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13th workshop on graph-based methods for natural language processing, Textgraphs 2019, in conjunction with the 2019 Conference on Empirical methods in natural language processing and 9th International Joint Conference on natural language processing, EMNLP-IJCNLP 2019

作者： Ammanabrolu, Prithviraj Riedl, Mark O. School of Interactive Computing Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9781950737864

Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language. Prior work has demonstrated that using a knowledge graph as a state representation and question-answering to pre-train a deep Q-network facilitates faster control policy learning. In this paper, we explore the use of knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents. Our methods are tested across multiple computer generated and human authored games, varying in domain and complexity, and demonstrate that our transfer learning methods let us learn a higher-quality control policy faster. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： Knowledge graph

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graphBERT: Bridging graph and Text for Malicious Behavior Detection on Social Media

GraphBERT: Bridging Graph and Text for Malicious Behavior De...

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IEEE International Conference on Data Mining (ICDM)

作者： Jiele Wu Chunhui Zhang Zheyuan Liu Erchi Zhang Steven Wilson Chuxu Zhang Brandeis University MA USA

The development of social media (e.g., Twitter) allows users to make speeches with low cost and broad influence. Thus, social media has become a perfect place for users’ malicious behaviors like committing hate crimes, spreading toxic information, abetting crimes, etc. Malicious behaviors are covert and widespread, with potential relevance regarding topic, person, place, and so on. Therefore, it is necessary to develop novel techniques to detect and disrupt malicious behavior on social media effectively. Previous research has shown promising results in extracting semantic text (speech) representation using natural language processing methods. Yet the latent relation between speeches and the connection between users behind speeches is rarely explored. In light of this, we propose a holistic model named graph adaption BERT (graphBERT) to detect malicious behaviors on Twitter with both semantic and relational information. Specifically, we first present a novel and a large-scale corpus of tweet data to benefit both graph-based and language-based malicious behavior detection research. Then, we design a novel model graphBERT to learn comprehensive tweet and user representation with the integration of both semantic information encoded by transformers (i.e., BERT) and relational information encoded by graph neural network. graphBERT further leverages a weight adaption BERT module implemented between transformer layers to refine tweet embedding using relational information for malicious tweet classification. Finally, the adapted tweet embedding is used with the initial tweet representation to generate user embedding for malicious user detection. The extensive experiments on the collected Twitter data show that our model outperforms the state-of-the-art baseline methods for both tasks (i.e., malicious tweet classification and malicious user detection).

关键词： Adaptation models Social networking (online) Semantics Blogs Bit error rate Transformers Data models

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Evaluating research novelty detection: Counterfactual approaches 13

Evaluating research novelty detection: Counterfactual approa...

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作者： Amplayo, Reinald Kim Hwang, Seung-Won Song, Min Yonsei University Seoul Korea Republic of

ISBN: (纸本)9781950737864

In this paper, we explore strategies to evaluate models for the task research paper novelty detection: Given all papers released at a given date, which of the papers discuss new ideas and influence future research? We find the novelty is not a singular concept, and thus inherently lacks of ground truth annotations with cross-annotator agreement, which is a major obstacle in evaluating these models. Test-oftime award is closest to such annotation, which can only be made retrospectively and is extremely scarce. We thus propose to compare and evaluate models using counterfactual simulations. first, we ask models if they can differentiate papers at time t and counterfactual paper from future time t + d. Second, we ask models if they can predict test-of-time award at t + d. These are proxies that can be agreed by human annotators and easily augmented by correlated signals, using which evaluation can be done through four tasks: classification, ranking, correlation and feature selection. We show these proxy evaluation methods complement each other regarding error handling, coverage, interpretability, and scope, and thus altogether contribute to the observation of the relative strength of existing models. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： graphic methods

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On the Cost-Effectiveness of Stacking of Neural and Non-Neural methods for Text Classification: Scenarios and Performance Prediction

On the Cost-Effectiveness of Stacking of Neural and Non-Neur...

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Joint Conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint Conference on natural language processing (IJCNLP) / 6th workshop on Representation Learning for NLP (RepL4NLP)

作者： Gomes, Christian Goncalves, Marcos Andre Rocha, Leonardo Canuto, Sergio UFMG Brazil Belo Horizonte MG Brazil UFSJ Brazil Sao Joao Del Rei MG Brazil IFG Brazil Goiania Go Brazil

ISBN: (纸本)9781954085541

Neural network algorithms such as those based on transformers and attention models have excelled on Automatic Text Classification (ATC) tasks. However, such enhanced performance comes at high computational costs. Ensembles of simpler classifiers (i.e., stacking) that exploit algorithmic and representational complementarities have also been shown to produce top-notch performance in ATC, enjoying high effectiveness and potentially lower computational costs. In this context, we present the first and largest comparative study to exploit the cost-effectiveness of stacking of ATC classifiers, consisting of transformers and non-neural algorithms. In particular, we are interested in answering research questions such as: Is it possible to obtain an effective ensemble with significantly less computational cost than the best learning model for a given dataset? Disregarding the computational cost, can an ensemble improve the effectiveness of the best learning model? Besides answering such questions, another main contribution of this paper is the proposal of a low-cost oracle-based method that can predict the best ensemble in each scenario (with and without computational cost limitations) using only a fraction of the available training data.

