This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prom...This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prompt + one-shot learning proposed in this article works well. Meanwhile, our research findings indicate that although timeline-based storyline extraction has shown promising prospects in the practical applications of LLMs, it is still a complex natural language processing task that requires further research.展开更多
Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated corpora.However,there are limited numbers of annotated biomedical event corpora,and the availa...Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated corpora.However,there are limited numbers of annotated biomedical event corpora,and the available datasets contain insufficient examples for training classifiers;the common cure is to seek large amounts of training samples from unlabeled data,but such data sets often contain many mislabeled samples,which will degrade the performance of classifiers.Therefore,this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event data.First,we construct the mislabeled dataset through error data analysis with the development dataset.The sample pairs’vector representations are then obtained by the means of sequence patterns and the joint model of convolutional neural network and long short-term memory recurrent neural network.Following this,the sample identification strategy is proposed,using error detection based on pair representation for unlabeled data.With the latter,the selected samples are added to enrich the training dataset and improve the classification performance.In the BioNLP Shared Task GENIA,the experiments results indicate that the proposed approach is competent in extract the biomedical event from biomedical literature.Our approach can effectively filter some noisy examples and build a satisfactory prediction model.展开更多
Event extraction is one of the most challenging tasks in information extraction.It is a common phenomenon where multiple events exist in the same sentence.However,extracting multiple events is more difficult than extr...Event extraction is one of the most challenging tasks in information extraction.It is a common phenomenon where multiple events exist in the same sentence.However,extracting multiple events is more difficult than extracting a single event.Existing event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too long.In addition,the current argument extraction relies on the results of syntactic dependency analysis,which is complicated and prone to error trans-mission.In order to solve the above problems,a joint event extraction method based on global event-type guidance and attention enhancement was proposed in this work.Specifically,for multiple event detection,we propose a global-type guidance method that can detect event types in the candidate sequence in advance to enhance the correlation information between events.For argument extraction,we converted it into a table-flling problem,and proposed a table-flling method of the attention mechanism,that is simple and can enhance the correlation between trigger words and arguments.The experimental results based on the ACE 2005 dataset showed that the proposed method achieved 1.6%improvement in the task of event detection,and obtained state-of-the-art results in the argument extraction task,which proved the effectiveness of the method.展开更多
As a basic unit of knowledge representation and an important means for information organization, event has drawn growing number of people’s attention, the research of event identification and extraction in natural la...As a basic unit of knowledge representation and an important means for information organization, event has drawn growing number of people’s attention, the research of event identification and extraction in natural language processing field is an important research topic in information extraction area, the recognition and extraction of event trigger word plays a decisive role in event identification and extraction. In this paper, the authors make experiment in Chinese Event Corpus CEC, and present a method of extracting event trigger word automatically that combines extended trigger word table and machine learning. The experiment result shows that the F-score of extracting event trigger word. can reach 71.2% by using this method.展开更多
The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information m...The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information may become a robust source of real-world data, which may form the basis of an objective data-driven analysis. In this study, a methodology for collecting information about audio and visual art events in an automated manner from a large array of websites is presented in detail. This process uses cutting edge Semantic Web, Web Search and Generative AI technologies to convert website documents into a collection of structured data. The value of the methodology is demonstrated by creating a large dataset concerning audiovisual events in Greece. The collected information includes event characteristics, estimated metrics based on their text descriptions, outreach metrics based on the media that reported them, and a multi-layered classification of these events based on their type, subjects and methods used. This dataset is openly provided to the general and academic public through a Web application. Moreover, each event’s outreach is evaluated using these quantitative metrics, the results are analyzed with an emphasis on classification popularity and useful conclusions are drawn concerning the importance of artistic subjects, methods, and media.展开更多
