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Textgraphs 2021 - graph-based methods for natural language processing, proceedings of the 15th workshop - in conjunction with the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 2021

TextGraphs 2021 - Graph-Based Methods for Natural Language P...

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15th workshop on graph-based methods for natural language processing, Textgraphs 2021

ISBN: (纸本)9781954085381

The proceedings contain 20 papers. The topics discussed include: bootstrapping large-scale fine-grained contextual advertising classifier from Wikipedia;modeling graph structure via relative position for text generation from knowledge graphs;entity prediction in knowledge graphs with joint embeddings;hierarchical graph convolutional networks for jointly resolving cross-document coreference of entity and event mentions;learning clause representation from dependency-anchor graph for connective prediction;selective attention based graph convolutional networks for aspect-level sentiment classification;keyword extraction using unsupervised learning on the document’s adjacency matrix;improving human text simplification with sentence fusion;and on geodesic distances and contextual embedding compression for text classification.

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A Survey of the Application of Neural Networks to Event Extraction

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Tsinghua Science and Technology 2025年第2期30卷 748-768页

作者： Jianye Xie Yulan Zhang Huaizhen Kou Xiaoran Zhao Zhikang Feng Lekang Song Weiyi Zhong College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266000China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261000China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210000China School of Computer Science Qufu Normal UniversityRizhao 276800China

Event extraction is an important part of natural language information extraction,and it’s widely employed in other natural language processing tasks including question answering and machine reading ***,there is a lack of recent comprehensive survey papers on event *** the past few years,numerous high-quality and innovative event extraction methods have been proposed,making it necessary to consolidate these new developments with previous work in order to provide a clear overview for researchers and serve as a reference for future *** addition,event detection is a fundamental sub-task in event extraction,previous survey papers have often overlooked the related work on event ***,this paper aims to bridge these gaps by presenting a comprehensive survey of event extraction,including recent advancements and an analysis of previous research on event *** resources for event extraction are first introduced in this research,and then the numerous neural network models currently employed in event extraction tasks are divided into four types:word sequence-based methods,graph-based neural network methods,external knowledge-based approaches,and prompt-based *** compare and contrast them in depth,pointing out the flaws and difficulties with existing ***,we discuss the future of event extraction development.

关键词： event extraction natural language processing event extraction methods graph neural network prompt-based learning

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Knowledge graph Completion With Pattern-based methods

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IEEE ACCESS 2025年 13卷 5815-5831页

作者： Sabet, Maryam Pajoohan, Mohammadreza Moosavi, Mohammad Reza Yazd Univ Comp Engn Dept Yazd *** Iran Shiraz Univ Sch Elect & Comp Engn Dept Comp Sci & Engn & IT Shiraz *** Iran

Knowledge graphs (KGs) are popularly used to develop several intelligent applications. Revealing valuable knowledge hidden in these graphs opened up a branch of research, known as KG reasoning, aiming at predicting the missing links. Some methods take advantage of external information such as entity description but at the cost of more computational complexity. Besides, most of the current techniques focus solely on local information in the KG. However, the learning process can utilise valuable global information in the entire graph. In this paper, we propose a Pattern-based Knowledge graph Completion (PKGC) method that consists of three phases. The first phase utilizes multi-source information and expands the KG using entity description as external information with efficient natural language processing (NLP) techniques. In the second phase, we mine frequent patterns from the expanded KG, extract connections between them and assign entities to the patterns that construct the abstraction layer. based on the extracted patterns, connections, and entity assignments, a flow network is constructed on the abstraction layer in the third phase. We use global internal information, namely patterns, by adapting the minimum-cost circulation problem to the flow network. This way the links in a larger neighborhood are involved in the inference. We conducted experiments on the link prediction task and evaluated the training time on two benchmark datasets, WordNet and Freebase. Experiments have demonstrated that the proposed method is superior to the state-of-the-art methods and that pattern extraction is effective for knowledge graph completion tasks.