关键词： Cost effectiveness

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Automatic detection of COVID-19 vaccine misinformation with graph link prediction

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JOURNAL OF BIOMEDICAL INFORMATICS 2021年 124卷 103955-103955页

作者： Weinzierl, Maxwell A. Harabagiu, Sanda M. Univ Texas Dallas Human Language Technol Res Inst Dept Comp Sci Richardson TX 75080 USA

Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major hurdle. Therefore, it is essential to automatically detect where misinformation about COVID-19 vaccines on social media is spread and what kind of misinformation is discussed, such that inoculation interventions can be delivered at the right time and in the right place, in addition to interventions designed to address vaccine hesitancy. This paper is addressing the first step in tackling hesitancy against COVID-19 vaccines, namely the automatic detection of known misinformation about the vaccines on Twitter, the social media platform that has the highest volume of conversations about COVID-19 and its vaccines. We present COVAXLIES, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines on which a novel method of detecting misinformation was developed. Our method organizes COVAXLIES in a Misinformation Knowledge graph as it casts misinformation detection as a graph link prediction problem. The misinformation detection method detailed in this paper takes advantage of the link scoring functions provided by several knowledge embedding methods. The experimental results demonstrate the superiority of this method when compared with classification-based methods, widely used currently.

关键词： natural language processing Machine learning COVID-19 vaccine misinformation Social Media knowledge graph embedding

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Cross-modal Clinical graph Transformer for Ophthalmic Report Generation

arXiv

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arXiv 2022年

作者： Li, Mingjie Cai, Wenjia Verspoor, Karin Pan, Shirui Liang, Xiaodan Chang, Xiaojun Department of Data Science and Artificial Intelligence Monash University Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University China School of Computing Technologies RMIT University Australia School of ISE Sun Yat-Sen University Peng Cheng National Lab China ReLER AAII University of Technology Sydney Australia

Automatic generation of ophthalmic reports using data-driven neural networks has great potential in clinical practice. When writing a report, ophthalmologists make inferences with prior clinical knowledge. This knowledge has been neglected in prior medical report generation methods. To endow models with the capability of incorporating expert knowledge, we propose a Cross-modal clinical graph Transformer (CGT) for ophthalmic report generation (ORG), in which clinical relation triples are injected into the visual features as prior knowledge to drive the decoding procedure. However, two major common Knowledge Noise (KN) issues may affect models' effectiveness. 1) Existing general biomedical knowledge bases such as the UMLS may not align meaningfully to the specific context and language of the report, limiting their utility for knowledge injection. 2) Incorporating too much knowledge may divert the visual features from their correct meaning. To overcome these limitations, we design an automatic information extraction scheme based on natural language processing to obtain clinical entities and relations directly from in-domain training reports. Given a set of ophthalmic images, our CGT first restores a sub-graph from the clinical graph and injects the restored triples into visual features. Then visible matrix is employed during the encoding procedure to limit the impact of knowledge. Finally, reports are predicted by the encoded cross-modal features via a Transformer decoder. Extensive experiments on the large-scale FFA-IR benchmark demonstrate that the proposed CGT is able to outperform previous benchmark methods and achieve state-of-the-art performances. Copyright © 2022, The Authors. All rights reserved.

关键词： Restoration

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面向中文的字词组合序列实体识别方法

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清华大学学报（自然科学版） 2023年第9期63卷 1326-1338页

作者：王庆人王银子仲红张以文安徽大学计算机科学与技术学院合肥230093

作为信息抽取的核心任务,命名实体识别能够从文本中识别不同类型命名实体。得益于深度学习在字词表示、特征提取方面的应用,中文命名实体识别任务取得了丰富研究成果。然而,中文命名实体识别任务依旧面临词汇信息缺乏的挑战,主要表现为... 详细信息

