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14th workshop on graph-based methods for natural language processing, Textgraphs 2020, in conjunction with the 28th International Conference on Computational Linguistics, COLING 2020

作者： Li, Weibin Lu, Yuxiang Huang, Zhengjie Liu, Jiaxiang Su, Weiyue Feng, Shikun Sun, Yu Baidu Inc. China

ISBN: (纸本)9781952148422

This paper describes the system designed by the Baidu PGL Team which achieved the first place in the Textgraphs 2020 Shared Task. The task focuses on generating explanations for elementary science questions. Given a question and its corresponding correct answer, we are asked to select the facts that can explain why the answer is correct for that question and answering (QA) from a large knowledge base. To address this problem, we use a pre-trained language model to recall the top-K relevant explanations for each question. Then, we adopt a re-ranking approach based on a pre-trained language model to rank the candidate explanations. To further improve the rankings, we also develop an architecture consisting both powerful pre-trained transformers and GNNs to tackle the multi-hop inference problem. The official evaluation shows that, our system can outperform the second best system by 1.91 points. © COLING *** rights reserved.

关键词： Knowledge based systems

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Beyond natural language processing: Building Knowledge graphs to Assist Scientists Understand COVID-19 Concepts 22

Beyond Natural Language Processing: Building Knowledge Graph...

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proceedings of the 2022 5th International Conference on Machine Learning and natural language processing

作者： Yishu Yu Dana Hall School USA

ISBN: (纸本)9781450399067

To combat COVID-19, scientists must digest the vast amount of relevant biomedical knowledge in the literature to understand disease mechanisms and related biological functions. Nearly 3,000 scientific papers are published on PubMed every day. This knowledge bottleneck has resulted in severe delays in developing COVID-19 vaccines and drugs. Our research produces a hierarchy of knowledge concepts related to COVID-19, designed to assist scientists in answering questions and generating summaries. It aims to discover scientific and comprehensive knowledge to extract fine-grained multimedia elements (i.e., physical and visual structures, relational events and events, and chemical knowledge). Our project is toward one step in natural language understanding: detailed contextual sentences, subgraphs, and knowledge subgraphs are the first time to be automatically generated, and relations and coreferences of COVID-19 mentions will be sketched. Extensive results show that our method outperforms other state-of-the-art methods. In addition, we have published the generated knowledge graph on Google Drive1 and released the source in the Github2.

关键词： COVID-19

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DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

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International Journal of Intelligence Science 2023年第4期13卷 81-130页

作者： Peter J. Worth Domagoj Doresic Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton USA Interdisciplinary Research Unit Mathematics and Life Sciences University of Bonn Bonn Germany

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.

关键词： Ontology Learning Ontology Engineering Concept Hierarchies Concept Mapping Concept Maps Artificial Intelligence Philosophy natural language processing Knowledge Representation Knowledge Representation and Reasoning Machine Learning natural language processing NLP Computer Science Theoretical Computer Science Epistemology Metaphysics Philosophy Logic Computing Ontology first Order Logic Predicate Calculus

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Towards Propagation Uncertainty: Edge-enhanced Bayesian graph Convolutional Networks for Rumor Detection 59

Towards Propagation Uncertainty: Edge-enhanced Bayesian Grap...

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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： Wei, Lingwei Hu, Dou Zhou, Wei Yue, Zhaojuan Hu, Songlin Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Natl Comp Syst Engn Res Inst China Beijing Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781954085527

Detecting rumors on social media is a very critical task with significant implications to the economy, public health, etc. Previous works generally capture effective features from texts and the propagation structure. However, the uncertainty caused by unreliable relations in the propagation structure is common and inevitable due to wily rumor producers and the limited collection of spread data. Most approaches neglect it and may seriously limit the learning of features. Towards this issue, this paper makes the first attempt to explore propagation uncertainty for rumor detection. Specifically, we propose a novel Edge-enhanced Bayesian graph Convolutional Network (EBGCN) to capture robust structural features. The model adaptively rethinks the reliability of latent relations by adopting a Bayesian approach. Besides, we design a new edge-wise consistency training framework to optimize the model by enforcing consistency on relations. Experiments on three public benchmark datasets demonstrate that the proposed model achieves better performance than baseline methods on both rumor detection and early rumor detection tasks.

关键词： Bayesian networks

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A graphical framework for contextual search and name disambiguation in email 1

A graphical framework for contextual search and name disambi...

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1st workshop on graph-based Algorithms for natural language processing, Textgraphs 2006 at Human language Technologies

作者： Minkov, Einat Cohen, William W. Ng, Andrew Y. Language Technologies Inst Carnegie Mellon University PittsburghPA15213 United States Machine Learning Dept Carnegie Mellon University PittsburghPA15213 United States Computer Science Dept Stanford University StanfordCA94305 United States

Similarity measures for text have historically been an important tool for solving information retrieval problems. In this paper we consider extended similarity metrics for documents and other objects embedded in graphs, facilitated via a lazy graph walk. We provide a detailed instantiation of this framework for email data, where content, social networks and a timeline are integrated in a structural graph. The suggested framework is evaluated for the task of disambiguating names in email documents. We show that reranking schemes based on the graph-walk similarity measures often outperform baseline methods, and that further improvements can be obtained by use of appropriate learning methods. © 2006 Association for Computational Linguistics

关键词： Electronic mail

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A Framework for Scalable Similarity Evaluation in Text graphs 7

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

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7th International Conference on Web Research, ICWR 2021

作者： Samani, Mahdi Ghadiri, Nasser Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9781665404266

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. © 2021 IEEE.