关键词： Cognition Knowledge graphs Vectors Semantics Tail Translation natural language processing Training Tensors Surveys Frequent pattern mining flow network knowledge graph completion minimum-cost circulation problem

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Emotion Classification With Visibility graphs

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IEEE SIGNAL processing LETTERS 2025年 32卷 2055-2059页

作者： Simsek, Ecem Topcu, Atakan Koc, Emirhan Saritas, Emine Ulku Koc, Aykut Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkiye Bilkent Univ UMRAM TR-06800 Ankara Turkiye

Transformers have gained prominence in natural language processing due to their representational capabilities and performances. Transformers process natural language as a sequence on finite context windows;however, global relationships among words beyond these windows cannot be completely modeled via sequence processing only. graph neural network (GNN) based models have been proposed to alleviate this problem, as they provide geometric extensions to neural networks, enabling models to learn associations within a text. However, regular graph-based methods ignore the sequential nature of underlying texts. In this paper, we propose EmoVis, the first generic graph-based neural network that utilizes visibility graphs, which converts classical time-series information to graph representations. We cast the problem as an emotion classification task, enabling the proposed model to learn associations between the labels and words in a sentence. Moreover, EmoVis can be used as a highly modular graph-based extension to any transformer-based model, significantly improving their performance and learning capabilities in various languages. We experimentally show that EmoVis enables transformer-based models to outperform the state-of-the-art baselines across three diverse datasets in different languages in the SemEval2018 competition datasets and the GoEmotions dataset.

关键词： Vectors Transformers Time series analysis Training Context modeling Computational modeling Topology Indexes graph convolutional networks Data mining Visibility graphs graphs transformer natural language processing classification graph neural networks (GNNs)

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proceedings of Textgraphs: The 1st workshop on graph-based methods for natural language processing

Proceedings of TextGraphs: The 1st Workshop on Graph-Based M...

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

The proceedings contain 15 papers. The topics discussed include: a graphical framework for contextual search and name disambiguation in email;graph based semi-supervised approach for information extraction;graph-based text representation for novelty detection;measuring aboutness of an entity in a text;a study of two graph algorithms in topic-driven summarization;similarity between pairs of co-indexed trees for textual entailment recognition;learning of graph-based question answering rules;seeing stars when there aren’t many stars: graph-based semi-supervised learning for sentiment categorization;and random-walk term weighting for improved text classification.

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COLING 2020 - graph-based methods for natural language processing - proceedings of the 14th workshop, Textgraphs 2020

COLING 2020 - Graph-Based Methods for Natural Language Proce...

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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

ISBN: (纸本)9781952148422

The proceedings contain 14 papers. The topics discussed include: a survey of embedding models of entities and relationships for knowledge graph completion;graph-based aspect representation learning for entity resolution;merge and recognize: a geometry and 2D context aware graph model for named entity recognition from visual documents;joint learning of the graph and the data representation for graph-based semi-supervised learning;contextual BERT: conditioning the language model using a global state;semi-supervised word sense disambiguation using example similarity graph;incorporating temporal information in entailment graph mining;relation specific transformations for open world knowledge graph completion;and PGL at Textgraphs 2020 Shared Task: explanation regeneration using language and graph learning methods.

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proceedings of Textgraphs@ACL 2017: The 11th workshop on graph-based methods for natural language processing

Proceedings of TextGraphs@ACL 2017: The 11th Workshop on Gra...

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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

ISBN: (纸本)9781945626609

The proceedings contain 10 papers. The topics discussed include: adapting predominant and novel sense discovery algorithms for identifying corpus-specific sense differences;merging knowledge bases in different languages;parameter free hierarchical graph-based clustering for analyzing continuous word embeddings;spectral graph-based method of multimodal word embedding;extract with order for coherent multi-document summarization;work hard, play hard: email classification on the avocado and Enron corpora;a graph based semi-supervised approach for analysis of derivational nouns in Sanskrit;and evaluating text coherence based on semantic similarity graph.

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HSE NLP Team at MEDIQA-CORR 2024 Task: In-Prompt Ensemble with Entities and Knowledge graph for Medical Error Correction 6

HSE NLP Team at MEDIQA-CORR 2024 Task: In-Prompt Ensemble wi...

