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2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Zhang, Xinliang Frederick Beauchamp, Nick Wang, Lu Computer Science and Engineering University of Michigan Ann ArborMI United States Department of Political Science Northeastern University BostonMA United States

Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly. To support this study, we curate a new dataset with 10, 619 annotations labeled at the sentence-level from news articles of different ideological leanings. We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them. We further enhance the model with a graph encoder to summarize entity activities and external knowledge surrounding the entities. Experiments show that our model outperforms strong comparisons by large margins. Further analyses demonstrate the usefulness of E2E stance detection for understanding media quotation and stance landscape, as well as inferring entity ideology. © 2022 Association for Computational Linguistics.

关键词： Knowledge graph

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Refined Global Word Embeddings based on Sentiment Concept for Sentiment Analysis

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IEEE ACCESS 2021年 9卷 37075-37085页

作者： Wang, Yabing Huang, Guimin Li, Jun Li, Hui Zhou, Ya Jiang, Hua Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China

Sentiment Analysis is an important research direction of natural language processing, and it is widely used in politics, news and other fields. Word embeddings play a significant role in sentiment analysis. The existing sentiment embeddings methods directly embed the sentiment lexicons into traditional word representation. This sentiment representation methods can only differentiate the sentiment information of different words, not the same word in different contexts, so it cannot provide accurate sentiment information for word in different contexts. This paper proposes sentiment concept to solve the problem. first, we found the optimal sentiment concept of words in Microsoft Concept graph according to the context of words. Then we obtained the sentiment information of words under optimal sentiment concept from the multi-semantics sentiment intensity lexicon which we constructed in this paper to achieve accurate embedding of sentiment information and provide more accurate semantics and sentiment representation for words. Finally, we combined two refined word embeddings methods to achieve a more comprehensive word representation. Compared with traditional and sentiment embeddings methods on six representative datasets, the validity of the word embeddings method based on sentiment concept proposed in this paper is verified.

关键词： Semantics Sentiment analysis Task analysis Analytical models Deep learning Predictive models Licenses Deep learning sentiment analysis sentiment concept word embeddings

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N-ary Constituent Tree Parsing with Recursive Semi-Markov Model 59

<i>N</i>-ary Constituent Tree Parsing with Recursive Semi-Ma...

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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： Xin, Xin Li, Jinlong Tan, Zeqi Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China BIT BJ ER Ctr HVLIPCCA Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781954085527

In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children. Previous graph-based methods on this setting typically generate hidden nodes with the dummy label inside the n-ary nodes, in order to transform the tree into a binary tree for prediction. The limitation is that the hidden nodes break the sibling relations of the n-ary node's children. Consequently, the dependencies of such sibling constituents might not be accurately modeled and is being ignored. To solve this limitation, we propose a novel graph-based framework, which is called "recursive semi-Markov model". The main idea is to utilize 1-order semi-Markov model to predict the immediate children sequence of a constituent candidate, which then recursively serves as a child candidate of its parent. In this manner, the dependencies of sibling constituents can be described by 1-order transition features, which solves the above limitation. Through experiments, the proposed framework obtains the F1 of 95:92% and 92:50% on the datasets of PTB and CTB 5.1 respectively. Specially, the recursive semi-Markov model shows advantages in modeling nodes with more than two children, whose average F1 can be improved by 0:3-1:1 points in PTB and 2:3-6:8 points in CTB 5.1.

关键词： graphic methods

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ABCD: A graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences 59

ABCD: A Graph Framework to Convert Complex Sentences to a Co...

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作者： Gao, Yanjun Huang, Ting-Hao (kenneth) Passonneau, Rebecca J. Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9781954085527

Atomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering. Previous work mainly relies on rule-based methods dependent on parsing. We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task. Our neural model learns to Accept, Break, Copy or Drop elements of a graph that combines word adjacency and grammatical dependencies. The full processing pipeline includes modules for graph construction, graph editing, and sentence generation from the output graph. We introduce DeSSE, a new dataset designed to train and evaluate complex sentence decomposition, and MinWiki, a subset of MinWikiSplit. ABCD achieves comparable performance as two parsing baselines on MinWiki. On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline. Results include a detailed error analysis.

关键词： Syntactics

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Multimodal Sentiment Detection based on Multi-channel graph Neural Networks 59

Multimodal Sentiment Detection Based on Multi-channel Graph ...

