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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： Luo, Chuwei Wang, Yongpan Zheng, Qi Li, Liangcheng Gao, Feiyu Zhang, Shiyu Alibaba Group China College of Computer Science Zhejiang University No.38 Zheda Road China

ISBN: (纸本)9781952148422

Named entity recognition (NER) from visual documents, such as invoices, receipts or business cards, is a critical task for visual document understanding. Most classical approaches use a sequence-based model (typically BiLSTM-CRF framework) without considering document structure. Recent work on graph-based model using graph convolutional networks to encode visual and textual features have achieved promising performance on the task. However, few attempts take geometry information of text segments (text in bounding box) in visual documents into account. Meanwhile, existing methods do not consider that related text segments which need to be merged to form a complete entity in many real-world situations. In this paper, we present graphNEMR, a graph-based model that uses graph convolutional networks to jointly merge text segments and recognize named entities. By incorporating geometry information from visual documents into our model, richer 2D context information is generated to improve document representations. To merge text segments, we introduce a novel mechanism that captures both geometry information as well as semantic information based on pre-trained language model. Experimental results show that the proposed graphNEMR model outperforms both sequence-based and graph-based SOTA methods significantly. © COLING *** rights reserved.

关键词： Geometry

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Implicit Business Competitor Inference Using Heterogeneous Knowledge graph 12

Implicit Business Competitor Inference Using Heterogeneous K...

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12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Qin, Wei Luo, Xiangfeng Wang, Hao Shanghai University School of Computer Engineering and Science Shanghai China

ISBN: (纸本)9781665438582

Competitor inference is the task of identifying current or potential competitors given their primary markets and Business Scope. Previous methods have achieved remarkable success on explicit competitor inference using state-of-the-art natural language processing (NLP) techniques, mainly relying on comparative expressions. However, those methods lack interpretability and cannot identify implicit competitors without the explicit mentions of competitive relationships in the text. To remedy these problems, in this paper, we propose a probabilistic graphical model which leverages heterogeneous enterprise knowledge graph containing both structured information, e.g., Product Analysis, Sales Territory, and unstructured information, e.g., Business Scope. The model is defined with first-order logic rules using the declarative language of Probabilistic Soft Logic (PSL). As a result, our model enables predicting implicit competitors while provides pieces of interpretable evidence. Experimental results show that our approach is significantly superior to previous methods. © 2021 IEEE.

关键词： Knowledge graph

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Dynamic and Multi-Channel graph Convolutional Network for Aspect-based Sentiment Analysis

Dynamic and Multi-Channel Graph Convolutional Network for As...

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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： Pang, Shiguan Xue, Yun Yang, Zehao Huang, Weihao Feng, Jinhui South China Normal Univ Sch Phys & Telecommun Engn Guangzhou Peoples R China

ISBN: (纸本)9781954085541

Aspect-based sentiment analysis aims to judge the polarity of the given aspect word in reviews. Most recent methods adopt syntax-based graph Neural Networks to extract the syntactic information from the dependency graph, thinking that would be beneficial for establishing relations between aspect and opinion words. Houver, these methods may ignore that son Abstrntences have no remarkable syntactic stru , which causes the opposite judgement in entiment analysis. In this paper, we figure out this problem by means of optimally fusing syntactic information, semantic information and their combinations simultaneously. firstly, syntactic graphs and semantic graphs are generated by dependency tree and multi-head self-attention respectively. we propose a Dynamic and Multi-channel graph Convolutional Network (DM-GCN) to learn the correlated information from the generated graphs effectively. Our extensive experiments on SemEval 2014 and Twitter datasets confirm that DM-GCN fuses syntactic, semantic and their combinations optimally and out-performs all state-of-the-art alternatives with a large margin.

关键词： Syntactics

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[email protected] 2023: The first workshop on Generative Information Retrieval 23

[email protected] 2023: The First Workshop on Generative Inf...

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proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Garbiel Bénédict Ruqing Zhang Donald Metzler University of Amsterdam and RTL NL Amsterdam Netherlands ICT Chinese Academy of Sciences Beijing China Google Research Mountain View USA

ISBN: (纸本)9781450394086

Generative information retrieval (IR) has experienced substantial growth across multiple research communities (e.g., information retrieval, computer vision, natural language processing, and machine learning), and has been highly visible in the popular press. Theoretical, empirical, and actual user-facing products have been released that retrieve documents (via generation) or directly generate answers given an input request. We would like to investigate whether end-to-end generative models are just another trend or, as some claim, a paradigm change for IR. This necessitates new metrics, theoretical grounding, evaluation methods, task definitions, models, user interfaces, etc. The goal of this workshop1 is to focus on previously explored Generative IR techniques like document retrieval and direct Grounded Answer Generation, while also offering a venue for the discussion and exploration of how Generative IR can be applied to new domains like recommendation systems, summarization, etc. The format of the workshop is interactive, including roundtable and keynote sessions and tends to avoid the one-sided dialogue of a mini-conference.

