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Academic literature on the topic 'Graph Approaches in NLP'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Graph Approaches in NLP"

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Tsang, Vivian, and Suzanne Stevenson. "A Graph-Theoretic Framework for Semantic Distance." Computational Linguistics 36, no. 1 (2010): 31–69. http://dx.doi.org/10.1162/coli.2010.36.1.36101.

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Many NLP applications entail that texts are classified based on their semantic distance (how similar or different the texts are). For example, comparing the text of a new document to that of documents of known topics can help identify the topic of the new text. Typically, a distributional distance is used to capture the implicit semantic distance between two pieces of text. However, such approaches do not take into account the semantic relations between words. In this article, we introduce an alternative method of measuring the semantic distance between texts that integrates distributional inf

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Santos, Vinicius Dos, Érica Ferreira De Souza, Kátia Romero Felizardo, et al. "Conceptual Map Creation from Natural Language Processing: a Systematic Mapping Study." Revista Brasileira de Informática na Educação 27, no. 03 (2019): 150–76. http://dx.doi.org/10.5753/rbie.2019.27.03.150.

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Context: Conceptual Maps (CMs) have been used to organize knowledge and facilitate learning and teaching in multiple domains. CMs also are used in multiple settings in education, since they are able to clarify the relationships between the subcomponents of a particular topic. However, the construction of a CM requires time and effort in identifying and structuring knowledge. In order to mitigate this problem, Natural Language Processing (NLP) techniques have been employed and have contributed to automate the extraction of concepts and relationships from texts. Objective: This article summarize

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Peng, Nanyun, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. "Cross-Sentence N-ary Relation Extraction with Graph LSTMs." Transactions of the Association for Computational Linguistics 5 (December 2017): 101–15. http://dx.doi.org/10.1162/tacl_a_00049.

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Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction. The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and inter-sentential dependencies, s

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Zhao, Yu, Huali Feng, and Patrick Gallinari. "Embedding Learning with Triple Trustiness on Noisy Knowledge Graph." Entropy 21, no. 11 (2019): 1083. http://dx.doi.org/10.3390/e21111083.

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Embedding learning on knowledge graphs (KGs) aims to encode all entities and relationships into a continuous vector space, which provides an effective and flexible method to implement downstream knowledge-driven artificial intelligence (AI) and natural language processing (NLP) tasks. Since KG construction usually involves automatic mechanisms with less human supervision, it inevitably brings in plenty of noises to KGs. However, most conventional KG embedding approaches inappropriately assume that all facts in existing KGs are completely correct and ignore noise issues, which brings about pote

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Wang, Xiaoyan, Pavan Kapanipathi, Ryan Musa, et al. "Improving Natural Language Inference Using External Knowledge in the Science Questions Domain." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7208–15. http://dx.doi.org/10.1609/aaai.v33i01.33017208.

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Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained significant attention due to the release of large scale, challenging datasets. Present approaches to the problem largely focus on learning-based methods that use only textual information in order to classify whether a given premise entails, contradicts, or is neutral with respect to a given hypothesis. Surprisingly, the use of methods based on structured knowledge – a central topic in artificial intelligence – has no

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Ali, Muhammad Asif, Yifang Sun, Bing Li, and Wei Wang. "Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7391–98. http://dx.doi.org/10.1609/aaai.v34i05.6234.

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Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FG-NET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention's sentence-specific context. This is inadequate for high

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Murray, Gabriel. "Information Processing and Overload in Group Conversation: A Graph-Based Prediction Model." Multimodal Technologies and Interaction 3, no. 3 (2019): 46. http://dx.doi.org/10.3390/mti3030046.