作为信息抽取的核心任务,命名实体识别能够从文本中识别不同类型命名实体。得益于深度学习在字词表示、特征提取方面的应用,中文命名实体识别任务取得了丰富研究成果。然而,中文命名实体识别任务依旧面临词汇信息缺乏的挑战,主要表现为:1)词汇边界信息和上下文语义信息未充分利用;2)字和自匹配词汇间语义信息未能有效捕获;3)图注意力网络输出信息中不同交互图信息的重要性未被考虑。该文提出一种面向中文的字词组合序列实体识别方法。采用字词组合序列嵌入结构,实现词汇边界信息以及字符与词汇间语义信息的充分捕捉;采用多图注意力融合架构,实现不同图神经网络提取特征重要性的区分。实验表明,相比已有经典方法,该方法在Weibo、Resume、OntoNotes4.0及MSRA四个数据集上的F1明显提升,在中文命名实体识别任务上具有可行性。

关键词：自然语言处理命名实体识别图注意力网络字词组合嵌入多图注意力

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Core Semantic first: A Top-down Approach for AMR Parsing 9

Core Semantic First: A Top-down Approach for AMR Parsing

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Conference on Empirical methods in natural language processing / 9th International Joint Conference on natural language processing (EMNLP-IJCNLP)

作者： Cai, Deng Lam, Wai Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781950737901

We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation (AMR): graph Spanning based Parsing (GSP). One novel characteristic of GSP is that it constructs a parse graph incrementally in a top-down fashion. Starting from the root, at each step, a new node and its connections to existing nodes will be jointly predicted. The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details. The core semantic first principle emphasizes capturing the main ideas of a sentence, which is of great interest. We evaluate our model on the latest AMR sembank and achieve the state-of-the-art performance in the sense that no heuristic graph re-categorization is adopted. More importantly, the experiments show that our parser is especially good at obtaining the core semantics.

关键词： Semantics

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《科学计算与MATLAB语言》课程知识图谱的构建

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智能计算机与应用 2025年第2期15卷 116-123页

作者：李子亮李兴春五邑大学电子与信息工程学院广东江门529020

在建设教育强国和推进教育数字化的背景下,知识图谱在教育领域展现出巨大的潜力。针对《科学计算与MATLAB语言》课程内容复杂、知识点分散,学生学习难度大等问题,研究分析该课程的知识结构和学习需求,提出构建课程知识图谱,以系统化、... 详细信息

在建设教育强国和推进教育数字化的背景下,知识图谱在教育领域展现出巨大的潜力。针对《科学计算与MATLAB语言》课程内容复杂、知识点分散,学生学习难度大等问题,研究分析该课程的知识结构和学习需求,提出构建课程知识图谱,以系统化、可视化的方法改进教学效果。采用Protégé本体编辑器构建知识图谱框架,并利用Neo4j图数据库进行可视化展示。通过Bi-LSTM+CRF模型提取关键知识点,利用关系图卷积网络(R-GCN)识别知识点之间的关联,并结合基于规则匹配的方法进行属性抽取,丰富知识图谱内容。基于该图谱,使用前后端分离方法开发整合教学资源与个性化学习等功能的教学平台。结果表明,基于课程专题一构建的知识图谱共包含242个节点,320条边,覆盖95%以上核心知识点,显著提升了学生的学习效率和教学效果,为高等教育数字化转型提供了参考,也为其他课程提供可借鉴的经验。

关键词：自然语言处理知识图谱 MATLAB语言关系图神经网络数字化教学

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A Framework For Scalable Similarity Evaluation in Text graphs

A Framework For Scalable Similarity Evaluation in Text Graph...

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International Conference on Web Research (ICWR)

作者： Mahdi Samani Nasser Ghadiri Isfahan University of Technology Isfahan Iran

graphs and graph databases are applicable over a wide range of domains, including text mining and web mining. Using graphs to represent relationships between entities provides enriched models for emerging tasks of web search and information retrieval. natural language processing algorithms use graphs to model structural relationships of texts efficiently, resulting in improved performance. However, the need to increase the accuracy of graph construction and weight allocation remains a fundamental challenge. Existing methods for these tasks provide limited efficiency and lack scalability for large graphs. In this study, we propose a novel graph-based method for text modeling and running a query to evaluate the similarity of text segments. In this method, the graph corresponding to the text is first created by modeling words and named entities by the state-of-the-art pre-trained BERT model. graph nodes are then weighted in two stages. In the first stage, the nodes with more generalization obtain higher weights. The second weighting stage is done by the graph obtained from the query text. In this weighting step, nodes are considered important if they are specifically related to the query text. After determining the important nodes in the graph, the semantic similarity between the query text and the texts in the database is measured. The whole process of this framework uses a natural language processing pipeline in Apache Spark scalable platform. The efficiency of the model was evaluated for both distributed and non-distributed configuration and its scalability on a Spark cluster. Evaluation of the accuracy using the Pearson correlation coefficient shows that the proposed method performs higher performance than its competitors.

关键词： Text mining Databases Scalability Semantics Web mining natural language processing Sparks

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