Purpose:The purpose of this study is to serve as a comprehensive review of the existing annotated corpora.This review study aims to provide information on the existing annotated corpora for event extraction,which are ...Purpose:The purpose of this study is to serve as a comprehensive review of the existing annotated corpora.This review study aims to provide information on the existing annotated corpora for event extraction,which are limited but essential for training and improving the existing event extraction algorithms.In addition to the primary goal of this study,it provides guidelines for preparing an annotated corpus and suggests suitable tools for the annotation task.Design/methodology/approach:This study employs an analytical approach to examine available corpus that is suitable for event extraction tasks.It offers an in-depth analysis of existing event extraction corpora and provides systematic guidelines for researchers to develop accurate,high-quality corpora.This ensures the reliability of the created corpus and its suitability for training machine learning algorithms.Findings:Our exploration reveals a scarcity of annotated corpora for event extraction tasks.In particular,the English corpora are mainly focused on the biomedical and general domains.Despite the issue of annotated corpora scarcity,there are several high-quality corpora available and widely used as benchmark datasets.However,access to some of these corpora might be limited owing to closed-access policies or discontinued maintenance after being initially released,rendering them inaccessible owing to broken links.Therefore,this study documents the available corpora for event extraction tasks.Research limitations:Our study focuses only on well-known corpora available in English and Chinese.Nevertheless,this study places a strong emphasis on the English corpora due to its status as a global lingua franca,making it widely understood compared to other languages.Practical implications:We genuinely believe that this study provides valuable knowledge that can serve as a guiding framework for preparing and accurately annotating events from text corpora.It provides comprehensive guidelines for researchers to improve the quality of corpus annotations,especially for event extraction tasks across various domains.Originality/value:This study comprehensively compiled information on the existing annotated corpora for event extraction tasks and provided preparation guidelines.展开更多
Event extraction stands as a significant endeavor within the realm of information extraction,aspiring to automatically extract structured event information from vast volumes of unstructured text.Extracting event eleme...Event extraction stands as a significant endeavor within the realm of information extraction,aspiring to automatically extract structured event information from vast volumes of unstructured text.Extracting event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the data.Although researchers have proposed various methods to accomplish this task,most existing event extraction models cannot address these challenges because they are only applicable to text scenarios.To solve the above issues,this paper proposes a multi-modal event extraction method based on knowledge fusion.Specifically,for event-type recognition,we use a meticulous pipeline approach that integrates multiple pre-trained models.This approach enables a more comprehensive capture of the multidimensional event semantic features present in military texts,thereby enhancing the interconnectedness of information between trigger words and events.For event element extraction,we propose a method for constructing a priori templates that combine event types with corresponding trigger words.This approach facilitates the acquisition of fine-grained input samples containing event trigger words,thus enabling the model to understand the semantic relationships between elements in greater depth.Furthermore,a fusion method for spatial mapping of textual event elements and image elements is proposed to reduce the category number overload and effectively achieve multi-modal knowledge fusion.The experimental results based on the CCKS 2022 dataset show that our method has achieved competitive results,with a comprehensive evaluation value F1-score of 53.4%for the model.These results validate the effectiveness of our method in extracting event elements from multi-modal data.展开更多
Social media like Twitter who serves as a novel news medium and has become increasingly popular since its establishment. Large scale first-hand user-generated tweets motivate automatic event detection on Twitter. Prev...Social media like Twitter who serves as a novel news medium and has become increasingly popular since its establishment. Large scale first-hand user-generated tweets motivate automatic event detection on Twitter. Previous unsupervised approaches detected events by clustering words. These methods detect events using burstiness,which measures surging frequencies of words at certain time windows. However,event clusters represented by a set of individual words are difficult to understand. This issue is addressed by building a document-level event detection model that directly calculates the burstiness of tweets,leveraging distributed word representations for modeling semantic information,thereby avoiding sparsity. Results show that the document-level model not only offers event summaries that are directly human-readable,but also gives significantly improved accuracies compared to previous methods on unsupervised tweet event detection,which are based on words/segments.展开更多
Background: Dialyzable leukocyte extracts (DLE) are heterogeneous mixtures of peptides less than 10 kDa in size that are used as immunomodulatory adjuvants in immune-mediated diseases. TransferonTM is DLE manufactured...Background: Dialyzable leukocyte extracts (DLE) are heterogeneous mixtures of peptides less than 10 kDa in size that are used as immunomodulatory adjuvants in immune-mediated diseases. TransferonTM is DLE manufactured by National Polytechnic Institute (IPN), and is registered by Mexican health-regulatory authorities as an immunomodulatory drug and commercialized nationally. The proposed mechanism of action of TransferonTM is induction of a Th1 immunoregulatory response. Despite that it is widely used, to date there are no reports of adverse events related to the clinical safety of human DLE or TransferonTM. Objective: To assess the safety of TransferonTM in a large group of patients exposed to DLE as adjuvant treatment. Methods: We included in this study 3844 patients from our Clinical Immunology Service at the Unit of External Services and Clinical Research (USEIC), IPN. Analysis was performed from January 2014 to November 2014, searching for clinical