Event Argument Research Articles

Fine-grained document-level financial event argument extraction approach

Fine-grained document-level financial event argument extraction approach

Multi-view entity type overdependency reduction for event argument extraction

Multi-view entity type overdependency reduction for event argument extraction

Multi-Model Fusion-Based Hierarchical Extraction for Chinese Epidemic Event

In recent years, Coronavirus disease 2019 (COVID-19) has become a global epidemic, and some efforts have been devoted to tracking and controlling its spread. Extracting structured knowledge from involved epidemic case reports can inform the surveillance system, which is important for controlling the spread of outbreaks. Therefore, in this paper, we focus on the task of Chinese epidemic event extraction (EE), which is defined as the detection of epidemic-related events and corresponding arguments in the texts of epidemic case reports. To facilitate the research of this task, we first define the epidemic-related event types and argument roles. Then we manually annotate a Chinese COVID-19 epidemic dataset, named COVID-19 Case Report (CCR). We also propose a novel hierarchical EE architecture, named multi-model fusion-based hierarchical event extraction (MFHEE). In MFHEE, we introduce a multi-model fusion strategy to tackle the issue of recognition bias of previous EE models. The experimental results on CCR dataset show that our method can effectively extract epidemic events and outperforms other baselines on this dataset. The comparative experiments results on other generic datasets show that our method has good scalability and portability. The ablation studies also show that the proposed hierarchical structure and multi-model fusion strategy contribute to the precision of our model.

Advancing document-level event extraction: Integration across texts and reciprocal feedback.

The primary objective of document-level event extraction is to extract relevant event information from lengthy texts. However, many existing methods for document-level event extraction fail to fully incorporate the contextual information that spans across sentences. To overcome this limitation, the present study proposes a document-level event extraction model called Integration Across Texts and Reciprocal Feedback (IATRF). The proposed model constructs a heterogeneous graph and employs a graph convolutional network to enhance the connection between document and entity information. This approach facilitates the acquisition of semantic information enriched with document-level context. Additionally, a Transformer classifier is introduced to transform multiple event types into a multi-label classification task. To tackle the challenge of event argument recognition, this paper introduces the Reciprocal Feedback Argument Extraction strategy. Experimental results conducted on both our COSM dataset and the publicly available ChFinAnn dataset demonstrate that the proposed model outperforms previous methods in terms of F1 value, thus confirming its effectiveness. The IATRF model effectively solves the problems of long-distance document context-aware representation and cross-sentence argument dispersion.

Framing events in the logic of verbal modification

I ask what a small set of modification data requires of clausal event semantics. Classic Davidsonian semantics posits that modifiers like "in the hallway" express properties of events, and expects that iterations of such modifiers will simply contribute additional conjuncts at logical form. The data I consider challenges this view, and others cast in the Davidsonian spirit, at least so long as we hope to preserve an important and plausible semantic principle, Role Exhaustion (Williams 2015). As I show, preserving the principle and accounting for the facts can be accomplished by adopting two independently-motivated sets of claims: first, that verbs introduce existential closure over their event argument, and modifiers take verb meanings as semantic arguments (Champollion 2015); second, that simple clauses have two layers of event description, "framing" and "framed" (Schein 2016). In the end, I sketch two possible extensions of the approach, towards the interpretation of temporal modification and negative perceptual reports.

Multi-Turn and Multi-Granularity Reader for Document-Level Event Extraction

Most existing event extraction works mainly focus on extracting events from one sentence. However, in real-world applications, arguments of one event may scatter across sentences and multiple events may co-occur in one document. Thus, these scenarios require document-level event extraction (DEE), which aims to extract events and their arguments across sentences from a document. Previous works cast DEE as a two-step paradigm: sentence-level event extraction (SEE) to document-level event fusion. However, this paradigm lacks integrating document-level information for SEE and suffers from the inherent limitations of error propagation. In this article, we propose a multi-turn and multi-granularity reader for DEE that can extract events from the document directly without the stage of preliminary SEE. Specifically, we propose a new paradigm of DEE by formulating it as a machine reading comprehension task (i.e., the extraction of event arguments is transformed to identify the answer span from the document). Beyond the framework of machine reading comprehension, we introduce a multi-turn and multi-granularity reader to capture the dependencies between arguments explicitly and model long texts effectively. The empirical results demonstrate that our method achieves superior performance on the MUC-4 and the ChFinAnn datasets.

Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis

The advance of knowledge graphs can bring tangible benefits to the fault detection of industrial robots. However, the construction of the KG for industrial robot fault detection is still in its infancy. In this paper, we propose a top-down approach to constructing a knowledge graph from robot fault logs. We define the event argument classes for fault phenomena and fault cause events as well as their relationship. Then, we develop the event logic ontology model. In order to construct the event logic knowledge extraction dataset, the ontology is used to label the entity and relationship of the fault detection event argument in the corpus. Additionally, due to the small size of the corpus, many professional terms, and sparse entities, a model for recognizing entities for robot fault detection is proposed. The accuracy of the entity boundary determination of the model is improved by combining multiple text features and using the relationship information. Compared with other methods, this method can significantly improve the performance of entity recognition of the dataset.

Combining NSP and NER for public opinion event extraction model

Event extraction in the field of public opinion aims to extract important event arguments and their corresponding roles from the moment-to-moment generated opinion reports. Most of the existing research methods divide the task into three subtasks: event trigger extraction, event type detection, and event argument extraction. Despite the remarkable achievements of the event argument extraction paradigm combining part-of-speech (POS) and event trigger features, the performance of POS features in combinatorial event argument extraction tasks is struggling due to its inherent semantic diversity in Chinese. In addition, previous research work ignored the deep semantic interaction between event trigger and text. To address the aforementioned problems, this paper proposes an opinion event extraction model (NN-EE) combining NSP and NER, which alleviates the lack of performance of combinatorial event argument extraction by introducing NER technology. Meanwhile, the event trigger features are incorporated into the NSP mechanism of the pre-trained language model BERT to prompt the model to learn the deep semantic interaction between the event trigger and original text. The results of the self-constructed food opinion report dataset (FD-OR) in this paper show that the NN-EE model achieves optimal performance.

Document-level event argument extraction with self-augmentation and a cross-domain joint training mechanism

Document-level event argument extraction (EAE), a critical task for event knowledge acquisition, seeks to find event arguments beyond the sentence level. Previous approaches for this task adopt supervised learning, which suffers from the data scarcity issue, especially in low-resource situations. To address this challenge, we propose a new method for document-level EAE that addresses the data scarcity from two perspectives: self-augmentation and cross-domain joint training. On the one hand, our method can generate additional training samples from an existing dataset using pre-trained language models, considerably expanding the training set; on the other hand, our method can learn with out-of-domain datasets with varying event schema, using a cross-domain joint training framework. To address data quality problems and domain mismatches, we also provide a new noise filtering method based on a teacher-student framework. The extensive experiments demonstrate that our approach delivers state-of-the-art performance on benchmark datasets, and that it is especially effective in low-resource situations. • A new approach for resolving the data sparsity issue in document-level event argument extraction from two complementing viewpoints. • A self-augmentation technique that combines pre-trained language models and a label-conditioned pre-training procedure to retain word-label consistency. • A cross-domain joint training framework for knowledge transfer from datasets of varied granularity, task specifications, and the event schema description language. • A novel noise filtering method in teacher-student framework to mitigate the data quality issue. • State-of-the-art performance on standard datasets.

Trigger is Non-central: Jointly event extraction via label-aware representations with multi-task learning

Event extraction (EE) occupies an important position in information extraction. Recently, deep neural network methods have been demonstrated to learn potential features well. However, existing networks for EE suffer from the following challenges: (1) Event argument extraction relies heavily on the classification of event triggers. (2) Previous works fail to exploit the predefined label representations for EE. (3) The interactive information between candidate arguments has not been fully exploited. To address the above mentioned problems, in this paper, we propose an advanced multi-task learning framework, named TNC, based on a fresh concept proposed by us: Trigger is Non-Central, in which event argument extraction no longer depends on the event triggers but is performed synchronously with it. Our TNC extracts multiple event triggers and arguments simultaneously by adopting label representations and an auxiliary task, named Sentence Event Identification (SEI), which is devised to extract the event types contained in a sentence. In addition, we design a special symbol to merge the representation of candidate arguments over the Transformer encoder. We experiment on the widely used ACE 2005 corpora and TAC-KBP 2015, and the experimental results have proved that our model achieves state-of-the-art compared to other models, with higher effectiveness and adaptability.

