Event Extraction Research Articles

MAPPING THE SKIES: ANALYZING AEROSPACE INDUSTRY TRENDS USING EVENT MAPS

Accurate event extraction and analysis are vital for understanding trends and making informed decisions in various domains. This paper presents a concise rule-based approach for event extraction in the aerospace domain, aiming to enhance decision-making processes. The proposed methodology utilizes rule-based pattern-matching techniques to identify and extract events from textual data, such as news articles and research papers. By leveraging linguistic features and syntactic structures, the approach effectively captures event-related information. Additionally, an event map visualization technique is introduced to provide a comprehensive overview of the extracted events and their relationships. The proposed approach, integrated into the Aerospace Expert System (AES), offers a powerful tool for tracking and analyzing events in the aerospace industry. Evaluation results demonstrate the high precision and recall of the rule-based approach, enabling stakeholders to gain valuable insights and make informed decisions in the aerospace domain.

Event Extraction by Associating Event Types and Argument Roles

Event Extraction by Associating Event Types and Argument Roles

AI-technology for efficient noise monitoring and analysis in complex urban soundscapes

This paper presents a user-friendly, project-based noise monitoring and data analysis software called NorCloud. This software has been developed to exploit the advantages of noise monitoring terminals that allow localizing dominant sound sources in complex urban soundscapes. NorCloud can be used with multiple microphones simultaneously and has data analysis features that enable identification, classification and extraction of sound events. The efficiency of these processes is greatly increased by using AI-technology for automatic removal of non-relevant sound sources. In the article, the advantages and limitations of using automatic noise monitoring and analysis systems are explained. Current applications and possibilities for further development are also presented.

A Synergistic Bidirectional LSTM and N-gram Multi-channel CNN Approach Based on BERT and FastText for Arabic Event Identification

Event extraction from texts continues to pose a challenge for many NLP systems. This article presents a novel neural network architecture that can extract and classify events from Arabic sentences. The model combines word representations and Part-Of-Speech (POS) tags and uses a bidirectional LSTM layer and a dual combined convolutional neural network. The first layer of the network focuses on sentence representations, while the second layer focuses on POS representations. The model takes advantage of both N-gram character features from FastText and contextual representations from bidirectional encoder representations from transformers. This combination proves to be successful, as evidenced by the good results obtained from evaluating the model on the Arabic TimeML corpus. Our results show that combining both contextual and N-gram representations outperforms the traditional skip-gram model.

Global information enhancement and subgraph-level weakly contrastive learning for lightweight weakly supervised document-level event extraction

Document-level event extraction (DEE) is a branch of information extraction task that is valuable but difficult at present. To effectively implement DEE, previous works with better performance are often accompanied by larger parameters and computation. In this paper, we propose a novel framework based on global information enhancement and subgraph-level weakly contrastive learning, which bring better performance at lower extra computing costs. We verify the effectiveness of our method in a lightweight DEE model. The experimental results indicate that our proposed method outperforms other approaches in terms of the F1 score for both overall event extraction evaluation and single event extraction. In the inference phase, our model uses only 25% of the GPU memory required by the optimal model and maintains the advantage of 6.4 times faster inference speed.

Joint Overlapping Event Extraction Model via Role Pre-Judgment with Trigger and Context Embeddings

The objective of event extraction is to recognize event triggers and event categories within unstructured text and produce structured event arguments. However, there is a common phenomenon of triggers and arguments of different event types in a sentence that may be the same word elements, which poses new challenges to this task. In this article, a joint learning framework for overlapping event extraction (ROPEE) is proposed. In this framework, a role pre-judgment module is devised prior to argument extraction. It conducts role pre-judgment by leveraging the correlation between event types and roles, as well as trigger embeddings. Experiments on the FewFC show that the proposed model outperforms other baseline models in terms of Trigger Classification, Argument Identification, and Argument Classification by 0.4%, 0.9%, and 0.6%. In scenarios of trigger overlap and argument overlap, the proposed model outperforms the baseline models in terms of Argument Identification and Argument Classification by 0.9%, 1.2%, 0.7%, and 0.6%, respectively, indicating the effectiveness of ROPEE in solving overlapping events.