adverse events in patients with immune-mediated diseases and treated with TransferonTM as an adjuvant. Results: In this work we observed clinical nonserious adverse events (AE) in 1.9% of patients treated with TransferonTM (MD 1.9, IQR 1.7 - 2.0). AE were 2.8 times more frequently observed in female than in male patients. The most common AE were headache in 15.7%, followed by rash in 11.4%, increased disease-related symptomatology in 10%, rhinorrhea in 7.1%, cough in 5.7%, and fatigue in 5.7% of patients with AE. 63% of adverse event presentation occurred from day 1 to day 4 of treatment with TransferonTM, and mean time resolution of adverse events was 14 days. In 23 cases, the therapy was stopped because of adverse events and no serious adverse events were observed in this study. Conclusion: TransferonTM induced low frequency of nonserious adverse events during adjuvant treatment. Further monitoring is advisable for different age and disease groups of patients.展开更多
Event extraction extracts event frames from text, while grounded situation recognition detects events in images. As real-world applications frequently encounter a multitude of unforeseen events, certain researchers ha...Event extraction extracts event frames from text, while grounded situation recognition detects events in images. As real-world applications frequently encounter a multitude of unforeseen events, certain researchers have introduced cross-domain and in-domain event extraction, while grounded situation recognition primarily explores in-domain scenarios. Therefore, in this paper, we propose cross-domain grounded situation recognition and establish a new benchmark SWiG-XD. In this more challenging setting, we deepen the connection between the two tasks based on their underlying unity in two different modalities and explore how to transfer the generalization ability from text to images. Firstly, we utilize ChatGPT to automatically generate textual data, which can be divided into two categories. One category is directly matched with images, establishing a direct connection with the images. The other category encompasses all event types and possesses greater generalization. Then we employ a unified model framework to establish the association between textual concepts and local image features and achieve cross-domain generalization transfer across modalities through modality-shared prompts and self-attention mechanism. Furthermore, we incorporate textual data with higher generalization to further assist in improving generalization on images. The experimental results on the newly constructed benchmark demonstrate the effectiveness of our method.展开更多
Event extraction is an important part of natural language information extraction,and it’s widely employed in other natural language processing tasks including question answering and machine reading comprehension.Howe...Event extraction is an important part of natural language information extraction,and it’s widely employed in other natural language processing tasks including question answering and machine reading comprehension.However,there is a lack of recent comprehensive survey papers on event extraction.In the past few years,numerous high-quality and innovative event extraction methods have been proposed,making it necessary to consolidate these new developments with previous work in order to provide a clear overview for researchers and serve as a reference for future studies.In addition,event detection is a fundamental sub-task in event extraction,previous survey papers have often overlooked the related work on event detection.Therefore,this paper aims to bridge these gaps by presenting a comprehensive survey of event extraction,including recent advancements and an analysis of previous research on event detection.The resources for event extraction are first introduced in this research,and then the numerous neural network models currently employed in event extraction tasks are divided into four types:word sequence-based methods,graph-based neural network methods,external knowledge-based approaches,and prompt-based approaches.We compare and contrast them in depth,pointing out the flaws and difficulties with existing research.Finally,we discuss the future of event extraction development.展开更多
Evidential Document-level Event Factuality Identification(EvDEFI)aims to predict the factual nature of an event and extract evidential sentences from the document precisely.Previous work usually limited to only predic...Evidential Document-level Event Factuality Identification(EvDEFI)aims to predict the factual nature of an event and extract evidential sentences from the document precisely.Previous work usually limited to only predicting the factuality of an event with respect to a document,and neglected the interpretability of the task.As a more fine-grained and interpretable task,EvDEFI is still in the early stage.The existing model only used shallow similarity calculation to extract evidences,and employed simple attentions without lexical features,which is quite coarse-grained.Therefore,we propose a novel EvDEFI model named Heterogeneous and Extractive Graph Attention Network(HEGAT),which can update representations of events and sentences by multi-view graph attentions based on tokens and various lexical features from both local and global levels.Experiments on EB-DEF-v2 corpus demonstrate that HEGAT model is superior to several competitive baselines and can validate the interpretability of the task.展开更多
1 Introduction Sound event detection(SED)aims to identify and locate specific sound event categories and their corresponding timestamps within continuous audio streams.To overcome the limitations posed by the scarcity...1 Introduction Sound event detection(SED)aims to identify and locate specific sound event categories and their corresponding timestamps within continuous audio streams.To overcome the limitations posed by the scarcity of strongly labeled training data,researchers have increasingly turned to semi-supervised learning(SSL)[1],which leverages unlabeled data to augment training and improve detection performance.Among many SSL methods[2-4].展开更多