关键词： information retrieval large language models generative models

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A Flexible Knowledge graph Error Detection Framework Combined with Semantic Information 12th

A Flexible Knowledge Graph Error Detection Framework Combine...

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12th CCF Conference on BigData, BigData 2024

作者： Zhao, Yangwu Liu, Yang Ao, Xiang He, Qing Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China

ISBN: (纸本)9789819610235

Knowledge graphs (KGs) are extensively utilized in numerous applications, including question-answering systems and recommender systems. However, knowledge graphs are often constructed through web crawling or crowdsourcing, leading to errors in the data. The task of knowledge graph error detection aims to identify inaccurate triplets in KGs and has received substantial attention in recent years. However, the majority of current error detection methods overlook the semantic information of the triplets, which can be vital for accurate error detection. In this paper, we introduce a Flexible Knowledge graph Error Detection Framework that integrates Semantic Information (FKED), which combines both structural and semantic information to detect errors within the knowledge graph. FKED first extracts the structural information of KGs using a graph embedding model. Next, FKED employs a pre-trained language model (PLM) to extract semantic information from the triplets. Then the structural and semantic information are combined to detect errors within the knowledge graph. FKED can be flexibly added to other structure-based error detection models, enhancing their capabilities in downstream tasks of knowledge graphs. We assess FKED using two benchmark datasets: FB15k-237 and WN18RR. Experimental results indicate that our method is both superior and effective, substantially improving the performance of the base models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Legal Judgment Prediction via Relational Learning 21

Legal Judgment Prediction via Relational Learning

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44th International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Dong, Qian Niu, Shuzi Chinese Acad Sci Inst Software Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781450380379

Given a legal case and all law articles, Legal Judgment Prediction (LJP) is to predict the case's violated articles, charges and term of penalty. naturally, these labels are entangled among different tasks and within a task. For example, each charge is only logically or semantically related to some fixed articles. Ignoring these constraints, LJP methods would predict unreliable results. To solve this problem, we first formalize LJP as a node classification problem over a global consistency graph derived from the training set. In terms of node encoder, we utilize a masked transformer network to obtain case aware node representations consistent among tasks and discriminative within a task. In terms of node classifier, each node's label distribution is dependent on its neighbors' in this graph to achieve local consistency by relational learning. Both the node encoder and classifier are optimized by variational EM. Finally, we propose a novel measure to evaluate self-consistency of classification results. Experimental results on two benchmark datasets demonstrate that the F1 improvement of our method is about 4.8% compared with SOTA methods.

关键词： Statistical Relational Learning natural language processing Legal Judgement Prediction

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A Knowledge-Injected Curriculum Pretraining Framework for Question Answering

arXiv

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arXiv 2024年

作者： Lin, Xin Su, Tianhuang Huang, Zhenya Xue, Shangzi Liu, Haifeng Chen, Enhong School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Guangdong OPPO Mobile Telecommunications Corp. Ltd Shenzhen China University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China

Knowledge-based question answering (KBQA) is a key task in natural language processing research, and also an approach to access the web data and knowledge, which requires exploiting knowledge graphs (KGs) for reasoning. In the literature, one promising solution for KBQA is to incorporate the pretrained language model (LM) with KGs by generating KG-centered pretraining corpus, which has shown its superiority. However, these methods often depend on specific techniques and resources to work, which may not always be available and restrict its application. Moreover, existing methods focus more on improving language understanding with KGs, while neglect the more important human-like complex reasoning. To this end, in this paper, we propose a general Knowledge-Injected Curriculum Pretraining framework (KICP) to achieve comprehensive KG learning and exploitation for KBQA tasks, which is composed of knowledge injection (KI), knowledge adaptation (KA) and curriculum reasoning (CR). Specifically, the KI module first injects knowledge into the LM by generating KG-centered pretraining corpus, and generalizes the process into three key steps that could work with different implementations for flexible application. Next, the KA module learns knowledge from the generated corpus with LM equipped with an adapter as well as keeps its original natural language understanding ability to reduce the negative impacts of the difference between the generated and natural corpus. Last, to enable the LM with complex reasoning, the CR module follows human reasoning patterns to construct three corpora with increasing difficulties of reasoning, and further trains the LM from easy to hard in a curriculum manner to promote model learning. We provide an implementation of the general framework, and evaluate the proposed KICP on four real-word datasets. The results demonstrate that our framework can achieve higher performances, and have good generalization ability to other QA tasks. Copyright © 2024, The

关键词： Knowledge graph

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An Automatic Method for Standartizing Argumentative Annotations across Annotators

An Automatic Method for Standartizing Argumentative Annotati...

