Search Results (9)

The Chinese government releases a large number of enterprise-friendly policy documents every year, but due to the dispersed policy release channels and complex document parsing, enterprises need to invest a lot of human and material resources to analyze them, which results in many enterprises missing the opportunity to file. To solve this problem, this paper proposes a two-step cue learning-based event extraction framework (TPEE), which learns to recognize the role start and stop markers in the text by two prompts, and introduces a dynamic weighting mechanism to enhance the information interaction between roles. In the experiments, TPEE shows strong generalization ability on the publicly available financial dataset DuEE-Fin, and improves the F1-scores in argument recognition and classification tasks by 0.84% and 0.74%, respectively. In addition, based on the policy dataset DEE-Policy constructed by the authors, TPEE achieves F1-scores of 73.92% and 72.81% in the argument recognition and classification tasks, respectively, which are improved by 2.14% and 2.26% compared to the baseline model. The results show that the TPEE framework performs well in event extraction in the policy domain and provides technical support for enterprises to obtain policy information efficiently. Full article

For the extraction of judicial events for Tibetan, a low-resource language, traditional simple neural network approaches struggle to adequately capture the deep semantics and features of the texts because Tibetan texts are usually lengthy and contain numerous judicial-related entities. To overcome this limitation, this research presents an event extraction model combining deep word representation with hybrid neural networks for the Tibetan judicial domain. The model introduces the Chinese minority pre-trained language model (CINO), which generates dynamic word vector representations, addressing the challenge of modeling the deep semantics inherent in Tibetan texts. During feature extraction, a bidirectional long short-term memory network (BiLSTM) is applied to extract the temporal and contextual dependencies, while a convolutional neural network (CNN) is utilized to capture the local semantic features to construct a comprehensive global semantic representation. Finally, the sequences are decoded through conditional random field (CRF) to generate optimal prediction results, thus achieving the efficient extraction of Tibetan judicial events. The experimental findings indicate that the model outperforms the baselines by achieving F1 scores of 70.47% for trigger detection and 62.99% for argument recognition, with improvements of 16.6% and 16.42%, respectively. These results confirm the effectiveness and superiority of the proposed model. Full article

Within the vast landscape of the Built Environment, where challenges of uncertainty abound, this paper ventures into a detailed exploration of antifragile planning. Antifragility, a concept rooted in the capacity of systems to not only withstand but also thrive in the face of volatility, stands as a beacon of resilience amidst the uncertainties of the Anthropocene. The paper offers a systematic examination of antifragile planning, specifically by concentrating on uncertainty as one of its key theoretical tenets and by exploring the implications of these principles within the context of the Anthropocene. After offering a systematic and comprehensive review of the literature, the analysis delves into several important themes in antifragile planning, including the recognition of limited predictive reliability, critiques of conventional responses to shocks such as urban resilience and smart cities, and the strategic elimination of potential fragilizers through a unique planning methodology. Furthermore, the paper discusses three key arguments challenging the efficacy of antifragility: the systemic approach, the classification of responses to perturbations, and the validity of adaptivity and optionality theses. Specifically, the gaps identified in the antifragile planning methodology reveal its shortcomings in addressing the complexity of cities, its failure to recognize the variety of responses to shocks and perturbations, and its neglect of broader urban relationalities, especially in relation to climate-induced uncertainty. Thus, it is asserted that antifragility remains urbocentric. For these reasons, the paper contends that rectifying the gaps detected in antifragility is necessary to address the uncertainty of the Anthropocene. By aligning largely with emerging posthumanist planning strategies, the paper emphasizes the significance of adopting a proactive approach that goes beyond merely suppressing natural events. This approach involves fostering urban intelligence, contextualizing urban materialities within broader planetary dynamics, and embracing exploratory design strategies that prioritize both the ethical and aesthetic dimensions of planning. Full article

As one of foundation technologies for massive data processing for AI, event mining is attracting more and more attention, mainly including event detection (event trigger identification and event classification) and argument extraction. At present, EE-GCN is one of the most effective methods for event detection. However, since EE-GCN only focuses on event detection, complete event multi-tuple extraction needs to be improved. Inspired by the EE-GCN event detection method, this paper proposes an effective event extraction method via graph convolutional network indication with a hierarchical argument selection strategy. The method mainly includes the following steps. (1) Based on the ACE2005 argument extraction template, a new argument extraction template is established for the Baidu event extraction dataset. (2) The trigger events and event classification detected by EE-GCN are used as indicators to determine the argument extraction template, and the alternative arguments are extracted via named entity recognition based on the determined template. (3) Making full use of the side information of EE-GCN graph to solve the local and global correlation degree, and based on the local and global correlation degrees, the final argument multi-tuple is determined. (4) Finally, several experiments are conducted on the Baidu event extraction dataset to compare the proposed method with other methods. The experimental results show that the proposed method has improved the accuracy and completeness of the event extraction compared to other existing methods. Full article