Event extraction(EE)is a complex natural language processing(NLP)task that aims at identifying and classifying triggers and arguments in raw text.The polysemy of triggers and arguments stands out as one of the key cha...Event extraction(EE)is a complex natural language processing(NLP)task that aims at identifying and classifying triggers and arguments in raw text.The polysemy of triggers and arguments stands out as one of the key challenges affecting the precise extraction of events.Existing approaches commonly consider the semantic distribution of triggers and arguments to be balanced.However,the sample quantities of different semantics in the same trigger or argument vary in real-world scenarios,leading to a biased semantic distribution.The bias introduces two challenges:(1)low-frequency semantics is difficult to identify;(2)high-frequency semantics is often mistakenly identified.To tackle these challenges,we propose an adaptive learning method with the reward-penalty mechanism for balancing the semantic distribution in polysemous triggers and arguments.The reward-penalty mechanism balances the semantic distribution by enlarging the gap between the target and nontarget semantics by rewarding correct classifications and penalizing incorrect classifications.Additionally,we propose a sentencelevel event situation awareness(SA)mechanism to guide the encoder to accurately learn the knowledge of events mentioned in the sentence,thereby enhancing target event semantics in the distribution of polysemous triggers and arguments.Finally,for various semantics in different tasks,we propose task-specific semantic decoders to precisely identify the boundaries of the predicted triggers and arguments for the semantics.Our experimental results on ACE2005 and its variants,along with the rich Entities,Relations,and Events(ERE),demonstrate the superiority of our approach over single-task and multi-task EE baselines.展开更多
Event detection(ED)is aimed at detecting event occurrences and categorizing them.This task has been previously solved via recognition and classification of event triggers(ETs),which are defined as the phrase or word m...Event detection(ED)is aimed at detecting event occurrences and categorizing them.This task has been previously solved via recognition and classification of event triggers(ETs),which are defined as the phrase or word most clearly expressing event occurrence.Thus,current approaches require both annotated triggers as well as event types in training data.Nevertheless,triggers are non-essential in ED,and it is time-wasting for annotators to identify the“most clearly”word from a sentence,particularly in longer sentences.To decrease manual effort,we evaluate event detectionwithout triggers.We propose a novel framework that combines Type-aware Attention and Graph Convolutional Networks(TA-GCN)for event detection.Specifically,the task is identified as a multi-label classification problem.We first encode the input sentence using a novel type-aware neural network with attention mechanisms.Then,a Graph Convolutional Networks(GCN)-based multilabel classification model is exploited for event detection.Experimental results demonstrate the effectiveness.展开更多
Document-level financial event extraction(DFEE) is the task of detecting events and extracting the corresponding event arguments in financial documents, which plays an important role in information extraction in the f...Document-level financial event extraction(DFEE) is the task of detecting events and extracting the corresponding event arguments in financial documents, which plays an important role in information extraction in the financial domain. This task is challenging as the financial documents are generally long text and event arguments of one event may be scattered in different sentences. To address this issue, we proposed a novel Prior Information Enhanced Extraction framework(PIEE) for DFEE, leveraging prior information from both event types and pre-trained language models. Specifically, PIEE consists of three components: event detection, event argument extraction, and event table filling. In event detection, we identify the event type. Then, the event type is explicitly used for event argument extraction. Meanwhile, the implicit information within language models also provides considerable cues for event arguments localization. Finally, all the event arguments are filled in an event table by a set of predefined heuristic rules. To demonstrate the effectiveness of our proposed framework, we participated in the share task of CCKS2020 Task 4-2: Documentlevel Event Arguments Extraction. On both Leaderboard A and Leaderboard B, PIEE took the first place and significantly outperformed the other systems.展开更多
Around the globe, the necessity of green supply with a dedicated standard quality thrust of consumers is increasing day by day. The advancement in technology urges the electrical power system to deliver a high-quality...Around the globe, the necessity of green supply with a dedicated standard quality thrust of consumers is increasing day by day. The advancement in technology urges the electrical power system to deliver a high-quality rated undistorted sinusoidal current, the voltage at a constant desired standard frequency to its consumers. The present paper reveals a complete and inclusive study of power quality events, such as automatic classification and signal processing via creative techniques and the noises effect on the detection and classification of power quality disturbances. It’s planned to make a possible list for quick reference to obtain an extensive variety on the condition & status of available research for detection and classification for young engineers, designers and researchers who enter in the power quality field. The current extensive study is supported by a critical review of more than 200 publications on detection and classification techniques of power quality disturbances.展开更多
文摘This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prompt + one-shot learning proposed in this article works well. Meanwhile, our research findings indicate that although timeline-based storyline extraction has shown promising prospects in the practical applications of LLMs, it is still a complex natural language processing task that requires further research.