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Annual Siberian Russian workshop on Electron Devices and Materials (EDM)

作者： Ivan S. Pimenov Natalia V. Salomatina Artificial Intelligence Laboratory Ershov Institute of Informatics Systems Novosibirsk Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The prevalence of machine learning methods in addressing natural language processing tasks, particularly the problem of automatic extraction of argumentation structures, entails a demand for developing sufficient-sized text corpora with reliable argumentation annotation. Annotation reliability implies consistency in annotating analyzed entities, especially in case of several different annotators performing annotation of a large-sized corpus. Evaluation of annotation reliability traditionally relies on calculating inter-annotator agreement coefficients. The improvement of annotations consistency in the presented work relies on an automatic method for standardizing argumentative annotations in form of argumentation graphs with two node types: information nodes with the text content of argumentative statements (premises, conclusions) and schemes nodes indicating reasoning models (from Walton’s classification) at the base of connections between statements. The modification of annotations consists in, first, removal of leaf information nodes present only in one annotation version of a text, and second, in replacement of disagreement-causing schemes in accordance with a hierarchy of pairwise rules based on functional properties of these schemes. Calculation of Krippendorff’s alpha shows a considerable increase of inter-annotator agreement after the automatic modification of the corpus containing 100 argumentative annotations for 50 scientific articles. Classification scores between the unmodified and modified corpus exhibit a limited 2–5% increase of F-measure for identifying three analyzed schemes (VerbalClassification, PartToWhole, CorrelationToCause). We conclude that, for evaluating efficiency of automatic annotations unification methods, the improvement of inter-annotator agreement coefficients acts as a less reliable measure than the dynamic of experimental classification scores between the unmodified and modified datasets.

关键词： Annotations Machine learning Linguistics Cognition natural language processing Reliability Task analysis

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Wordup! At vaxxstance 2021: Combining contextual information with textual and dependency-based syntactic features for stance detection

Wordup! At vaxxstance 2021: Combining contextual information...

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2021 Iberian languages Evaluation Forum, IberLEF 2021

作者： Lai, Mirko Cignarella, Alessandra Teresa Finos, Livio Sciandra, Andrea Università Degli Studi di Torino Torino Italy Universitat Politècnica de València València Spain Università Degli Studi di Padova Padova Italy Università Degli Studi di Modena e Reggio Emilia Reggio Emilia Italy

In this paper we describe the participation of theWordUp! team in the VaxxStance shared task at IberLEF 2021. The goal of the competition is to determine the author's stance from tweets written both in Spanish and Basque on the topic of the Antivaxxers movement. Our approach, in the four different tracks proposed, combines the Logistic Regression classifier with diverse groups of features: Stylistic, tweet-based, user-based, lexicon-based, dependency-based, and network-based. The outcomes of our experiments are in line with state-of-the-art results on other languages, proving the efficacy of combining methods derived from NLP and Network Science for detecting stance in Spanish and Basque. © 2021 CEUR-WS. All rights reserved.

关键词： natural language processing systems

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Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources

Overview of DisTEMIST at BioASQ: Automatic detection and nor...

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2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Miranda-Escalada, Antonio Gascó, Luis Lima-López, Salvador Farré-Maduell, Eulàlia Estrada, Darryl Nentidis, Anastasios Krithara, Anastasia Katsimpras, Georgios Paliouras, Georgios Krallinger, Martin Barcelona Supercomputing Center Spain National Center for Scientific Research "Demokritos" Athens Greece Aristotle University of Thessaloniki Thessaloniki Greece

There is a pressing need for advanced semantic annotation technologies of medical content, in particular medical publications, clinical trials and clinical records. Search engines and information retrieval systems require semantic annotation and indexing systems to support more advanced user search queries. Considering the relevance of disease concepts for clinical coding, automated processing of clinical trials and even patents, it is critical to provide access to high quality manually annotated documents labelled by clinicians for the development and benchmarking of disease mention recognition and grounding tools. This is particularly important for medical content beyond English, where even fewer annotated corpora have been released. To address these issues, we have organized the DisTEMIST (DISease TExt MIning Shared Task) track at BioASQ 2022. It represents the first community effort to evaluate and promote the development of resources for automatic detection and normalization of disease mentions from clinical case documents in Spanish. For this track we have released the DisTEMIST corpus, a collection of 1000 clinical case documents carefully selected by clinicians and annotated manually by a team of healthcare professionals following annotation guidelines and quality control analysis for consistency. Disease mentions were exhaustively mapped by these experts to their corresponding SNOMED CT concept identifiers. Moreover, we have created additional multilingual Silver Standard versions of the corpus for 7 languages (English, Portuguese, French, Italian, Romanian, Catalan and Galician), as well as mention normalization cross-mappings to 4 additional highly used terminologies. We received 38 systems or runs from 9 teams, obtaining very competitive results. Most participants implemented sophisticated AI approaches, mainly deep learning algorithms based on pre-trained transformer-like language models (BERT, BETO, RoBERTa, etc.), with a classifier layer for named ent

关键词： natural language processing systems

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