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Based on analyzing verbal and nonverbal features of small group conversations in a task-based scenario, this work focuses on automatic detection of group member perceptions about how well they are making use of available information, and whether they are experiencing information overload. Both the verbal and nonverbal features are derived from graph-based social network representations of the group interaction. For the task of predicting the information use ratings, a predictive model using random forests with verbal and nonverbal features significantly outperforms baselines in which the mean

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Al-Sabahi, Kamal, and Zhang Zuping. "Document Summarization Using Sentence-Level Semantic Based on Word Embeddings." International Journal of Software Engineering and Knowledge Engineering 29, no. 02 (2019): 177–96. http://dx.doi.org/10.1142/s0218194019500086.

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In the era of information overload, text summarization has become a focus of attention in a number of diverse fields such as, question answering systems, intelligence analysis, news recommendation systems, search results in web search engines, and so on. A good document representation is the key point in any successful summarizer. Learning this representation becomes a very active research in natural language processing field (NLP). Traditional approaches mostly fail to deliver a good representation. Word embedding has proved an excellent performance in learning the representation. In this pap

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Mousavi, Hamid, Shi Gao, Deirdre Kerr, Markus Iseli, and Carlo Zaniolo. "Mining Semantics Structures from Syntactic Structures in Web Document Corpora." International Journal of Semantic Computing 08, no. 04 (2014): 461–89. http://dx.doi.org/10.1142/s1793351x14400157.

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The Web is making possible many advanced text-mining applications, such as news summarization, essay grading, question answering, semantic search and structured queries on corpora of Web documents. For many of such applications, statistical text-mining techniques are of limited effectiveness since they do not utilize the morphological structure of the text. On the other hand, many approaches use NLP-based techniques that parse the text into parse trees, and then use patterns to mine and analyze parse trees which are often unnecessarily complex. To reduce this complexity and ease the entire pro

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Ma, Shutian, Yingyi Zhang, and Chengzhi Zhang. "Using multiple Web resources and inference rules to classify Chinese word semantic relation." Information Discovery and Delivery 46, no. 2 (2018): 120–26. http://dx.doi.org/10.1108/idd-03-2018-0010.

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Purpose The purpose of this paper is to classify Chinese word semantic relations, which are synonyms, antonyms, hyponyms and meronymys. Design/methodology/approach Basically, four simple methods are applied, ontology-based, dictionary-based, pattern-based and morpho-syntactic method. The authors make good use of search engine to build lexical and semantic resources for dictionary-based and pattern-based methods. To improve classification performance with more external resources, they also classify the given word pairs in Chinese and in English at the same time by using machine translation. Fin

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Dissertations / Theses on the topic "Graph Approaches in NLP"

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Lukose, Susan. "Ontology learning for online course creation using nlp techniques and graph theory /." Full text available from ProQuest UM Digital Dissertations, 2008. http://0-proquest.umi.com.umiss.lib.olemiss.edu/pqdweb?index=0&did=1798967591&SrchMode=1&sid=2&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1258130866&clientId=22256.

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Thesis (Ph.D.)--University of Mississippi, 2008.<br>Typescript. Vita. "December 2008." Major professor: Pamela Lawhead. Includes bibliographical references (leaves 86-90). Also available online via ProQuest to authorized users.

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Hope, David Richard. "Graph-based approaches to word sense induction." Thesis, University of Sussex, 2015. http://sro.sussex.ac.uk/id/eprint/53254/.

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This thesis is a study of Word Sense Induction (WSI), the Natural Language Processing (NLP) task of automatically discovering word meanings from text. WSI is an open problem in NLP whose solution would be of considerable benefit to many other NLP tasks. It has, however, has been studied by relatively few NLP researchers and often in set ways. Scope therefore exists to apply novel methods to the problem, methods that may improve upon those previously applied. This thesis applies a graph-theoretic approach to WSI. In this approach, word senses are identifed by finding particular types of subgrap

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Pershina, Maria. "Graph-based approaches to resolve entity ambiguity." Thesis, New York University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10139524.