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作者： Yang, Xiaocui Feng, Shi Zhang, Yifei Wang, Daling Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781954085527

With the popularity of smartphones, we have witnessed the rapid proliferation of multimodal posts on various social media platforms. We observe that the multimodal sentiment expression has specific global characteristics, such as the interdependencies of objects or scenes within the image. However, most previous studies only considered the representation of a single image-text post and failed to capture the global co-occurrence characteristics of the dataset. In this paper, we propose Multi-channel graph Neural Networks with Sentiment-awareness (MGNNS) for imagetext sentiment detection. Specifically, we first encode different modalities to capture hidden representations. Then, we introduce multichannel graph neural networks to learn multimodal representations based on the global characteristics of the dataset. Finally, we implement multimodal in-depth fusion with the multi-head attention mechanism to predict the sentiment of image-text pairs. Extensive experiments conducted on three publicly available datasets demonstrate the effectiveness of our approach for multimodal sentiment detection.

关键词： graph neural networks

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The unknown knowns: a graph-based approach for temporal COVID-19 literature mining

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ONLINE INFORMATION REVIEW 2021年第4期45卷 687-708页

作者： Bayram, Ulya Roy, Runia Assalil, Aqil BenHiba, Lamia Canakkale Onsekiz Mart Univ Dept Elect Engn Canakkale Turkey SUNY Binghamton Dept Biomed Engn Binghamton NY USA Florida Atlant Univ Comp Sci Boca Raton FL USA Bethune Cookman Univ Daytona Beach FL USA Univ Mohammed V Rabat Dept Informat & Aide Decis Ecole Natl Superieure Informat & Analyse Syst Rabat Morocco

Purpose - The COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weighted semantic graph model to compress the research studies efficiently. Also, the authors present two analyses on this graph to propose alternative ways to uncover additional aspects of COVID-19 research. Design/methodology/approach - The authors construct the semantic graph using state-of-the-art natural language processing (NLP) techniques on COVID-19 publication texts (>100,000 texts). Next, the authors conduct an evolutionary analysis to capture the changes in COVID-19 research across time. Finally, the authors apply a link prediction study to detect novel COVID-19 research directions that are so far undiscovered. Findings - Findings reveal the success of the semantic graph in capturing scientific knowledge and its evolution. Meanwhile, the prediction experiments provide 79% accuracy on returning intelligible links, showing the reliability of the methods for predicting novel connections that could help scientists discover potential new directions. Originality/value - To the authors' knowledge, this is the first study to propose a holistic framework that includes encoding the scientific knowledge in a semantic graph, demonstrates an evolutionary examination of past and ongoing research and offers scientists with tools to generate new hypotheses and research directions through predictive modeling and deep machine learning techniques.

关键词： COVID-19 Semantic graphs natural language processing Link prediction Machine learning

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Deeper Clinical Document Understanding Using Relation Extraction

Deeper Clinical Document Understanding Using Relation Extrac...

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2022 workshop on Scientific Document Understanding, SDU 2022

作者： Haq, Hasham Ul Kocaman, Veysel Talby, David John Snow Labs inc. 16192 Coastal Highway LewesDE19958 United States

The surging amount of biomedical literature & digital clinical records presents a growing need for text mining techniques that can not only identify but also semantically relate entities in unstructured data. In this paper we propose a text mining framework comprising of Named Entity Recognition (NER) and Relation Extraction (RE) models, which expands on previous work in three main ways. first, we introduce two new RE model architectures - an accuracy-optimized one based on BioBERT and a speed-optimized one utilizing crafted features over a Fully Connected Neural Network (FCNN). Second, we evaluate both models on public benchmark datasets and obtain new state-of-the-art F1 scores on the 2012 i2b2 Clinical Temporal Relations challenge (F1 of 73.6, +1.2% over the previous SOTA), the 2010 i2b2 Clinical Relations challenge (F1 of 69.1, +1.2%), the 2019 Phenotype-Gene Relations dataset (F1 of 87.9, +8.5%), the 2012 Adverse Drug Events Drug-Reaction dataset (F1 of 90.0, +6.3%), and the 2018 n2c2 Posology Relations dataset (F1 of 96.7, +0.6%). Third, we show two practical applications of this framework - for building a biomedical knowledge graph and for improving the accuracy of mapping entities to clinical codes. The system is built using the Spark NLP library which provides a production-grade, natively scalable, hardware-optimized, trainable & tunable NLP framework. © 2021 Copyright for this paper by its authors.