A syntactic distance sensitive neural network for event argument extraction

A syntactic distance sensitive neural network for event argument extraction

Learning to Ask for Data-Efficient Event Argument Extraction (Student Abstract)

Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles. In this study, we cast EAE as a question-based cloze task and empirically analyze fixed discrete token template performance. As generating human-annotated question templates is often time-consuming and labor-intensive, we further propose a novel approach called “Learning to Ask,” which can learn optimized question templates for EAE without human annotations. Experiments using the ACE-2005 dataset demonstrate that our method based on optimized questions achieves state-of-the-art performance in both the few-shot and supervised settings.

Conditional Probability Joint Extraction of Nested Biomedical Events: Design of a Unified Extraction Framework Based on Neural Networks.

BackgroundEvent extraction is essential for natural language processing. In the biomedical field, the nested event phenomenon (event A as a participating role of event B) makes extracting this event more difficult than extracting a single event. Therefore, the performance of nested biomedical events is always underwhelming. In addition, previous works relied on a pipeline to build an event extraction model, which ignored the dependence between trigger recognition and event argument detection tasks and produced significant cascading errors.ObjectiveThis study aims to design a unified framework to jointly train biomedical event triggers and arguments and improve the performance of extracting nested biomedical events.MethodsWe proposed an end-to-end joint extraction model that considers the probability distribution of triggers to alleviate cascading errors. Moreover, we integrated the syntactic structure into an attention-based gate graph convolutional network to capture potential interrelations between triggers and related entities, which improved the performance of extracting nested biomedical events.ResultsThe experimental results demonstrated that our proposed method achieved the best F1 score on the multilevel event extraction biomedical event extraction corpus and achieved a favorable performance on the biomedical natural language processing shared task 2011 Genia event corpus.ConclusionsOur conditional probability joint extraction model is good at extracting nested biomedical events because of the joint extraction mechanism and the syntax graph structure. Moreover, as our model did not rely on external knowledge and specific feature engineering, it had a particular generalization performance.

Constructing a cross-document event coreference corpus for Dutch

Event coreference resolution is a task in which different text fragments that refer to the same real-world event are automatically linked together. This task can be performed not only within a single document but also across different documents and can serve as a basis for many useful Natural Language Processing applications. Resources for this type of research, however, are extremely limited. We compiled the first large-scale dataset for cross-document event coreference resolution in Dutch, comparable in size to the most widely used English event coreference corpora. As data for event coreference is notoriously sparse, we took additional steps to maximize the number of coreference links in our corpus. Due to the complex nature of event coreference resolution, many algorithms consist of pipeline architectures which rely on a series of upstream tasks such as event detection, event argument identification and argument coreference. We tackle the task of event argument coreference to both illustrate the potential of our compiled corpus and to lay the groundwork for a Dutch event coreference resolution system in the future. Results show that existing NLP algorithms can be easily retrofitted to contribute to the subtasks of an event coreference resolution pipeline system.

Application Analysis of Emotional Learning Model Based on Improved Text in Campus Review and Student Public Opinion Management

In an era of rapid mobile Internet development, students are increasingly expressing their views on specific events through campus comments. In the Web 2.0 era, the concept of public media participation is widely used by students, and it is important to effectively analyze online campus public opinion comments and present the findings in a rationalized form for the management of campus public opinion. In the new era of education governance modernization, the level of public opinion management in universities has become a key indicator to improve the standard of education management in higher education institutions; therefore, this paper focuses on proposing an innovative deep learning-based research method for topic management of university public opinion. Firstly, through the improved LDA module with sentiment discrimination learning capability, the sentiment of the main arguments in the campus commentary is extracted, and then the statistical sentiment intensity of the in-depth learning module is used to analyze the sentiment intensity of the thematic arguments of different events in time series, so as to achieve the long-term tracking of the trend of the sentiment intensity of the whole event.

Using Semi-Supervised Learning and Wikipedia to Train an Event Argument Extraction System

The paper presents a methodology for training an event argument extraction system in a semi-supervised setting. We use Wikipedia and Wikidata to automatically obtain a small noisily labeled dataset and a large unlabeled dataset. The dataset consists of event clusters containing Wikipedia pages in multiple languages. The unlabeled data is iteratively labeled using semi-supervised learning combined with probabilistic soft logic to infer the pseudo-label of each example from the predictions of multiple base learners. The proposed methodology is applied to Wikipedia pages about earthquakes and terrorist attacks in a cross-lingual setting. Our experiments show improvement of the results when using the proposed methodology. The system achieves F1-score of 0.79 when only the automatically labeled dataset is used, and F1-score of 0.84 when trained according to the methodology with semi-supervised learning combined with probabilistic soft logic.

