and are therefore devoid of flexibility. To overcome these restrictions, an enhanced pattern re...Traditional pattern representation in information extraction lack in the ability of representing domain-specific concepts and are therefore devoid of flexibility. To overcome these restrictions, an enhanced pattern representation is designed which includes ontological concepts, neighboring-tree structures and soft constraints. An information-(extraction) inference engine based on hypothesis-generation and conflict-resolution is implemented. The proposed technique is successfully applied to an information extraction system for Chinese-language query front-end of a job-recruitment search engine.展开更多
Information extraction techniques on the Web are the current research hotspot. Now many information extraction techniques based on different principles have appeared and have different capabilities. We classify the ex...Information extraction techniques on the Web are the current research hotspot. Now many information extraction techniques based on different principles have appeared and have different capabilities. We classify the existing information extraction techniques by the principle of information extraction and analyze the methods and principles of semantic information adding, schema defining, rule expression, semantic items locating and object locating in the approaches. Based on the above survey and analysis, several open problems are discussed.展开更多
Satellite remote sensing data are usually used to analyze the spatial distribution pattern of geological structures and generally serve as a significant means for the identification of alteration zones. Based on the L...Satellite remote sensing data are usually used to analyze the spatial distribution pattern of geological structures and generally serve as a significant means for the identification of alteration zones. Based on the Landsat Enhanced Thematic Mapper （ETM＋） data, which have better spectral resolution （8 bands） and spatial resolution （15 m in PAN band）, the synthesis processing techniques were presented to fulfill alteration information extraction： data preparation, vegetation indices and band ratios, and expert classifier-based classification. These techniques have been implemented in the MapGIS-RSP software （version 1.0）, developed by the Wuhan Zondy Cyber Technology Co., Ltd, China. In the study area application of extracting alteration information in the Zhaoyuan （招远） gold mines, Shandong （山东） Province, China, several hydorthermally altered zones （included two new sites） were found after satellite imagery interpretation coupled with field surveys. It is concluded that these synthesis processing techniques are useful approaches and are applicable to a wide range of gold-mineralized alteration information extraction.展开更多
Road geometric design data are a vital input for diverse transportation studies. This information is usually obtained from the road design project. However, these are not always available and the as-built course of th...Road geometric design data are a vital input for diverse transportation studies. This information is usually obtained from the road design project. However, these are not always available and the as-built course of the road may diverge considerably from its projected one, rendering subsequent studies inaccurate or impossible. Moreover, the systematic acquisition of this data for the entire road network of a country or even a state represents a very challenging and laborious task. This study＇s goal was the extraction of geometric design data for the paved segments of the Brazilian federal highway network, containing more than 47,000 km of highways. It presents the details of the method＇s adoption process, the particularities of its application to the dataset and the obtained geometric design information. Additionally, it provides a first overview of the Brazilian federal highway network composition （curves and tangents） and geometry.展开更多
The paper introduce segmentation ideas in the pretreatment process of web page. By page segmentation technique to extract the accurate information in the extract region, the region was processed to extract according t...The paper introduce segmentation ideas in the pretreatment process of web page. By page segmentation technique to extract the accurate information in the extract region, the region was processed to extract according to the rules of ontology extraction, and ultimately get the information you need. Through experiments on two real datasets and compare with related work, experimental results show that this method can achieve good extraction results.展开更多
Visual Information Extraction (VIE) is a technique that enables users to perform information extraction from visual documents driven by the visual appearance and the spatial relations occurring among the elements in t...Visual Information Extraction (VIE) is a technique that enables users to perform information extraction from visual documents driven by the visual appearance and the spatial relations occurring among the elements in the document. In particular, the extractions are expressed through a query language similar to the well known SQL. To further reduce the human effort in the extraction task, in this paper we present a fully formalized assistance mechanism that helps users in the interactive formulation of the queries.展开更多
Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:S...Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:Semantic dictionary and conditional random field model(CRFM)were used to annotate the semantic information of research papers.Based on the annotation results,the research level information was extracted through regular expression.All the functions were implemented on Sybase platform.Findings:According to the result of our experiment in carbon nanotube research,the precision and recall rates reached 65.13%and 57.75%,respectively after the semantic properties of word class have been labeled,and F-measure increased dramatically from less than 50%to60.18%while added with semantic features.Our experiment also showed that the information extraction system for research level(IESRL)can extract performance indicators from research papers rapidly and effectively.Research limitations:Some text information,such as that of format and chart,might have been lost due to the extraction processing of text format from PDF to TXT files.Semantic labeling on sentences could be insufficient due to the rich meaning of lexicons in the semantic dictionary.Research implications:The established system can help researchers rapidly compare the level of different research papers and find out their implicit innovation values.It could also be used as an auxiliary tool for analyzing research levels of various research institutions.Originality/value:In this work,we have successfully established an information extraction system for research papers by a revised semantic annotation method based on CRFM and the semantic dictionary.Our system can analyze the information extraction problem from two levels,i.e.from the sentence level and noun(phrase)level of research papers.Compared with the extraction method based on knowledge engineering and that on machine learning,our system shows advantages of the both.展开更多
Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed vi...Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed video with interframe motion vectors for speed, density and flow detection, has been proposed for ex-traction of traffic information under fixed camera setting and well-defined environment. The motion vectors arefirst separated from the compressed video streams, and then filtered to eliminate incorrect and noisy vectors u-sing the well-defined environmental knowledge. By applying the projective transform and using the filtered mo-tion vectors, speed can be calculated from motion vector statistics, density can be estimated using the motionvector occupancy, and flow can be detected using the combination of speed and density. The embodiment of aprototype system for sky camera traffic monitoring using the MPEG video has been implemented, and experi-mental results proved the effectiveness of the method proposed.展开更多
Information Extraction(IE)aims to extract structural knowledge from plain natural language texts.Recently,generative Large Language Models(LLMs)have demonstrated remarkable capabilities in text understanding and gener...Information Extraction(IE)aims to extract structural knowledge from plain natural language texts.Recently,generative Large Language Models(LLMs)have demonstrated remarkable capabilities in text understanding and generation.As a result,numerous works have been proposed to integrate LLMs for IE tasks based on a generative paradigm.To conduct a comprehensive systematic review and exploration of LLM efforts for IE tasks,in this study,we survey the most recent advancements in this field.We first present an extensive overview by categorizing these works in terms of various IE subtasks and techniques,and then we empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs.Based on a thorough review conducted,we identify several insights in technique and promising research directions that deserve further exploration in future studies.We maintain a public repository and consistently update related works and resources on GitHub(LLM4IE repository).展开更多
Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of use...Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of useful information. A weak characteristic information extraction based on μ-SVD and local mean decomposition （LMD） is developed to address this problem. The basic principle of the method is as follows： Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and μ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.展开更多
Web information extraction is viewed as a classification process and a competing classification method is presented to extract Web information directly through classification. Web fragments are represented with three ...Web information extraction is viewed as a classification process and a competing classification method is presented to extract Web information directly through classification. Web fragments are represented with three general features and the similarities between fragments are then defined on the bases of these features. Through competitions of fragments for different slots in information templates, the method classifies fragments into slot classes and filters out noise information. Far less annotated samples are needed as compared with rule-based methods and therefore it has a strong portability. Experiments show that the method has good performance and is superior to DOM-based method in information extraction. Key words information extraction - competing classification - feature extraction - wrapper induction CLC number TP 311 Foundation item: Supported by the National Natural Science Foundation of China (60303024)Biography: LI Xiang-yang (1974-), male, Ph. D. Candidate, research direction: information extraction, natural language processing.展开更多
Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating t...Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating terrains;3) Small fragmented land;4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion;2) extraction research on color features;3) extraction research on vegetation index;4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.展开更多
文摘Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and events—while also involving reasoning tasks like personnel classification,relationship judgment,and implicit inference.Moreover,utilizing models for extracting information from police incident data poses a significant challenge—data scarcity,which limits the effectiveness of traditional rule-based and machine-learning methods.To address these,we propose TIPS.In collaboration with public security experts,we used de-identified police incident data to create templates that enable large language models(LLMs)to populate data slots and generate simulated data,enhancing data density and diversity.We then designed schemas to efficiently manage complex extraction and reasoning tasks,constructing a high-quality dataset and fine-tuning multiple open-source LLMs.Experiments showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%,nearly 30%higher than the base model,significantly reducing error rates.Manual corrections further improved performance by 9.39%.This study demonstrates that combining largescale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments,offering a new approach for intelligent public security applications.