基金This work was supported by the National Natural Science Foundation of China(No.61672301)Jilin Provincial Science&Technology Development(20180101054JC)+1 种基金Science and Technology Innovation Guide Project of Inner Mongolia Autonomous Region of China(2017)Talent Development Fund of Jilin Province(2018).
文摘Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated corpora.However,there are limited numbers of annotated biomedical event corpora,and the available datasets contain insufficient examples for training classifiers;the common cure is to seek large amounts of training samples from unlabeled data,but such data sets often contain many mislabeled samples,which will degrade the performance of classifiers.Therefore,this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event data.First,we construct the mislabeled dataset through error data analysis with the development dataset.The sample pairs’vector representations are then obtained by the means of sequence patterns and the joint model of convolutional neural network and long short-term memory recurrent neural network.Following this,the sample identification strategy is proposed,using error detection based on pair representation for unlabeled data.With the latter,the selected samples are added to enrich the training dataset and improve the classification performance.In the BioNLP Shared Task GENIA,the experiments results indicate that the proposed approach is competent in extract the biomedical event from biomedical literature.Our approach can effectively filter some noisy examples and build a satisfactory prediction model.
基金This work was supported by the Hunan Provincial Natural Science Foundation of China(Grant No.2020JJ4624,2019JJ50655)the Scientific Research Fund of Hunan Provincial Education Department(Grant No.19A020)the National Social Science Fund of China(Grant No.20&ZD047)。
文摘Event extraction is one of the most challenging tasks in information extraction.It is a common phenomenon where multiple events exist in the same sentence.However,extracting multiple events is more difficult than extracting a single event.Existing event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too long.In addition,the current argument extraction relies on the results of syntactic dependency analysis,which is complicated and prone to error trans-mission.In order to solve the above problems,a joint event extraction method based on global event-type guidance and attention enhancement was proposed in this work.Specifically,for multiple event detection,we propose a global-type guidance method that can detect event types in the candidate sequence in advance to enhance the correlation information between events.For argument extraction,we converted it into a table-flling problem,and proposed a table-flling method of the attention mechanism,that is simple and can enhance the correlation between trigger words and arguments.The experimental results based on the ACE 2005 dataset showed that the proposed method achieved 1.6%improvement in the task of event detection,and obtained state-of-the-art results in the argument extraction task,which proved the effectiveness of the method.
文摘As a basic unit of knowledge representation and an important means for information organization, event has drawn growing number of people’s attention, the research of event identification and extraction in natural language processing field is an important research topic in information extraction area, the recognition and extraction of event trigger word plays a decisive role in event identification and extraction. In this paper, the authors make experiment in Chinese Event Corpus CEC, and present a method of extracting event trigger word automatically that combines extended trigger word table and machine learning. The experiment result shows that the F-score of extracting event trigger word. can reach 71.2% by using this method.
文摘The World Wide Web provides a wealth of information about everything, including contemporary audio and visual art events, which are discussed on media outlets, blogs, and specialized websites alike. This information may become a robust source of real-world data, which may form the basis of an objective data-driven analysis. In this study, a methodology for collecting information about audio and visual art events in an automated manner from a large array of websites is presented in detail. This process uses cutting edge Semantic Web, Web Search and Generative AI technologies to convert website documents into a collection of structured data. The value of the methodology is demonstrated by creating a large dataset concerning audiovisual events in Greece. The collected information includes event characteristics, estimated metrics based on their text descriptions, outreach metrics based on the media that reported them, and a multi-layered classification of these events based on their type, subjects and methods used. This dataset is openly provided to the general and academic public through a Web application. Moreover, each event’s outreach is evaluated using these quantitative metrics, the results are analyzed with an emphasis on classification popularity and useful conclusions are drawn concerning the importance of artistic subjects, methods, and media.