关键词： natural language processing systems

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Exploiting Visual Relation and Multi-Grained Knowledge for Multimodal Relation Extraction

Exploiting Visual Relation and Multi-Grained Knowledge for M...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shen, Qianru Lin, Hailun Liu, Huan Lin, Zheng Wang, Weiping Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Key Lab Cyberspace Secur Def Beijing Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Given a text and its related image, the multimodal relation extraction (MRE) task aims at predicting the correct semantic relation between two entities in the input text. Though certain advances have been made by recent MRE approaches, they still suffer from two drawbacks. first, they ignore finegrained visual relations between image objects which can serve as important clues for inferring the correct relation. Second, they are unable to utilize useful external knowledge about entities and the input sentence, leading to a sub-optimal performance when processing examples that demand related background or commonsense knowledge. To alleviate above limitations, we propose a novel method, named VRMK, which exploits both Visual Relations and Multi-grained Knowledge for the MRE task. Specifically, the input image-text pair is converted into a unified multimodal graph. Then, the relation-aware transformer is adopted to update node representations while explicitly encoding diverse relations among visual and textual nodes. based on the input text, the powerful large language model (LLM) is used to generate entity-level and sentence-level knowledge with the in-context learning. The most relevant knowledge information is captured by a cross-attention mechanism and is further combined with the representations of entity nodes and original text to predict the final relation label. On the MNRE dataset, VRMK outperforms recent state-of-the-art baselines including LLMbased methods by 2.71% (82.55%.85.26% in F1 score). We also conduct extensive ablation experiments to reveal contributions of different modules and provide useful insights for future research.

关键词： multimodal relation extraction scene graph generation relation-aware Transformer multi-grained knowledge

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The Sixth International workshop on Deep Learning on graphs - methods and Applications (DLG-KDD'21) 21

The Sixth International Workshop on Deep Learning on Graphs ...

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27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Wu, Lingfei Tang, Jiliang Xia, Yinglong Pei, Jian Guo, Xiaojie Michigan State Univ E Lansing MI 48824 USA Facebook Menlo Pk CA USA Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781450383325

Deep Learning models are at the core of research in Artificial Intelligence research today. A tide in research for deep learning on graphs or graph neural networks. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, program synthesis and analysis, financial security, Drug Discovery and so on. However, there are still many challenges regarding a broad range of the topics in deep learning on graphs, from methodologies to applications, and from foundations to the new frontiers of GNNs. This international workshop on "Deep Learning on graphs: Method and Applications (DLG-KDD'21)" aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to above challenges.

关键词： graph neural network deep learning graph representation learning graph mining

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Between History and natural language processing: Study, Enrichment and Online Publication of French Parliamentary Debates of the Early Third Republic (1881–1899)

Between History and Natural Language Processing: Study, Enri...

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2022 workshop on Creating, Enriching and Using Parliamentary Corpora, ParlaCLARIN III 2022

作者： Puren, Marie Pellet, Aurélien Bourgeois, Nicolas Vernus, Pierre Lebreton, Fanny MNSHS-Epitech Le Kremlin-Bicêtre France Centre Jean Mabillon Paris France LARHRA Lyon France Université Lumière Lyon 2 Lyon France École Nationale des Chartes Paris France

ISBN: (纸本)9791095546856

We present the AGODA (Analyse sémantique et graphes relationnels pour l’Ouverture des Débats à l’Assemblée nationale) project, which aims to create a platform for consulting and exploring digitised French parliamentary debates (1881-1940) available in the digital library of the National Library of France. This project brings together historians and NLP specialists: parliamentary debates are indeed an essential source for French history of the contemporary period, but also for linguistics. This project therefore aims to produce a corpus of texts that can be easily exploited with computational methods, and that respect the TEI standard. Ancient parliamentary debates are also an excellent case study for the development and application of tools for publishing and exploring large historical corpora. In this paper, we present the steps necessary to produce such a corpus. We detail the processing and publication chain of these documents, in particular by mentioning the problems linked to the extraction of texts from digitised images. We also introduce the first analyses that we have carried out on this corpus with "bag-of-words" techniques not too sensitive to OCR quality (namely topic modelling and word embedding). © European language Resources Association (ELRA).

关键词： Embeddings

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