文摘Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved information retrieval efficiency to a certain extent,they exhibit significant limitations in adaptability and accuracy when dealing with procurement documents characterized by diverse formats and a high degree of unstructured content.The emergence of Large Language Models(LLMs)offers new possibilities for efficient procurement information processing and extraction.Methods:This study collected procurement transaction documents from public procurement websites,and proposed a procurement Information Extraction(IE)method based on LLMs.Unlike traditional approaches,this study systematically explores the applicability of LLMs in both structured and unstructured entities in procurement documents,addressing the challenges posed by format variability and content complexity.Furthermore,an optimized prompt framework tailored for procurement document extraction tasks is developed to enhance the accuracy and robustness of IE.The aim is to process and extract key information from medical device procurement quickly and accurately,meeting stakeholders'demands for precision and timeliness in information retrieval.Results:Experimental results demonstrate that,compared to traditional methods,the proposed approach achieves an F1 Score of 0.9698,representing a 4.85%improvement over the best baseline model.Moreover,both recall and precision rates are close to 97%,significantly outperforming other models and exhibiting exceptional overall recognition capabilities.Notably,further analysis reveals that the proposed method consistently maintains high performance across both structured and unstructured entities in procurement documents while balancing recall and precision effectively,demonstrating its adaptability in handling varying document formats.The results of ablation experiments validate the effectiveness of the proposed prompting strategy.Conclusion:Additionally,this study explores the challenges and potential improvements of the proposed method in IE tasks and provides insights into its feasibility for real-world deployment and application directions,further clarifying its adaptability and value.This method not only exhibits significant advantages in medical device procurement but also holds promise for providing new approaches to information processing and decision support in various domains.
基金supported by the Fundamental Research Funds for the Central Universities of China(Grant No.2013SCU11006)the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying,Mapping and Geoinformation of China(Grant NO.DM2014SC02)the Key Laboratory of Geospecial Information Technology,Ministry of Land and Resources of China(Grant NO.KLGSIT201504)
文摘The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.
基金This work was supported by the Deanship of Scientific Research at King Khalid University through a General Research Project under Grant Number GRP/41/42.
文摘Information extraction plays a vital role in natural language processing,to extract named entities and events from unstructured data.Due to the exponential data growth in the agricultural sector,extracting significant information has become a challenging task.Though existing deep learningbased techniques have been applied in smart agriculture for crop cultivation,crop disease detection,weed removal,and yield production,still it is difficult to find the semantics between extracted information due to unswerving effects of weather,soil,pest,and fertilizer data.This paper consists of two parts.An initial phase,which proposes a data preprocessing technique for removal of ambiguity in input corpora,and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer andmultilayer perceptron to find agricultural-based named entity recognition,events,and relations between them.The proposed algorithm has been trained and tested on four input corpora i.e.,agriculture,weather,soil,and pest&fertilizers.The experimental results have been compared with existing techniques and itwas observed that the proposed algorithm outperformsWeighted-SOM,LSTM+RAO,PLR-DBN,KNN,and Na飗e Bayes on standard parameters like accuracy,sensitivity,and specificity.
