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SD2SG: A Novel Framework for Detecting Important Subevents from Crisis Events via Dynamic Semantic graphs 7

SD2SG: A Novel Framework for Detecting Important Subevents f...

引用

7th workshop on Noisy User-Generated Text, W-NUT 2021

作者： Spiliopoulou, Evangelia Saha, Tanay K. Tetreault, Joel Jaimes, Alejandro Language Technologies Institute Carnegie Mellon University United States Dataminr Inc. United States

ISBN: (纸本)9781954085909

Social media is an essential tool to share information about crisis events, such as natural disasters. Event Detection aims at extracting information in the form of an event, but considers each event in isolation, without combining information across sentences or events. Many posts in Crisis NLP contain repetitive or complementary information which needs to be aggregated (e.g., the number of trapped people and their location) for disaster response. Although previous approaches in Crisis NLP aggregate information across posts, they only use shallow representations of the content (e.g., keywords), which cannot adequately represent the semantics of a crisis event and its sub-events. In this work, we propose a novel framework to extract critical sub-events from a large-scale crisis event by combining important information across relevant tweets. Our framework first converts all the tweets from a crisis event into a temporally-ordered set of graphs. Then it extracts subgraphs that represent semantic relationships connecting verbs and nouns in 3 to 6 node subgraphs. It does this by learning edge weights via Dynamic graph Convolutional Networks (DGCNs) and extracting smaller, relevant subgraphs. Our experiments show that our extracted structures (1) are semantically meaningful sub-events and (2) contain information important for the large crisis-event. Furthermore, we show that our approach significantly outperforms event detection baselines, highlighting the importance of aggregating information across tweets for our task. © 2021 Association for Computational Linguistics.

关键词： natural language processing systems

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Keep It Surprisingly Simple: A Simple first Order graph based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit

Keep It Surprisingly Simple: A Simple First Order Graph Base...

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Conference on Empirical methods in natural language processing (EMNLP)

作者： Krishna, Amrith Gupta, Ashim Garasangi, Deepak Satuluri, Pavankumar Goyal, Pawan Univ Cambridge Dept Comp Sci & Technol Cambridge England Univ Utah Sch Comp Salt Lake City UT USA Chinmaya Vishwavidyapeeth Sch Linguist & Literary Studies Kochi India IIT Kharagpur Dept Comp Sci & Engn Kharagpur India

ISBN: (纸本)9781952148606

Morphologically rich languages seem to benefit from joint processing of morphology and syntax, as compared to pipeline architectures. We propose a graph-based model for joint morphological parsing and dependency parsing in Sanskrit. Here, we extend the Energy based model framework (Krishna et al., 2020), proposed for several structured prediction tasks in Sanskrit, in 2 simple yet significant ways. first, the framework's default input graph generation method is modified to generate a multigraph, which enables the use of an exact search inference. Second, we prune the input search space using a linguistically motivated approach, rooted in the traditional grammatical analysis of Sanskrit. Our experiments show that the morphological parsing from our joint model outperforms standalone morphological parsers. We report state of the art results in morphological parsing, and in dependency parsing, both in standalone (with gold morphological tags) and joint morphosyntactic parsing setting.

关键词： graphic methods

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KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion

KACC: A Multi-task Benchmark for Knowledge Abstraction, Conc...

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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： Zhou, Jie Hu, Shengding Lv, Xin Yang, Cheng Liu, Zhiyuan Xu, Wei Jiang, Jie Li, Juanzi Sun, Maosong Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Inst Artificial Intelligence Beijing Peoples R China Tsinghua Univ State Key Lab Intelligent Technol & Syst Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Tencent Tencent Mkt Solut Shenzhen Peoples R China

ISBN: (纸本)9781954085541

A comprehensive knowledge graph (KG) contains an instance-level entity graph and an ontology-level concept graph. The two-view KG provides a testbed for models to "simulate" human's abilities on knowledge abstraction, concretization, and completion (KACC), which are crucial for human to recognize the world and manage learned knowledge. Existing studies mainly focus on partial aspects of KACC. In order to promote thorough analyses for KACC abilities of models, we propose a unified KG benchmark by improving existing benchmarks in terms of dataset scale, task coverage, and difficulty. Specifically, we collect new datasets that contain larger concept graphs, abundant cross-view links as well as dense entity graphs. based on the datasets, we propose novel tasks such as multihop knowledge abstraction (MKA), multi-hop knowledge concretization (MKC) and then design a comprehensive benchmark. For MKA and MKC tasks, we further annotate multi-hop hierarchical triples as harder samples. The experimental results of existing methods demonstrate the challenges of our benchmark. The resource is available at https://***/ thunlp/KACC.