Event Extraction with Spectrum Estimation Using Neural Networks Linear Methods

The timely extraction of event information from enormous amounts of textual data is a critical research problem. Event extraction using deep learning technology has gained academic attention as a result of the fast growth of deep learning. Event extraction requires costly, expert-level, high-quality human annotations. As a result, developing a data-efficient event extraction model that can be trained using only a few labelled samples has emerged as a key difficulty. Existing research work focuses mainly on the structured data with supervised models. The proposed work focuses on Movie Scene Event Extraction, a practical media analysis problem that seeks to extract structured events from unstructured movie screenplays. We suggest using the correlation between various argument roles in situations where different argument roles in a movie scene share similar qualities. This can be beneficial to the Movie Scene Argument Extraction (argument classification and identification) and film scene trigger extraction (Trigger recognition and classifying). In order to represent the relation between different roles in argument and their respective roles, we propose a Superior Concept of Role (SRC) as a top-level idea of a role that is based on the classic argument role, as well as an SRC-based Graph Attention System (GAT). To assess the efficacy of the model we designed, we constructed the dataset MovieSceneEvent to extract movies’ scene-related events. Additionally, we conducted tests on an existing dataset in order to compare results with different models. Results from the experiments like extraction of words, aspect keywords from the documents indicate that our model does better than other models. Furthermore, the information on the relationship between the argument roles helps improve the effectiveness of film scene extraction of events.

Алгоритм построения дерева синтаксических единиц русскоязычного предложения по дереву синтаксических связей

Automatic syntactic analysis of a sentence is an important computational linguistics task. At present, there are no syntactic structure parsers for Russian that are publicly available and suitable for practical applications. Ground-up creation of such parsers requires building of a treebank annotated according to a given formal grammar, which is quite a cumbersome task. However, since there are several syntactic dependency parsers for Russian, it seems reasonable to employ dependency parsing results for syntactic structure analysis. The article introduces an algorithm that allows to construct the constituency tree of a Russian sentence by a syntactic dependency tree. The formal grammar used by the algorithm is based on the D.E. Rosenthal’s classic reference. The algorithm was evaluated on 300 Russian-language sentences. 200 of them were selected from the aforementioned reference, and 100 from OpenCorpora, an open corpus of sentences extracted from Russian news and periodicals. During the evaluation, the sentences were passed to syntactic dependency parsers from Stanza, SpaCy, and Natasha packages, then the resulted dependency trees were processed by the proposed algorithm. The obtained constituency trees were compared with the trees manually annotated by experts in linguistics. The best performance was achieved using the Stanza parser: the constituency parsing F1–score was 0.85, and the sentence parts tagging accuracy was 0.93, that would be sufficient for many practical applications, such as event extraction, information retrieval and sentiment analysis.

A tree-like structured perceptron for transition-based biomedical event extraction

Event extraction in the biomedical domain has many complex situations, such as nested events, overlapping events, and multiple processing streams. For the problem of massive nested and multi-level nested events in the corpus, existing approaches generally transform these nested events into judgments of the relationships between triggers, resulting ineffective utilization of the complete event information. Similarly, when confronted with the issue of overlapping events in the corpus, the prevailing approach often recognizes only one of the overlapping events while neglecting the other. Due to the complexity of the biomedical events, their effectiveness is unsatisfactory. Biomedical events, which contain nested and overlapping events, can be conceptualized as a tree structure. In light of this, we present a novel tree-like structured perceptron designed for transition-based biomedical event extraction, which converts event predictions into sequences of transition operations. This approach enables the incremental construction of nested events, with each operation relying on the already constructed event and surrounding contextual features. We evaluate the proposed model on two tasks: Genia 2011 and Genia 2013. The experimental results show a 0.22% and 0.04% improvement in F1 score, respectively, compared to the currently better performing baseline models without using external resources.