关键词： graphic methods

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Beta Distribution Guided Aspect-aware graph for Aspect Category Sentiment Analysis with Affective Knowledge

Beta Distribution Guided Aspect-aware Graph for Aspect Categ...

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Conference on Empirical methods in natural language processing (EMNLP)

作者： Liang, Bin Su, Hang Yin, Rongdi Gui, Lin Yang, Min Zhao, Qin Yu, Xiaoqi Xu, Ruifeng Harbin Inst Technol Sch Comp Sci & Technol Shenzhen Peoples R China Joint Lab HITSZ & China Merchants Secur Shenzhen Peoples R China Univ Warwick Dept Comp Sci Coventry W Midlands England Chinese Acad Sci SIAT Shenzhen Peoples R China China Merchants Secur Co Ltd Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781955917094

In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge. That is, we are no longer entangled about how to laboriously search the sentiment clues for coarse-grained aspects from the context, but how to preferably find the words highly sentimen-trelated to the aspects in the context and determine their importance based on the public knowledge base, so as to naturally learn the aspect-related contextual sentiment dependencies with these words in ACSA. To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge. Then, we employ Beta Distribution to educe the aspect-aware weight, which reflects the importance to the aspect, for each aspect-aware word. Afterward, the aspect-aware words are served as the substitutes of the coarse-grained aspect to construct graphs for leveraging the aspect-related contextual sentiment dependencies in ACSA. Experiments on 6 benchmark datasets show that our approach significantly outperforms the state-of-the-art baseline methods.

关键词： Sentiment analysis

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Parameter free hierarchical graph-based clustering for analyzing continuous word embeddings 11

Parameter free hierarchical graph-based clustering for analy...

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11th workshop on graph-based methods for natural language processing, Textgraphs 2017, in conjunction with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

作者： Trost, Thomas A. Klakow, Dietrich Saarland University Saarbrücken Germany

ISBN: (纸本)9781945626609

Word embeddings are high-dimensional vector representations of words and are thus difficult to interpret. In order to deal with this, we introduce an unsupervised parameter free method for creating a hierarchical graphical clustering of the full ensemble of word vectors and show that this structure is a geometrically meaningful representation of the original relations between the words. This newly obtained representation can be used for better understanding and thus improving the embedding algorithm and exhibits semantic meaning, so it can also be utilized in a variety of language processing tasks like categorization or measuring similarity. © 2017 Association for Computational Linguistics

关键词： Embeddings

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Combining Adversarial Training and Relational graph Attention Network for Aspect-based Sentiment Analysis with BERT 14

Combining Adversarial Training and Relational Graph Attentio...

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14th International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Chen, Mingfei Wu, Wencong Zhang, Yungang Zhou, Ziyun Yunnan Normal Univ Sch Informat Sci & Technol Kunming 650500 Yunnan Peoples R China Informat Ctr Yunnan Adm Market Regulat Kunming 650228 Yunnan Peoples R China

ISBN: (纸本)9781665400046

Aspect-based Sentiment Analysis (ABSA), also called Aspect Level Sentiment Classification (ALSC), is a common task in natural language processing (NLP). Aspect-based Sentiment Analysis mainly aims to extract and classify the sentiments objects in texts. In this paper, we propose a novel BERT-based ABSA model, which combines an adversarial training procedure with relational graph attention neural network (R-GAT). To our best knowledge, it is the first model that simultaneously using adversarial training, relational graph attention neural network and BERT for aspect-based sentiment analysis. In our proposed model, the BERT Encoder is used to extract the context feature vector, R-GAT is applied to integrate the typed syntactic dependency information. The proposed model also includes an adversarial training method to ensure the robustness of neural network, which is realized by artificially increasing data samples similar to the real-world examples using adversarial processes in the embedding space. Our experimental results on three benchmark datasets demonstrate that the proposed model is competitive compared to the other state-of-the-art methods.

关键词： Aspect-based Sentiment Analysis Adversarial Training Relational graph Attention Network BERT

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Position Bias Mitigation: A Knowledge-Aware graph Model for Emotion Cause Extraction 59

Position Bias Mitigation: A Knowledge-Aware Graph Model for ...

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作者： Yan, Hanqi Gui, Lin Pergola, Gabriele He, Yulan Univ Warwick Dept Comp Sci Coventry W Midlands England

ISBN: (纸本)9781954085527

The Emotion Cause Extraction (ECE) task aims to identify clauses which contain emotion-evoking information for a particular emotion expressed in text. We observe that a widely-used ECE dataset exhibits a bias that the majority of annotated cause clauses are either directly before their associated emotion clauses or are the emotion clauses themselves. Existing models for ECE tend to explore such relative position information and suffer from the dataset bias. To investigate the degree of reliance of existing ECE models on clause relative positions, we propose a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses. We test the performance of existing models on such adversarial examples and observe a significant performance drop. To address the dataset bias, we propose a novel graph-based method to explicitly model the emotion triggering paths by leveraging the commonsense knowledge to enhance the semantic dependencies between a candidate clause and an emotion clause. Experimental results show that our proposed approach performs on par with the existing state-of-the-art methods on the original ECE dataset, and is more robust against adversarial attacks compared to existing models.

关键词： Extraction

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