Document-level event argument linking as machine reading comprehension

Document-level event argument linking aims to find global event arguments to fill an event’s semantic role, which is a challenging task owing to the appearance of long contexts and the issue of data sparsity. In this paper, we study a new formulation to address the above challenges in document-level EAL, by explicitly framing the task as a machine reading comprehension (MRC) problem. In this formulation, argument extraction is viewed as a question answering procedure. To better transfer each semantic role into a question, we propose a back-translation based query generation method, which can effectively generate well-formed questions without adopting huge human effort. Moreover, to better capture the non-local dependencies between triggers and arguments, we devise a dependency-guided question answering process, which can explore the underlying structure of the document to boost learning. The extensive experiments on a benchmark have justified the effectiveness of our approach. Particularity, our approach achieves substantially improvement over previous methods, leading to +5.7% in F1 in the full argument linking setting. Moreover, our approach is particular data-efficient and demonstrates superior performance in the data-low scenario with limited training data.

Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault Diagnosis

Knowledge graph technology has important guiding significance for efficient and orderly fault diagnosis of robot transmission system. Taking the historical robot maintenance logs of robot transmission system as the research object, a top-down fault diagnosis event logic knowledge graph construction method is proposed. Firstly, we define event arguments of fault phenomenon and fault cause events, define event argument classes and relation between classes, and construct an event logic knowledge ontology model. According to the event logic knowledge ontology, the fault diagnosis event argument entity and relation in the corpus are labeled, and an event logic knowledge extraction dataset is formed. Secondly, an event argument entity and relation joint extraction model is proposed. Using stacked bidirectional long short-term memory(BiLSTM) to obtain deep context features of text. As a supplement to stacked BiLSTM, self-attention mechanism extracts character dependency features from multiple subspaces, and uses conditional random field(CRF) to realize entity recognition. The character dependency features are mapped to the entity label weight embedding, and spliced with deep context features to extract relations. Bidirectional graph convolutional network(BiGCN) is introduced for relation inference, graph convolution features are used to update deep context features to perform joint extraction in the second phase. Experimental results show that this method can improve the effect of event argument entity and relation joint extraction and is better than other methods. Finally, an event logic knowledge graph of robot transmission system fault diagnosis is constructed, which provides decision support for autonomous fault diagnosis of robot transmission system.

Document-level Core Events Extraction based on QA

Document-level core events extraction model based on QA is provided in this paper. Question with prior information is constructed input into the BTBiLSTM model. Firstly, extract events from documents. Secondly finds event arguments from the text to be extracted. Lastly fuse events to select core events. The experimental results on ACE2005 Chinese corpus show that the event argument role question with prior information has a good effect on event argument extraction. Compared with existing method, our method in this paper realizes cross sentences events arguments extraction, it also applicable to documents without headline.

Balancing Precision and Recall for Neural Biomedical Event Extraction

Biomedicalevent extraction is an essential task in the biomedical research. Existing models suffer from the issue of low recall due to the large proportion of unrecognized events and inflexible event argument combination. To address this issue, we propose an end-to-end multi-task approach for biomedical event extraction. Our model is able to achieve balanced precision and recall with several nichetargeting designs. First, neural encoders with rich lexical and syntactic features are used and shared by multiple subtasks such as event trigger recognition and argument relation extraction, in order to enhance the generalizability of the model. Second, a novel auxiliary subtask is added to identify the proteins that participate in the events, which helps decreasing the challenge of mining event-related proteins from the large candidate space. Third, event argument combination is performed using a strong neural network rather than inflexible rules or templates, to further increase the recall, especially for complex nested events. To demonstrate the effectiveness of our model, we evaluate it on two widely-used biomedical event extraction datasets used in the BioNLP 2011 and 2013 shared tasks. Our model achieves the state-of-the-art results (63.15% and 55.67% in F1 score) by significantly improving the recalls (compared with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepEvnetMine<inline-formula><tex-math notation="LaTeX">$_{SciBERT}$</tex-math></inline-formula></i> , 4.65% and 5.0%) on the two datasets. Further experiments and analyses show the effectiveness of our proposed features and modules in the model.