KC-GEE: knowledge-based conditioning for generative event extraction

AbstractEvent extraction is an important, but challenging task. Many existing techniques decompose it into event and argument detection/classification subtasks, which are complex structured prediction problems. Generation-based extraction techniques lessen the complexity of the problem formulation and are able to leverage the reasoning capabilities of large pretrained language models. However, they still suffer from poor zero-shot generalizability and are ineffective in handling long contexts such as documents. We propose a generative event extraction model, KC-GEE, that addresses these limitations. A key contribution of KC-GEE is a novel knowledge-based conditioning technique that injects the schema of candidate event types as the prefix into each layer of an encoder-decoder language model. This enables effective zero-shot learning and improves supervised learning. Our experiments on two benchmark datasets demonstrate the strong performance of our KC-GEE model. It achieves particularly strong results in the challenging document-level extraction task and in the zero-shot learning setting, outperforming state-of-the-art models by up to 5.4 absolute F1 points.

Classification of events from [formula omitted]-induced reactions in the MUSIC detector via statistical and ML methods

The Multi-Sampling Ionization Chamber (MUSIC) detector is typically used to measure nuclear reaction cross sections relevant for nuclear astrophysics, fusion studies, and other applications. From the MUSIC data produced in one experiment scientists carefully extract an order of 103 events of interest from about 109 total events, where each event can be represented by an 18-dimensional vector. However, the standard data classification process is based on expert-driven, manually intensive data analysis techniques that require several months to identify patterns and classify the relevant events from the collected data. To address this issue, we present a method for the classification of events originating from specific α-induced reactions by combining statistical and machine learning methods that require significantly less input from the domain scientist, relative to the standard technique. We applied the new method to two experimental data sets and compared our results with those obtained using traditional methods. With few exceptions, the new method yields results such that the percent change with respect to results obtained with traditional methods are within ±20%. With the present method, which is the first of its kind for the MUSIC data, we have established the foundation for the automated extraction of physical events of interest from experiments using the MUSIC detector.

Research on the construction of event corpus with document-level causal relations for social security

Event corpora are imperative to train event extraction models. Currently, most existing event corpora suffer from being available only in English, and their construction is limited by high annotation costs. This paper aims to construct a corpus that concerns social security causality events in Chinese and proposes a faster and less expensive construction method. The contributions are as follows: (i) An event corpus SSECau for the social security field in Chinese is constructed. They are from 2,235 web texts and microblogs, with event causality annotated at the document level. (ii) A corpus construction method with manual annotation and machine pre-tagging is proposed to improve accuracy and speed. (iii) A pre-tagging method based on BiLSTM-CRF (bidirectional long short-term memory and conditional random field) is deployed to extract events automatically. The experimental results show the best consistency between automatic pre-tagging and manual annotation can reach up to 82 %; while the dynamic tagging process improves both the labeling speed and accuracy. The SSECau corpus can aid the development and evaluation of event extraction models for the social security field; annotated cause-effect relationships at the document level can potentially enhance the training of complex extraction models; the proposed dynamic process with pre-tagging can serve as a reference for future corpus construction.

Constructing and Interpreting Causal Knowledge Graphs from News

Many financial jobs rely on news to learn about causal events in the past and present, to make informed decisions and predictions about the future. With the ever-increasing amount of news available online, there is a need to automate the extraction of causal events from unstructured texts. In this work, we propose a methodology to construct causal knowledge graphs (KGs) from news using two steps: (1) Extraction of Causal Relations, and (2) Argument Clustering and Representation into KG. We aim to build graphs that emphasize on recall, precision and interpretability. For extraction, although many earlier works already construct causal KGs from text, most adopt rudimentary pattern-based methods. We close this gap by using the latest BERT-based extraction models alongside pattern-based ones. As a result, we achieved a high recall, while still maintaining a high precision. For clustering, we utilized a topic modelling approach to cluster our arguments, so as to increase the connectivity of our graph. As a result, instead of 15,686 disconnected subgraphs, we were able to obtain 1 connected graph that enables users to infer more causal relationships from. Our final KG effectively captures and conveys causal relationships, validated through experiments, multiple use cases and user feedback.