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6th workshop on Clinical natural language processing, ClinicalNLP 2024, held at NAACL 2024

作者： Valiev, Airat Tutubalina, Elena HSE University Moscow Russia Kazan State University Kazan Russia

ISBN: (纸本)9798891761094

This paper presents our LLM-based system designed for the MEDIQA-CORR @ NAACLClinicalNLP 2024 Shared Task 3, focusing on medical error detection and correction in medical records. Our approach consists of three key components: entity extraction, prompt engineering, and ensemble. first, we automatically extract biomedical entities such as therapies, diagnoses, and biological species. Next, we explore few-shot learning techniques and incorporate graph information from the MeSH database for the identified entities. Finally, we investigate two methods for ensembling: (i) combining the predictions of three previous LLMs using an AND strategy within a prompt and (ii) integrating the previous predictions into the prompt as separate ‘expert’ solutions, accompanied by trust scores representing their performance. The latter system ranked second with a BERTScore score of 0.8059 and third with an aggregated score of 0.7806 out of the 15 teams’ solutions in the shared task. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Reducing Knowledge Noise for Improved Semantic Analysis in Biomedical natural language processing Applications 5

Reducing Knowledge Noise for Improved Semantic Analysis in B...

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5th workshop on Clinical natural language processing, ClinicalNLP 2023. held at ACL 2023

作者： Naseem, Usman Thapa, Surendrabikram Zhang, Qi Hu, Liang Masood, Anum Nasim, Mehwish University of Sydney Australia Virginia Tech United States Tongji University China DeepBlue Academy of Sciences China Norwegian University of Science and Technology Norway University of Western Australia Australia Flinders University Australia

ISBN: (纸本)9781959429883

graph-based techniques have gained traction for representing and analyzing data in various natural language processing (NLP) tasks. Knowledge graph-based language representation models have shown promising results in leveraging domain-specific knowledge for NLP tasks, particularly in the biomedical NLP field. However, such models have limitations, including knowledge noise and neglect of contextual relationships, leading to potential semantic errors and reduced accuracy. To address these issues, this paper proposes two novel methods. The first method combines knowledge graphbased language model with nearest-neighbor models to incorporate semantic and category information from neighboring instances. The second method involves integrating knowledge graph-based language model with graph neural networks (GNNs) to leverage feature information from neighboring nodes in the graph. Experiments on relation extraction (RE) and classification tasks in English and Chinese language datasets demonstrate significant performance improvements with both methods, highlighting their potential for enhancing the performance of language models and improving NLP applications in the biomedical domain. © 2023 Association for Computational Linguistics.

关键词： Semantics

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Tibetan Dependency Parsing with graph Convolutional Neural Networks 1

Tibetan Dependency Parsing with Graph Convolutional Neural N...

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1st workshop on Ancient language processing, ALP 2023

作者： An, Bo Institute of Ethnology and Anthropology Chinese Academy of Social Sciences Building 6 Zhongguancun Nandajie 27 Beijing China

ISBN: (纸本)9789544520878

Dependency parsing is a syntactic analysis method to analyze the dependency relationships between words in a sentence. The interconnection between words through dependency relationships is typical graph data. Traditional Tibetan dependency parsing methods typically model dependency analysis as a transition-based or sequence-labeling task, ignoring the graph information between words. We propose a graph neural network (GNN)-based Tibetan dependency parsing method to address this issue. This method treats Tibetan words as nodes and the dependency relationships between words as edges, thereby constructing the graph data of Tibetan sentences. Specifically, we use BiLSTM to learn the word representations of Tibetan, utilize GNN to model the relationships between words, and employ MLP to predict the types of relationships between words. We conduct experiments on a Tibetan dependency database, and the results show that the proposed method can achieve high-quality Tibetan dependency parsing results. © RANLP-ALP 2023 - proceedings of the Ancient language processing workshop, associated with 14th International Conference on Recent Advances in natural language processing.

关键词： Syntactics

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