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<p> Information extraction is the task of automatically extracting structured information from unstructured or semi-structured machine-readable documents. One of the challenges of Information Extraction is to resolve ambiguity between entities either in a knowledge base or in text documents. There are many variations of this problem and it is known under different names, such as coreference resolution, entity disambiguation, entity linking, entity matching, etc. For example, the task of coreference resolution decides whether two expressions refer to the same entity; entity disambiguation dete

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Lu, Zhi. "Optimization approaches for minimum conductance graph partitioning." Thesis, Angers, 2020. http://www.theses.fr/2020ANGE0013.

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Le problème de partitionnement de graphe de conductance minimale (MCGPP) est un problème d’optimisation combinatoire NP-difficile avec de nombreuses applications pratiques dans divers domaines tels que la détection communautaire, la bioinformatique et la vision par ordinateur. Etant donnée sa complexité intrinsèque, des approches heuristiques et métaheuristiques constituent un moyen convenable pour résoudre des instances de grande taille. Cette thèse est consacrée au développement d’algorithmes métaheuristiques performants pour le MC-GPP. Plus précisément, nous proposons un algorithme «Stagnat

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Asan, Emrah. "Video Shot Boundary Detection By Graph Theoretic Approaches." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609819/index.pdf.

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This thesis aims comparative analysis of the state of the art shot boundary detection algorithms. The major methods that have been used for shot boundary detection such as pixel intensity based, histogram-based, edge-based, and motion vectors based, are implemented and analyzed. A recent method which utilizes &ldquo<br>graph partition model&rdquo<br>together with the support vector machine classifier as a shot boundary detection algorithm is also implemented and analyzed. Moreover, a novel graph theoretic concept, &ldquo<br>dominant sets&rdquo<br>, is also successfully applied to the shot boun

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Tamura, Takeyuki. "Graph Algorithmic Approaches for Structure Inferences in Bioinformatics." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/68893.

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Karlin, Ievgen. "An Evaluation of NLP Toolkits for Information Quality Assessment." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-22606.

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Documentation is often the first source, which can help user to solve problems or provide conditions of use of some product. That is why it should be clear and understandable. But what does “understandable” mean? And how to detect whether some text is unclear? And this thesis can answer on those questions.The main idea of current work is to measure clarity of the text information using natural language processing capabilities. There are three global steps to achieve this goal: to define criteria of bad clarity of text information, to evaluate different natural language toolkits and find suitab

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Wilks, C. F. "Untangling word webs : graph theory approaches to L2 lexicons." Thesis, Swansea University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636603.

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This research sets out to examine some of the implications of metaphor of the "vocabulary network" that is widely used in lexical research. It takes a formal approach to the exploration of this metaphor by applying the principles of Graph Theory to the notion of the lexical network. It looks, in particular, at how graph theoretical principles may be applied to word association data in order to compare the relative densities of L1 and L2 lexical networks. Nine experiments were carried out. An initial series of studies followed up in detail a simple graph theoretical model, proposed by Welsh (19

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Ullah, Ehsan. "Pathway Analysis of Metabolic Networks using Graph Theoretical Approaches." Thesis, Tufts University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3640954.

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<p> Cellular pathways defining biochemical transformational routes are often utilized as engineering targets to achieve industrial-scale production of commercially useful biomolecules including polyesters, building blocks for polymers, biofuels, and therapeutics derived from isoprenoids, polyketides, and non-ribosomal peptides. Identifying target pathways can be expedited using computational tools, leading to reduced development cost, time, and effort, and enabling new discoveries with potential positive impact on human health and the environment. </p><p> This thesis addresses three cellula

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Birchall, Kristian. "Reduced graph approaches to analysing high-throughput screening data." Thesis, University of Sheffield, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443869.

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Books on the topic "Graph Approaches in NLP"

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Bonchev, Danail, and Ovanes Mekenyan, eds. Graph Theoretical Approaches to Chemical Reactivity. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1202-4.

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Bonchev, Danail. Graph Theoretical Approaches to Chemical Reactivity. Springer Netherlands, 1994.