Multi-Information Preprocessing Event Extraction With BiLSTM-CRF Attention for Academic Knowledge Graph Construction

Academic knowledge graph is an important application of knowledge graph in the vertical field of academia. At present, the construction of the academic knowledge graph is mainly completed by extracting published academic papers, authors, publications, and other information from related databases. However, academic information is not just information of published papers. Scholars’ academic activities include participation in academic conferences, visiting and making presentation, and so on. However, the above academic information is hidden in natural language texts and cannot be directly stored in academic knowledge graph. This article proposes an approach named construct-SCHOLAT knowledge graph to construct an academic event knowledge graph based on academic social network SCHOLAT. The construction framework mainly consists of two parts: data preprocessing and event extraction. In the data preprocessing, we propose a knowledge graph embedding method to represent scholars’ academic social feature. In the event extraction, we concatenate the preprocessed scholar vector with academic we-media blog text into the extraction model based on BiLSTM-CRF fused with attention mechanism. The extracted events are added to academic knowledge graph, and a public relationship exists between the event and the scholar. Compared to the previous methods, our framework has an excellent performance after experimental verification. To the best of our knowledge, this is the first study to use the scholar academic social information of the scholar who edited the text as the event extraction input information. In addition, we publish a Chinese event extraction dataset SCHOLAT academic event extraction.https://www.scholat.com/research/opendata The dataset includes academic we-media blog and the social behavior embedding vector of the scholar. All the data in this dataset are derived from the academic social network SCHOLAT.

Event Extraction With Dynamic Prefix Tuning and Relevance Retrieval

We consider event extraction in a generative manner with template-based conditional generation. Although there is a rising trend of casting the task of event extraction as a sequence generation problem with prompts, these generation-based methods have several significant challenges, including using suboptimal prompts, static event type information, and the overwhelming number of irrelevant event types. In this paper, we propose a generative template-based method with dynamic prefixes and a relevance retrieval framework for event extraction ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GREE</small> ) by first integrating context information with type-specific prefixes to learn a context-specific prefix for each context, and then retrieving the relevant event types with an adaptive threshold. Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OneIE</small> on ACE 2005 and achieves the best performances on ERE. Additionally, our model is proven to be portable to new types of events effectively.

Multipitch estimation based on the iterative detection and separation of note events from single-channel polyphonic recordings.

Multiple fundamental frequency estimation has been extensively used in applications such as melody extraction, music transcription, instrument identification, and source separation. This paper presents an approach based on the iterative detection and extraction of note events, which are considered to be harmonic sounds characterised by a continuous pitch trajectory. Note events are assumed to be associated with musical notes being played by a single instrument, and their pitch trajectories are iteratively estimated. In every iteration, the pitch contour of the predominant note event is selected from a set of pitch estimates and used to separate its spectral energy from the input mixture in order to obtain a residual signal, which is then used as input in the next iteration. This iterative process stops when the energy of the residual is below a significance threshold. The pitch trajectories of all detected note events are then revised and reassembled to form the final set of pitch estimates for the original audio input. Evaluation of performance is conducted in different scenarios to show the potential of the proposed system, both in terms of its accuracy, and also as an initial stage in other complex tasks, such as note tracking and multipitch streaming.