文摘Purpose:The purpose of this study is to serve as a comprehensive review of the existing annotated corpora.This review study aims to provide information on the existing annotated corpora for event extraction,which are limited but essential for training and improving the existing event extraction algorithms.In addition to the primary goal of this study,it provides guidelines for preparing an annotated corpus and suggests suitable tools for the annotation task.Design/methodology/approach:This study employs an analytical approach to examine available corpus that is suitable for event extraction tasks.It offers an in-depth analysis of existing event extraction corpora and provides systematic guidelines for researchers to develop accurate,high-quality corpora.This ensures the reliability of the created corpus and its suitability for training machine learning algorithms.Findings:Our exploration reveals a scarcity of annotated corpora for event extraction tasks.In particular,the English corpora are mainly focused on the biomedical and general domains.Despite the issue of annotated corpora scarcity,there are several high-quality corpora available and widely used as benchmark datasets.However,access to some of these corpora might be limited owing to closed-access policies or discontinued maintenance after being initially released,rendering them inaccessible owing to broken links.Therefore,this study documents the available corpora for event extraction tasks.Research limitations:Our study focuses only on well-known corpora available in English and Chinese.Nevertheless,this study places a strong emphasis on the English corpora due to its status as a global lingua franca,making it widely understood compared to other languages.Practical implications:We genuinely believe that this study provides valuable knowledge that can serve as a guiding framework for preparing and accurately annotating events from text corpora.It provides comprehensive guidelines for researchers to improve the quality of corpus annotations,especially for event extraction tasks across various domains.Originality/value:This study comprehensively compiled information on the existing annotated corpora for event extraction tasks and provided preparation guidelines.
基金supported by the National Natural Science Foundation of China(Grant No.81973695)Discipline with Strong Characteristics of Liaocheng University-Intelligent Science and Technology(Grant No.319462208).
文摘Event extraction stands as a significant endeavor within the realm of information extraction,aspiring to automatically extract structured event information from vast volumes of unstructured text.Extracting event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the data.Although researchers have proposed various methods to accomplish this task,most existing event extraction models cannot address these challenges because they are only applicable to text scenarios.To solve the above issues,this paper proposes a multi-modal event extraction method based on knowledge fusion.Specifically,for event-type recognition,we use a meticulous pipeline approach that integrates multiple pre-trained models.This approach enables a more comprehensive capture of the multidimensional event semantic features present in military texts,thereby enhancing the interconnectedness of information between trigger words and events.For event element extraction,we propose a method for constructing a priori templates that combine event types with corresponding trigger words.This approach facilitates the acquisition of fine-grained input samples containing event trigger words,thus enabling the model to understand the semantic relationships between elements in greater depth.Furthermore,a fusion method for spatial mapping of textual event elements and image elements is proposed to reduce the category number overload and effectively achieve multi-modal knowledge fusion.The experimental results based on the CCKS 2022 dataset show that our method has achieved competitive results,with a comprehensive evaluation value F1-score of 53.4%for the model.These results validate the effectiveness of our method in extracting event elements from multi-modal data.
基金Supported by the National High Technology Research and Development Programme of China(No.2015AA015405)
文摘Social media like Twitter who serves as a novel news medium and has become increasingly popular since its establishment. Large scale first-hand user-generated tweets motivate automatic event detection on Twitter. Previous unsupervised approaches detected events by clustering words. These methods detect events using burstiness,which measures surging frequencies of words at certain time windows. However,event clusters represented by a set of individual words are difficult to understand. This issue is addressed by building a document-level event detection model that directly calculates the burstiness of tweets,leveraging distributed word representations for modeling semantic information,thereby avoiding sparsity. Results show that the document-level model not only offers event summaries that are directly human-readable,but also gives significantly improved accuracies compared to previous methods on unsupervised tweet event detection,which are based on words/segments.
文摘Background: Dialyzable leukocyte extracts (DLE) are heterogeneous mixtures of peptides less than 10 kDa in size that are used as immunomodulatory adjuvants in immune-mediated diseases. TransferonTM is DLE manufactured by National Polytechnic Institute (IPN), and is registered by Mexican health-regulatory authorities as an immunomodulatory drug and commercialized nationally. The proposed mechanism of action of TransferonTM is induction of a Th1 immunoregulatory response. Despite that it is widely used, to date there are no reports of adverse events related to the clinical safety of human DLE or TransferonTM. Objective: To assess the safety of TransferonTM in a large group of patients exposed to DLE as adjuvant treatment. Methods: We included in this study 3844 patients from our Clinical Immunology Service at the Unit of External Services and Clinical Research (USEIC), IPN. Analysis was performed from January 2014 to November 2014, searching for clinical adverse events in patients with immune-mediated diseases and treated with TransferonTM as an adjuvant. Results: In this work we observed clinical nonserious adverse events (AE) in 1.9% of patients treated with TransferonTM (MD 1.9, IQR 1.7 - 2.0). AE were 2.8 times more frequently observed in female than in male patients. The most common AE were headache in 15.7%, followed by rash in 11.4%, increased disease-related symptomatology in 10%, rhinorrhea in 7.1%, cough in 5.7%, and fatigue in 5.7% of patients with AE. 63% of adverse event presentation occurred from day 1 to day 4 of treatment with TransferonTM, and mean time resolution of adverse events was 14 days. In 23 cases, the therapy was stopped because of adverse events and no serious adverse events were observed in this study. Conclusion: TransferonTM induced low frequency of nonserious adverse events during adjuvant treatment. Further monitoring is advisable for different age and disease groups of patients.