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Amaral, Barbara, and Marcelo Terra Cunha. On Graph Approaches to Contextuality and their Role in Quantum Theory. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93827-1.

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Pardalos, P. M. (Panos M.), 1954- and SpringerLink (Online service), eds. Data Correcting Approaches in Combinatorial Optimization. Springer New York, 2012.

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Kayser, Klaus. Graph theory and the entropy concept in histochemistry: Theoretical considerations, application in histopathology and the combination with receptor-specific approaches. G. Fischer, 1997.

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Litvinov, G. L. (Grigoriĭ Lazarevich), 1944- editor of compilation and Sergeev, S. N., 1981- editor of compilation, eds. Tropical and idempotent mathematics and applications: International Workshop on Tropical and Idempotent Mathematics, August 26-31, 2012, Independent University, Moscow, Russia. American Mathematical Society, 2014.

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Danail, Bonchev, and Mekenyan Ovanes, eds. Graph theoretical approaches to chemical reactivity. Kluwer Academic, 1994.

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Amaral, Barbara, and Marcelo Terra Cunha. On Graph Approaches to Contextuality and their Role in Quantum Theory. Springer, 2018.

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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Random graph ensembles. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0003.

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This chapter presents some theoretical tools for defining random graph ensembles systematically via soft or hard topological constraints including working through some properties of the Erdös-Rényi random graph ensemble, which is the simplest non-trivial random graph ensemble where links appear between two nodes with a fixed probability p. The chapter sets out the central representation of graph generation as the result of a discrete-time Markovian stochastic process. This unites the two flavours of graph generation approaches – because they can be viewed as simply moving forwards or backwards

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Ganoulis, Jacques. Transboundary Water Resources Management: Institutional and Engineering Approaches ). Springer, 2011.

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Book chapters on the topic "Graph Approaches in NLP"

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Aizawa, Akiko, and Kyo Kageura. "A graph-based approach to the automatic generation of multilingual keyword clusters." In Recent Advances in Computational Terminology. John Benjamins Publishing Company, 2001. http://dx.doi.org/10.1075/nlp.2.02aiz.

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Falster, Peter. "Graph Theoretical Approaches." In Computer-Aided Production Management. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73318-5_7.

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Frick, Arne, Can Keskin, and Volker Vogelmann. "Integration of declarative approaches (System Demonstration)." In Graph Drawing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62495-3_47.

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Köbler, Johannes. "On Graph Isomorphism for Restricted Graph Classes." In Logical Approaches to Computational Barriers. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11780342_26.

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More, Abhilasha, and Vipul Dalal. "Graph-Based Multi-document Text Summarization Using NLP." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8289-9_17.

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Biedl, Therese C. "Three Approaches to 3D-Orthogonal Box-Drawings." In Graph Drawing. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-37623-2_3.

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Rupp, Matthias. "Graph Kernels." In Statistical and Machine Learning Approaches for Network Analysis. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118346990.ch8.

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Kimura, Yusuke, Kazuma Kusu, Kenji Hatano, and Tokiya Baba. "Automatic Terminology Extraction Using a Dependency-Graph in NLP." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73603-3_38.

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Lin, Tao, and Peter Eades. "Integration of declarative and algorithmic approaches for layout creation." In Graph Drawing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-58950-3_392.

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de Mol, Maarten, Arend Rensink, and James J. Hunt. "Graph Transforming Java Data." In Fundamental Approaches to Software Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28872-2_15.

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Conference papers on the topic "Graph Approaches in NLP"

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Chen, Muhao, and Carlo Zaniolo. "Learning Multi-faceted Knowledge Graph Embeddings for Natural Language Processing." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/744.

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Knowledge graphs have challenged the present embedding-based approaches for representing their multifacetedness. To address some of the issues, we have investigated some novel approaches that (i) captures multilingual transitions on different language-specific versions of knowledge, and (ii) encodes the commonly existing monolingual knowledge with important relational properties and hierarchies. In addition, we propose the use of our approaches in a wide spectrum of NLP tasks that have not been well explored by related works.