Novel algorithms for improved detection and analysis of fluorescent signal fluctuations

Fluorescent dyes and genetically encoded fluorescence indicators (GEFI) are common tools for visualizing concentration changes of specific ions and messenger molecules during intra- as well as intercellular communication. Using advanced imaging technologies, fluorescence indicators are a prerequisite for the analysis of physiological molecular signaling. Automated detection and analysis of fluorescence signals require to overcome several challenges, including correct estimation of fluorescence fluctuations at basal concentrations of messenger molecules, detection, and extraction of events themselves as well as proper segmentation of neighboring events. Moreover, event detection algorithms need to be sensitive enough to accurately capture localized and low amplitude events exhibiting a limited spatial extent. Here, we present two algorithms (PBasE and CoRoDe) for accurate baseline estimation and automated detection and segmentation of fluorescence fluctuations.

A mode of action-based probabilistic framework of dose-response assessment for nonmutagenic liver carcinogens: a case study of PCB-126.

A main function of dose-response assessment is to estimate a "safe" dose in the target population to support chemical risk assessment. Typically, a "safe" dose is developed differently for cancer and noncancer effects based on a 2-step procedure, ie, point of departure (POD) derivation and low-dose extrapolation. However, the current dose-response assessment framework is criticized for its dichotomized strategy without integrating the mode of action (MOA) information. The objective of this study was, based on our previous work, to develop a MOA-based probabilistic dose-response framework that quantitatively synthesizes a biological pathway in a dose-response modeling process to estimate the risk of chemicals that have carcinogenic potential. 3,3',4,4',5-Pentachlorobiphenyl (PCB-126) was exemplified to demonstrate our proposed approach. There were 4 major steps in the new modeling framework, including (1) key quantifiable events (KQEs) identification and extraction, (2) essential dose calculation, (3) MOA-based POD derivation, and (4) MOA-based probabilistic reference dose (RfD) estimation. Compared with reported PODs and traditional RfDs, the MOA-based estimates derived from our approach were comparable and plausible. One key feature of our approach was the use of overall MOA information to build the dose-response relationship on the entire dose continuum including the low-dose region. On the other hand, by adjusting uncertainty and variability in a probabilistic manner, the MOA-based probabilistic RfDs can provide useful insights of health protection for the specific proportion of population. Moreover, the proposed framework had important potential to be generalized to assess different types of chemicals other than nonmutagenic carcinogens, highlighting its utility to improve current chemical risk assessment.

Generating Chinese Event Extraction Method Based on ChatGPT and Prompt Learning

Regarding the scarcity of annotated data for existing event extraction tasks and the insufficient semantic mining of event extraction models in the Chinese domain, this paper proposes a generative joint event extraction model to improve existing models in two aspects. Firstly, it utilizes the content generation capability of ChatGPT to generate annotated data corpora for event extraction tasks and trains the model using supervised learning methods adapted to downstream tasks. Secondly, explicit entity markers and event knowledge are added to the text to construct generative input templates, enhancing the performance of event extraction. To validate the performance of this model, experiments are conducted on DuEE1.0 and Title2Event public datasets, and the results show that both data enhancement and prompt learning based on ChatGPT effectively improve the performance of the event extraction model, and the F1 values of the events extracted by the CPEE model proposed in this paper reach 85.1% and 59.9% on the two datasets, respectively, which are comparable to the existing models’ values of 1.3% and 10%, respectively; moreover, on the Title2Event dataset, the performance of different models on the event extraction task can be gradually improved as the data size of the annotated corpus of event extraction generated using ChatGPT increases.

A joint event extraction model based on RoBERTa-wwm-ext and gating mechanism

Event extraction, as one of the difficult tasks of information extraction, can quickly obtain valuable information from the massive information on the Internet. This paper proposes a joint event extraction model based on RoBERTa-wwm-ext and gating mechanism for document-level long text data, which not only uses the prior knowledge from event types and pre-trained language models, but also uses gated fusion module to aggregate information in the event argument extraction tasks to enhance entity representation and splices entity type embedding, thereby enhancing the correlation among events, arguments and argument roles in the text, and improving the recognition accuracy of the arguments of each event in the document. Finally, the effectiveness of the model is verified on the public dataset.