基金the Artificial Intelligence Innovation and Development Project of Shanghai Municipal Commission of Economy and Information (No. 2019-RGZN-01081)。
文摘Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.
文摘Synthetic aperture radar （SAR） provides a large amount of image data for the observation and research of oceanic eddies. The use of SAR images to automatically depict the shape of eddies and extract the eddy information is of great significance to the study of the oceanic eddies and the application of SAR eddy images. In this paper, a method of automatic shape depiction and information extraction for oceanic eddies in SAR images is proposed, which is for the research of spiral eddies. Firstly, the skeleton image is got by the skeletonization of SAR image. Secondly, the logarithmic spirals detected in the skeleton image are drawn on the SAR image to depict the shape of oceanic eddies. Finally, the eddy information is extracted based on the results of shape depiction. The sentinel 1 SAR eddy images in the Black Sea area were used for the experiment in this paper. The experimental results show that the proposed method can automatically depict the shape of eddies and extract the eddy information. The shape depiction results are consistent with the actual shape of the eddies, and the extracted eddy information is consistent with the reference information extracted by manual operation. As a result, the validity of the method is verified.
基金Projects(61374140,61673173)supported by the National Natural Science Foundation of ChinaProjects(222201717006,222201714031)supported by the Fundamental Research Funds for the Central Universities,China
文摘A two-step information extraction method is presented to capture the specific index-related information more accurately.In the first step,the overall process variables are separated into two sets based on Pearson correlation coefficient.One is process variables strongly related to the specific index and the other is process variables weakly related to the specific index.Through performing principal component analysis(PCA)on the two sets,the directions of latent variables have changed.In other words,the correlation between latent variables in the set with strong correlation and the specific index may become weaker.Meanwhile,the correlation between latent variables in the set with weak correlation and the specific index may be enhanced.In the second step,the two sets are further divided into a subset strongly related to the specific index and a subset weakly related to the specific index from the perspective of latent variables using Pearson correlation coefficient,respectively.Two subsets strongly related to the specific index form a new subspace related to the specific index.Then,a hybrid monitoring strategy based on predicted specific index using partial least squares(PLS)and T2statistics-based method is proposed for specific index-related process monitoring using comprehensive information.Predicted specific index reflects real-time information for the specific index.T2statistics are used to monitor specific index-related information.Finally,the proposed method is applied to Tennessee Eastman(TE).The results indicate the effectiveness of the proposed method.
基金the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2019R1G1A1003312)the Ministry of Education(NRF-2021R1I1A3052815).
文摘Analyzing Research and Development(R&D)trends is important because it can influence future decisions regarding R&D direction.In typical trend analysis,topic or technology taxonomies are employed to compute the popularities of the topics or codes over time.Although it is simple and effective,the taxonomies are difficult to manage because new technologies are introduced rapidly.Therefore,recent studies exploit deep learning to extract pre-defined targets such as problems and solutions.Based on the recent advances in question answering(QA)using deep learning,we adopt a multi-turn QA model to extract problems and solutions from Korean R&D reports.With the previous research,we use the reports directly and analyze the difficulties in handling them using QA style on Information Extraction(IE)for sentence-level benchmark dataset.After investigating the characteristics of Korean R&D,we propose a model to deal with multiple and repeated appearances of targets in the reports.Accordingly,we propose a model that includes an algorithm with two novel modules and a prompt.A newly proposed methodology focuses on reformulating a question without a static template or pre-defined knowledge.We show the effectiveness of the proposed model using a Korean R&D report dataset that we constructed and presented an in-depth analysis of the benefits of the multi-turn QA model.
文摘Traditional pattern representation in information extraction lack in the ability of representing domain-specific concepts and are therefore devoid of flexibility. To overcome these restrictions, an enhanced pattern representation is designed which includes ontological concepts, neighboring-tree structures and soft constraints. An information-(extraction) inference engine based on hypothesis-generation and conflict-resolution is implemented. The proposed technique is successfully applied to an information extraction system for Chinese-language query front-end of a job-recruitment search engine.