基金supported by National Natural Science Foundation of China(No.62176058)National Key RD Program of China(2023YFF1204800).
文摘Event extraction extracts event frames from text, while grounded situation recognition detects events in images. As real-world applications frequently encounter a multitude of unforeseen events, certain researchers have introduced cross-domain and in-domain event extraction, while grounded situation recognition primarily explores in-domain scenarios. Therefore, in this paper, we propose cross-domain grounded situation recognition and establish a new benchmark SWiG-XD. In this more challenging setting, we deepen the connection between the two tasks based on their underlying unity in two different modalities and explore how to transfer the generalization ability from text to images. Firstly, we utilize ChatGPT to automatically generate textual data, which can be divided into two categories. One category is directly matched with images, establishing a direct connection with the images. The other category encompasses all event types and possesses greater generalization. Then we employ a unified model framework to establish the association between textual concepts and local image features and achieve cross-domain generalization transfer across modalities through modality-shared prompts and self-attention mechanism. Furthermore, we incorporate textual data with higher generalization to further assist in improving generalization on images. The experimental results on the newly constructed benchmark demonstrate the effectiveness of our method.
文摘Event extraction is an important part of natural language information extraction,and it’s widely employed in other natural language processing tasks including question answering and machine reading comprehension.However,there is a lack of recent comprehensive survey papers on event extraction.In the past few years,numerous high-quality and innovative event extraction methods have been proposed,making it necessary to consolidate these new developments with previous work in order to provide a clear overview for researchers and serve as a reference for future studies.In addition,event detection is a fundamental sub-task in event extraction,previous survey papers have often overlooked the related work on event detection.Therefore,this paper aims to bridge these gaps by presenting a comprehensive survey of event extraction,including recent advancements and an analysis of previous research on event detection.The resources for event extraction are first introduced in this research,and then the numerous neural network models currently employed in event extraction tasks are divided into four types:word sequence-based methods,graph-based neural network methods,external knowledge-based approaches,and prompt-based approaches.We compare and contrast them in depth,pointing out the flaws and difficulties with existing research.Finally,we discuss the future of event extraction development.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.62006167 and 62276177)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Evidential Document-level Event Factuality Identification(EvDEFI)aims to predict the factual nature of an event and extract evidential sentences from the document precisely.Previous work usually limited to only predicting the factuality of an event with respect to a document,and neglected the interpretability of the task.As a more fine-grained and interpretable task,EvDEFI is still in the early stage.The existing model only used shallow similarity calculation to extract evidences,and employed simple attentions without lexical features,which is quite coarse-grained.Therefore,we propose a novel EvDEFI model named Heterogeneous and Extractive Graph Attention Network(HEGAT),which can update representations of events and sentences by multi-view graph attentions based on tokens and various lexical features from both local and global levels.Experiments on EB-DEF-v2 corpus demonstrate that HEGAT model is superior to several competitive baselines and can validate the interpretability of the task.
基金supported by the Zhejiang Provincial Key R&D Program(Nos.2024C01108,2023C01030,2023C01034)the Hangzhou Key R&D Program(Nos.2023SZD0046,2024SZD1A03)the Ningbo Key R&D Program(No.2024Z114).
文摘1 Introduction Sound event detection(SED)aims to identify and locate specific sound event categories and their corresponding timestamps within continuous audio streams.To overcome the limitations posed by the scarcity of strongly labeled training data,researchers have increasingly turned to semi-supervised learning(SSL)[1],which leverages unlabeled data to augment training and improve detection performance.Among many SSL methods[2-4].