关键词： Knowledge graph

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HawkEye: Cross-Platform Malware Detection with Representation Learning on graphs 30th

HawkEye: Cross-Platform Malware Detection with Representatio...

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30th International Conference on Artificial Neural Networks (ICANN)

作者： Xu, Peng Zhang, Youyi Eckert, Claudia Zarras, Apostolis Tech Univ Munich Munich Germany Tongji Univ Shanghai Peoples R China Delft Univ Technol Delft Netherlands

ISBN: (纸本)9783030863654;9783030863647

Malicious software, widely known as malware, is one of the biggest threats to our interconnected society. Cybercriminals can utilize malware to carry out their nefarious tasks. To address this issue, analysts have developed systems that can prevent malware from successfully infecting a machine. Unfortunately, these systems come with two significant limitations. first, they frequently target one specific platform/architecture, and thus, they cannot be ubiquitous. Second, code obfuscation techniques used by malware authors can negatively influence their performance. In this paper, we design and implement HawkEye, a control-flow-graph-based cross-platform malware detection system, to tackle the problems mentioned above. In more detail, HawkEye utilizes a graph neural network to convert the control flow graphs of executable to vectors with the trainable instruction embedding and then uses a machine-learning-based classifier to create a malware detection system. We evaluate HawkEye by testing real samples on different platforms and operating systems, including Linux (x86, x64, and ARM-32), Windows (x86 and x64), and Android. The results outperform most of the existing works with an accuracy of 96.82% on Linux, 93.39% on Windows, and 99.6% on Android. To the best of our knowledge, HawkEye is the first approach to consider graph neural networks in the malware detection field, utilizing natural language processing.

关键词： graphic methods

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Exploiting Structured Knowledge in Text via graph-Guided Representation Learning

Exploiting Structured Knowledge in Text via Graph-Guided Rep...

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Conference on Empirical methods in natural language processing (EMNLP)

作者： Shen, Tao Mao, Yi He, Pengcheng Long, Guodong Trischler, Adam Chen, Weizhu Univ Technol Sydney FEIT Sch Comp Sci Australian AI Inst Sydney NSW Australia Microsoft Dynamics 365 AI Redmond WA USA Microsoft Res Montreal PQ Canada Microsoft Redmond WA USA

ISBN: (纸本)9781952148606

In this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme that exploits relational knowledge underlying the text. This is fulfilled by using a linked knowledge graph to select informative entities and then masking their mentions. In addition, we use knowledge graphs to obtain distractors for the masked entities, and propose a novel distractor-suppressed ranking objective that is optimized jointly with masked language model. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text. It is more efficient than retrieval-based methods that perform entity linking and integration during finetuning and inference, and generalizes more effectively than the methods that directly learn from concatenated graph triples. Experiments show that our proposed model achieves improved performance on five benchmarks, including question answering and knowledge base completion.

关键词： Knowledge graph

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Knowledge graph Alignment with Entity-Pair Embedding

Knowledge Graph Alignment with Entity-Pair Embedding

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Conference on Empirical methods in natural language processing (EMNLP)

作者： Wang, Zhichun Yang, Jinjian Ye, Xiaoju Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China Minist Educ Engn Res Ctr Intelligent Technol & Educ Applicat Beijing Peoples R China

ISBN: (纸本)9781952148606

Knowledge graph (KG) alignment is to match entities in different KGs, which is important to knowledge fusion and integration. Recently, a number of embedding-based approaches for KG alignment have been proposed and achieved promising results. These approaches first embed entities in low-dimensional vector spaces, and then obtain entity alignments by computations on their vector representations. Although continuous improvements have been achieved by recent work, the performances of existing approaches are still not satisfactory. In this work, we present a new approach that directly learns embeddings of entity-pairs for KG alignment. Our approach first generates a pair-wise connectivity graph (PCG) of two KGs, whose nodes are entity-pairs and edges correspond to relation-pairs;it then learns node (entity-pair) embeddings of the PCG, which are used to predict equivalent relations of entities. To get desirable embeddings, a convolutional neural network is used to generate similarity features of entity-pairs from their attributes;and a graph neural network is employed to propagate the similarity features and get the final embeddings of entity-pairs. Experiments on five real-world datasets show that our approach can achieve the state-of-the-art KG alignment results.

关键词： Knowledge graph

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graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms

引用

2021年

作者： Claudio Stamile Aldo Marzullo Enrico Deusebio

Build machine learning algorithms using graph data and efficiently exploit topological information within your modelsKey FeaturesImplement machine learning techniques and algorithms in graph dataIdentify the relations...