文摘Information extraction techniques on the Web are the current research hotspot. Now many information extraction techniques based on different principles have appeared and have different capabilities. We classify the existing information extraction techniques by the principle of information extraction and analyze the methods and principles of semantic information adding, schema defining, rule expression, semantic items locating and object locating in the approaches. Based on the above survey and analysis, several open problems are discussed.
基金The paper is supported by the Research Foundation for Out-standing Young Teachers, China University of Geosciences (Wuhan) (Nos. CUGQNL0628, CUGQNL0640)the National High-Tech Research and Development Program (863 Program) (No. 2001AA135170)the Postdoctoral Foundation of the Shandong Zhaojin Group Co. (No. 20050262120)
文摘Satellite remote sensing data are usually used to analyze the spatial distribution pattern of geological structures and generally serve as a significant means for the identification of alteration zones. Based on the Landsat Enhanced Thematic Mapper （ETM＋） data, which have better spectral resolution （8 bands） and spatial resolution （15 m in PAN band）, the synthesis processing techniques were presented to fulfill alteration information extraction： data preparation, vegetation indices and band ratios, and expert classifier-based classification. These techniques have been implemented in the MapGIS-RSP software （version 1.0）, developed by the Wuhan Zondy Cyber Technology Co., Ltd, China. In the study area application of extracting alteration information in the Zhaoyuan （招远） gold mines, Shandong （山东） Province, China, several hydorthermally altered zones （included two new sites） were found after satellite imagery interpretation coupled with field surveys. It is concluded that these synthesis processing techniques are useful approaches and are applicable to a wide range of gold-mineralized alteration information extraction.
文摘Road geometric design data are a vital input for diverse transportation studies. This information is usually obtained from the road design project. However, these are not always available and the as-built course of the road may diverge considerably from its projected one, rendering subsequent studies inaccurate or impossible. Moreover, the systematic acquisition of this data for the entire road network of a country or even a state represents a very challenging and laborious task. This study＇s goal was the extraction of geometric design data for the paved segments of the Brazilian federal highway network, containing more than 47,000 km of highways. It presents the details of the method＇s adoption process, the particularities of its application to the dataset and the obtained geometric design information. Additionally, it provides a first overview of the Brazilian federal highway network composition （curves and tangents） and geometry.
文摘The paper introduce segmentation ideas in the pretreatment process of web page. By page segmentation technique to extract the accurate information in the extract region, the region was processed to extract according to the rules of ontology extraction, and ultimately get the information you need. Through experiments on two real datasets and compare with related work, experimental results show that this method can achieve good extraction results.
文摘Visual Information Extraction (VIE) is a technique that enables users to perform information extraction from visual documents driven by the visual appearance and the spatial relations occurring among the elements in the document. In particular, the extractions are expressed through a query language similar to the well known SQL. To further reduce the human effort in the extraction task, in this paper we present a fully formalized assistance mechanism that helps users in the interactive formulation of the queries.
基金supported by the National Social Science Foundation of China(Grant No.12CTQ032)
文摘Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:Semantic dictionary and conditional random field model(CRFM)were used to annotate the semantic information of research papers.Based on the annotation results,the research level information was extracted through regular expression.All the functions were implemented on Sybase platform.Findings:According to the result of our experiment in carbon nanotube research,the precision and recall rates reached 65.13%and 57.75%,respectively after the semantic properties of word class have been labeled,and F-measure increased dramatically from less than 50%to60.18%while added with semantic features.Our experiment also showed that the information extraction system for research level(IESRL)can extract performance indicators from research papers rapidly and effectively.Research limitations:Some text information,such as that of format and chart,might have been lost due to the extraction processing of text format from PDF to TXT files.Semantic labeling on sentences could be insufficient due to the rich meaning of lexicons in the semantic dictionary.Research implications:The established system can help researchers rapidly compare the level of different research papers and find out their implicit innovation values.It could also be used as an auxiliary tool for analyzing research levels of various research institutions.Originality/value:In this work,we have successfully established an information extraction system for research papers by a revised semantic annotation method based on CRFM and the semantic dictionary.Our system can analyze the information extraction problem from two levels,i.e.from the sentence level and noun(phrase)level of research papers.Compared with the extraction method based on knowledge engineering and that on machine learning,our system shows advantages of the both.