Extensive research has explored the perception of English lexical stress by Chinese EFL learners and tried to unveil the underlying mechanism of the prosodic transfer from a native tonal language to a non-native stress language. However, the role of the pitch as the shared cue by lexical stress and lexical tone during the transfer remains controversial when the segmental cue (i.e., reduced vowel) is absent. By employing event-related potential (ERP) measurements, the current study aimed to further investigate the role of the pitch during the prosodic transfer from L1 lexical tone to L2 lexical stress and the underlying neural responses. Two groups of adult Chinese EFL learners were compared, as both Mandarin and Cantonese are tonal languages with different levels of complexity. The results showed that Cantonese speakers relied more than Mandarin speakers on pitch cues, not only in their processing of English lexical stress but also in word recognition. Our findings are consistent with the arguments of Cue Weighting and attest to the influence of native tonal language experience on second language acquisition. The results may have implications on pedagogical methods that pitch could be an important clue in second language teaching. Full article

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. Full article

With the rapid development of the internet and social media, extracting emergency events from online news reports has become an urgent need for public safety. However, current studies on the text mining of emergency information mainly focus on text classification and event recognition, only obtaining a general and conceptual cognition about an emergency event, which cannot effectively support emergency risk warning, etc. Existing event extraction methods of other professional fields often depend on a domain-specific, well-designed syntactic dependency or external knowledge base, which can offer high accuracy in their professional fields, but their generalization ability is not good, and they are difficult to directly apply to the field of emergency. To address these problems, an end-to-end Chinese emergency event extraction model, called EmergEventMine, is proposed using a deep adversarial network. Considering the characteristics of Chinese emergency texts, including small-scale labelled corpora, relatively clearer syntactic structures, and concentrated argument distribution, this paper simplifies the event extraction with four subtasks as a two-stage task based on the goals of subtasks, and then develops a lightweight heterogeneous joint model based on deep neural networks for realizing end-to-end and few-shot Chinese emergency event extraction. Moreover, adversarial training is introduced into the joint model to alleviate the overfitting of the model on the small-scale labelled corpora. Experiments on the Chinese emergency corpus fully prove the effectiveness of the proposed model. Moreover, this model significantly outperforms other existing state-of-the-art event extraction models. Full article

Most existing medical event extraction methods have primarily adopted a simplex model based on either pattern matching or deep learning, which ignores the distribution characteristics of entities and events in the medical corpus. They have not categorized the granularity of event elements, leading to the poor generalization ability of the model. This paper proposes a diagnosis and treatment event extraction method in the Chinese language, fusing long short-level semantic dependency of the corpus, LSLSD, for solving these problems. LSLSD can effectively capture different levels of semantic information within and between event sentences in the electronic medical record (EMR) corpus. Moreover, the event arguments are divided into short word-level and long sentence-level, with the sequence annotation and pattern matching combined to realize multi-granularity argument recognition, as well as to improve the generalization ability of the model. Finally, this paper constructs a diagnosis and treatment event data set of Chinese EMRs by proposing a semi-automatic corpus labeling method, and an enormous number of experiment results show that LSLSD can improve the F1-value of event extraction task by 7.1% compared with the several strong baselines. Full article

Geoparser is a fundamental component of a Geographic Information Retrieval (GIR) geoparser, which performs toponym recognition, disambiguation, and geographic coordinate resolution from unstructured text domain. However, geoparsing of news articles which report several events across many place-mentions in the document are not yet adequately handled by regular geoparser, where the scope of resolution is either toponym-level or document-level. The capacity to detect multiple events and geolocate their true coordinates along with their numerical arguments is still missing from modern geoparsers, much less in Indonesian news corpora domain. We propose an event geoparser model with three stages of processing, which tightly integrates event extraction model into geoparsing and provides precise event-level resolution scope. The model casts the geotagging and event extraction as sequence labeling and uses LSTM-CRF inferencer equipped with features derived using Aggregated Topic Model from a large corpus to increase the generalizability. Throughout the proposed workflow and features, the geoparser is able to significantly improve the identification of pseudo-location entities, resulting in a 23.43% increase for weighted F1 score compared to baseline gazetteer and POS Tag features. As a side effect of event extraction, various numerical arguments are also extracted, and the output is easily projected to a rich choropleth map from a single news document. Full article