基金supported by the National Natural Science Foundation of China(Nos.62306330 and 62106275),the Young Elite Scientist Sponsorship Program by China Association for Science and Technology(No.YESS20230367),and the Natural Science Foundation of Hunan Province,China(Nos.2022JJ40558 and WDZC20235250103)。
文摘Event extraction(EE)is a complex natural language processing(NLP)task that aims at identifying and classifying triggers and arguments in raw text.The polysemy of triggers and arguments stands out as one of the key challenges affecting the precise extraction of events.Existing approaches commonly consider the semantic distribution of triggers and arguments to be balanced.However,the sample quantities of different semantics in the same trigger or argument vary in real-world scenarios,leading to a biased semantic distribution.The bias introduces two challenges:(1)low-frequency semantics is difficult to identify;(2)high-frequency semantics is often mistakenly identified.To tackle these challenges,we propose an adaptive learning method with the reward-penalty mechanism for balancing the semantic distribution in polysemous triggers and arguments.The reward-penalty mechanism balances the semantic distribution by enlarging the gap between the target and nontarget semantics by rewarding correct classifications and penalizing incorrect classifications.Additionally,we propose a sentencelevel event situation awareness(SA)mechanism to guide the encoder to accurately learn the knowledge of events mentioned in the sentence,thereby enhancing target event semantics in the distribution of polysemous triggers and arguments.Finally,for various semantics in different tasks,we propose task-specific semantic decoders to precisely identify the boundaries of the predicted triggers and arguments for the semantics.Our experimental results on ACE2005 and its variants,along with the rich Entities,Relations,and Events(ERE),demonstrate the superiority of our approach over single-task and multi-task EE baselines.
基金supported by the Hunan Provincial Natural Science Foundation of China(Grant No.2020JJ4624)the National Social Science Fund of China(Grant No.20&ZD047)+1 种基金the Scientific Research Fund of Hunan Provincial Education Department(Grant No.19A020)the National University of Defense Technology Research Project ZK20-46 and the Young Elite Scientists Sponsorship Program 2021-JCJQ-QT-050.
文摘Event detection(ED)is aimed at detecting event occurrences and categorizing them.This task has been previously solved via recognition and classification of event triggers(ETs),which are defined as the phrase or word most clearly expressing event occurrence.Thus,current approaches require both annotated triggers as well as event types in training data.Nevertheless,triggers are non-essential in ED,and it is time-wasting for annotators to identify the“most clearly”word from a sentence,particularly in longer sentences.To decrease manual effort,we evaluate event detectionwithout triggers.We propose a novel framework that combines Type-aware Attention and Graph Convolutional Networks(TA-GCN)for event detection.Specifically,the task is identified as a multi-label classification problem.We first encode the input sentence using a novel type-aware neural network with attention mechanisms.Then,a Graph Convolutional Networks(GCN)-based multilabel classification model is exploited for event detection.Experimental results demonstrate the effectiveness.
基金The research is supported by the National Natural Science Foundation of China(No.61936010 and No.61876115)This work was partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization.
文摘Document-level financial event extraction(DFEE) is the task of detecting events and extracting the corresponding event arguments in financial documents, which plays an important role in information extraction in the financial domain. This task is challenging as the financial documents are generally long text and event arguments of one event may be scattered in different sentences. To address this issue, we proposed a novel Prior Information Enhanced Extraction framework(PIEE) for DFEE, leveraging prior information from both event types and pre-trained language models. Specifically, PIEE consists of three components: event detection, event argument extraction, and event table filling. In event detection, we identify the event type. Then, the event type is explicitly used for event argument extraction. Meanwhile, the implicit information within language models also provides considerable cues for event arguments localization. Finally, all the event arguments are filled in an event table by a set of predefined heuristic rules. To demonstrate the effectiveness of our proposed framework, we participated in the share task of CCKS2020 Task 4-2: Documentlevel Event Arguments Extraction. On both Leaderboard A and Leaderboard B, PIEE took the first place and significantly outperformed the other systems.
文摘Around the globe, the necessity of green supply with a dedicated standard quality thrust of consumers is increasing day by day. The advancement in technology urges the electrical power system to deliver a high-quality rated undistorted sinusoidal current, the voltage at a constant desired standard frequency to its consumers. The present paper reveals a complete and inclusive study of power quality events, such as automatic classification and signal processing via creative techniques and the noises effect on the detection and classification of power quality disturbances. It’s planned to make a possible list for quick reference to obtain an extensive variety on the condition & status of available research for detection and classification for young engineers, designers and researchers who enter in the power quality field. The current extensive study is supported by a critical review of more than 200 publications on detection and classification techniques of power quality disturbances.