文摘Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed video with interframe motion vectors for speed, density and flow detection, has been proposed for ex-traction of traffic information under fixed camera setting and well-defined environment. The motion vectors arefirst separated from the compressed video streams, and then filtered to eliminate incorrect and noisy vectors u-sing the well-defined environmental knowledge. By applying the projective transform and using the filtered mo-tion vectors, speed can be calculated from motion vector statistics, density can be estimated using the motionvector occupancy, and flow can be detected using the combination of speed and density. The embodiment of aprototype system for sky camera traffic monitoring using the MPEG video has been implemented, and experi-mental results proved the effectiveness of the method proposed.
基金supported in part by the grants from the National Natural Science Foundation of China(Nos.62222213,62072423)partially supported by Research Impact Fund(No.R1015-23),APRC-CityU New Research Initiatives(No.9610565,Start-up Grant for New Faculty of CityU)+7 种基金CityU-HKIDS Early Career Research Grant(No.9360163)Hong Kong ITC Innovation and Technology Fund Midstream Research Programme for Universities Project(No.ITS/034/22MS)Hong Kong Environmental and Conservation Fund(No.88/2022)SIRG-CityU Strategic Interdisciplinary Research Grant(No.7020046)Huawei(Huawei Innovation Research Program),Tencent(CCFTencent Open Fund,Tencent Rhino-Bird Focused Research Program),Ant Group(CCF-Ant Research Fund,Ant Group Research Fund)Alibaba(CCFAlimama Tech Kangaroo Fund(No.2024002))CCF-BaiChuan-Ebtech Foundation Model FundKuaishou.
文摘Information Extraction(IE)aims to extract structural knowledge from plain natural language texts.Recently,generative Large Language Models(LLMs)have demonstrated remarkable capabilities in text understanding and generation.As a result,numerous works have been proposed to integrate LLMs for IE tasks based on a generative paradigm.To conduct a comprehensive systematic review and exploration of LLM efforts for IE tasks,in this study,we survey the most recent advancements in this field.We first present an extensive overview by categorizing these works in terms of various IE subtasks and techniques,and then we empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs.Based on a thorough review conducted,we identify several insights in technique and promising research directions that deserve further exploration in future studies.We maintain a public repository and consistently update related works and resources on GitHub(LLM4IE repository).
基金This research was sponsored by the National Natural Science Foundation of China （Grant Nos. 51275052 and 51105041）, and the Key Project Supported by Beijing Natural Science Foundation （Grant No. 3131002）.
文摘Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of useful information. A weak characteristic information extraction based on μ-SVD and local mean decomposition （LMD） is developed to address this problem. The basic principle of the method is as follows： Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and μ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.
文摘Web information extraction is viewed as a classification process and a competing classification method is presented to extract Web information directly through classification. Web fragments are represented with three general features and the similarities between fragments are then defined on the bases of these features. Through competitions of fragments for different slots in information templates, the method classifies fragments into slot classes and filters out noise information. Far less annotated samples are needed as compared with rule-based methods and therefore it has a strong portability. Experiments show that the method has good performance and is superior to DOM-based method in information extraction. Key words information extraction - competing classification - feature extraction - wrapper induction CLC number TP 311 Foundation item: Supported by the National Natural Science Foundation of China (60303024)Biography: LI Xiang-yang (1974-), male, Ph. D. Candidate, research direction: information extraction, natural language processing.
文摘Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating terrains;3) Small fragmented land;4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion;2) extraction research on color features;3) extraction research on vegetation index;4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.