ISBN: (数字)9781800206755

Build machine learning algorithms using graph data and efficiently exploit topological information within your models

Key Features

Implement machine learning techniques and algorithms in graph data
Identify the relationship between nodes in order to make better business decisions
Apply graph-based machine learning methods to solve real-life problems

Book Description

graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You’ll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You"ll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you’ll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You’ll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.

What you will learn

Write Python scripts to extract features from graphs
Distinguish between the main graph representation learning techniques
Learn how to extract data from social networks, financi

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Every Document Owns Its Structure: Inductive Text Classification via graph Neural Networks 58

Every Document Owns Its Structure: Inductive Text Classifica...

引用

58th Annual Meeting of the Association-for-Computational-Linguistics (ACL)

作者： Zhang, Yufeng Yu, Xueli Cui, Zeyu Wu, Shu Wen, Zhongzhen Wang, Liang Chinese Acad Sci Inst Automat Beijing Peoples R China Xi An Jiao Tong Univ Xian Peoples R China

ISBN: (纸本)9781952148255

Text classification is fundamental in natural language processing (NLP), and graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words. In this work, to overcome such problems, we propose TextING(1) for inductive text classification via GNN. We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document. Finally, the word nodes are incorporated as the document embedding. Extensive experiments on four benchmark datasets show that our method outperforms state-of-the-art text classification methods.

关键词： graph neural networks

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REPRESENTATION LEARNING OF REMOTE SENSING KNOWLEDGE graph FOR ZERO-SHOT REMOTE SENSING IMAGE SCENE CLASSIFICATION

REPRESENTATION LEARNING OF REMOTE SENSING KNOWLEDGE GRAPH FO...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Yansheng Kong, Deyu Zhang, Yongjun Chen, Ruixian Chen, Jingdong Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Hubei Peoples R China Ant Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665403696

Although deep learning has revolutionized remote sensing image scene classification, current deep learning-based approaches highly depend on the massive supervision of the predetermined scene categories and have disappointingly poor performance on new categories which go beyond the predetermined scene categories. In reality, the classification task often has to be extended along with the emergence of new applications that inevitably involve new categories of remote sensing image scenes, so how to make the deep learning model own the inference ability to recognize the remote sensing image scenes from unseen categories becomes incredibly important. By fully exploiting the remote sensing domain characteristic, this paper proposes a novel remote sensing knowledge graph-guided deep alignment network to address zero-shot remote sensing image scene classification. To improve the semantic representation ability of remote sensing-oriented scene categories, this paper, for the first time, tries to generate the semantic representations of remote sensing scene categories by representation learning of remote sensing knowledge graph (SR-RSKG). In addition, this paper proposes a novel deep alignment network with a series of constraints (DAN) to conduct robust cross-modal alignment between visual features and semantic representations. Extensive experiments on one merged remote sensing image scene dataset, which is the integration of multiple publicly open remote sensing image scene datasets, show that the presented SR-RSKG obviously outperforms the existing semantic representation methods (e.g., the natural language processing models and manually annotated attribute vectors), and our proposed DAN shows better performance compared with the state- of-the-art methods under different kinds of semantic representations.

关键词： Deep alignment network (DAN) remote sensing knowledge graph (RSKG) remote sensing image scene classification zero-shot learning (ZSL)

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Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling

Better Combine Them Together! Integrating Syntactic Constitu...

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作者： Fei, Hao Wu, Shengqiong Ren, Yafeng Li, Fei Ji, Donghong Wuhan Univ Sch Cyber Sci & Engn Dept Key Lab Aerosp Informat Secur & Trusted Comp Minist Educ Wuhan Peoples R China Guangdong Univ Foreign Studies Lab Language & Artificial Intelligence Guangzhou Peoples R China

ISBN: (纸本)9781954085541

Structural syntax knowledge has been proven effective for semantic role labeling (SRL), while existing works mostly use only one singleton syntax, such as either syntactic dependency or constituency tree. In this paper, we explore the integration of heterogeneous syntactic representations for SRL. We first consider a TreeLSTM-based integration, collaboratively learning the phrasal boundaries from the constituency and the semantic relations from dependency. We further introduce a label-aware GCN solution for simultaneously modeling the syntactic edges and labels. Experimental results demonstrate that by effectively combining the heterogeneous syntactic representations, our methods yield task improvements on both span-based and dependency-based SRL. Also our system achieves new state-of-the-art SRL performances, meanwhile bringing explainable task improvements.

关键词： Syntactics

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