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Gupta, Aman, and Yadul Raghav. "Deep Learning Roles based Approach to Link Prediction in Networks." In 9th International Conference on Natural Language Processing (NLP 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101416.

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The problem of predicting links has gained much attention in recent years due to its vast application in various domains such as sociology, network analysis, information science, etc. Many methods have been proposed for link prediction such as RA, AA, CCLP, etc. These methods required hand-crafted structural features to calculate the similarity scores between a pair of nodes in a network. Some methods use local structural information while others use global information of a graph. These methods do not tell which properties are better than others. With an in-depth analysis of these methods, we

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Chen, Muhao, Yingtao Tian, Kai-Wei Chang, Steven Skiena, and Carlo Zaniolo. "Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/556.

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Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Ou

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McWherter, David, Mitchell Peabody, William C. Regli, and Ali Shokoufandeh. "Transformation Invariant Shape Similarity Comparison of Solid Models." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/dfm-21191.

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Abstract This paper presents two complementary approaches to comparing the shape and topology of solid models. First, we develop a mapping of solid models to Model Signature Graphs (MSGs) — labeled, undirected graphs that abstract the boundary representation of the model and capture relevant shape and engineering attributes. Model Signature Graphs are then used to define metric spaces over arbitrary sets of solid models. This paper introduces two such metric spaces: first, a mapping of MSGs to a high-dimension vector space where euclidean distance measures are applied; second, a distance compu

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Zhao, Yue, Francesco Di Maio, Enrico Zio, Qin Zhang, and Chunling Dong. "Genetic Algorithm Optimization of a Dynamic Uncertain Causality Graph (DUCG) for Fault Diagnosis in Nuclear Power Plants." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60199.

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Fault diagnostics is important for the safe operation of Nuclear Power Plants (NPPs). In recent years, data-driven approaches like neural networks, fuzzy and neuro-fuzzy approaches, support vector machine, K-nearest neighbors classifiers and inference methodologies, have been proposed and implemented to tackle the problem. Among these methodologies, Dynamic Uncertain Causality Graph (DUCG) has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicated and, in many cases, redundant on the symptoms needed to correctly classify the fault.

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Da Silva, Thiago Gouveia. "The Minimum Labeling Spanning Tree and Related Problems." In XXXII Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/ctd.2019.6333.

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The minimum labeling spanning tree problem (MLSTP) is a combinatorial optimization problem that consists in finding a spanning tree in a simple edge-labeled graph, i.e., a graph in which each edge has one label associated, by using a minimum number of labels. It is an NP-hard problem that has attracted substantial research attention in recent years. In its turn, the generalized minimum labeling spanning tree problem (GMLSTP) is a generalization of the MLSTP that allows the situation in which multiple labels can be assigned to an edge. Both problems have several practical applications in import

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Ekmekci, Berk, and Blake Howald. "WAFFLE: A Graph for WordNet Applied to FreeForm Linguistic Exploration." In Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS). Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.nlposs-1.21.

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Rahul, Surabhi Adhikari, and Monika. "NLP based Machine Learning Approaches for Text Summarization." In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2020. http://dx.doi.org/10.1109/iccmc48092.2020.iccmc-00099.

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Feng, Steven, Varun Gangal, Jason Wei, et al. "A Survey of Data Augmentation Approaches for NLP." In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-acl.84.

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Saqur, Raeid, and Ameet Deshpande. "CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes." In Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS). Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.nlposs-1.3.

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Reports on the topic "Graph Approaches in NLP"

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Horan, Victoria, and Michael Gudaitis. Investigation of Zero Knowledge Proof Approaches Based on Graph Theory. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada540835.

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Horan, Victoria, Steve Adachi, and Stanley Bak. A Comparison of Approaches for Solving Hard Graph-Theoretic Problems. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada623530.

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Imbens, Guido. Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w26104.

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