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检索条件"任意字段=6th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2011"

共 19 条记录，以下是1-10 订阅

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Cross Examine: An Ensemble-based approach to leverage Large language Models for Legal Text Analytics 6

Cross Examine: An Ensemble-based approach to leverage Large ...

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6th natural Legal language processing workshop 2024, NLLP 2024, co-located with the 2024 Conference on Empirical methods in natural language processing

作者： Chowdhury, Saurav Joshi, Suyog Dey, Lipika Indian Institute of Technology Jodhpur India Ashoka University India

ISBN: (纸本)9798891761834

Legal documents are complex in nature, describing a course of argumentative reasoning that is followed to settle a case. Churning through large volumes of legal documents is a daily requirement for a large number of professionals who need access to the information embedded in them. natural language processing(NLP) methods that help in document summarization with key information components, insight extraction and question answering play a crucial role in legal text processing. Most of the existing document analysis systems use supervised machine learning, which require large volumes of annotated training data for every different application and are expensive to build. In this paper we propose a legal text analytics pipeline using Large language Models (LLMs), which can work with little or no training data. For document summarization, we propose an iterative pipeline using retrieval augmented generation to ensure that the generated text remains contextually relevant. For question answering, we propose a novel ontology-driven ensemble approach similar to cross-examination that exploits questioning and verification principles. A knowledge graph, created with the extracted information, stores the key entities and relationships reflecting the repository content structure. A new dataset is created with Indian court documents related to bail applications for cases filed under POCSO1 Act. Analysis of insights extracted from the answers reveal patterns of crime and social conditions leading to those crimes, which are important inputs for social scientists as well as legal system. ©2024 Association for Computational Linguistics.

关键词： Question answering

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IJS at textgraphs-16 natural language Premise Selection Task: Will Contextual Information Improve natural language Premise Selection? 16

IJS at TextGraphs-16 Natural Language Premise Selection Task...

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16th workshop on graph-based methods for natural language processing, textgraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

作者： Tran, Hanh thi Hong Martinc, Matej Doucet, Antoine Pollak, Senja Jožef Stefan Institute Slovenia Jozef Stefan International Postgraduate School Slovenia University of La Rochelle France

natural language Premise Selection (NLPS) is a mathematical natural language processing (NLP) task that retrieves a set of useful relevant premises to support the end-user finding the proof for a particular statement. In this paper, we evaluate the impact of Transformer-based contextual information and different fundamental similarity scores towards NLPS. the results demonstrate that the contextual representation is better at capturing meaningful information despite not being pretrained on mathematical background in comparison with the statistical approach (e.g., the TF-IDF) with a boost of around 3.00% MAP@500. Our code is publicly available at https://***/honghanhh/premise-selection. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： natural language processing systems

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A Comparative Assessment of State-Of-the-Art methods for Multilingual Unsupervised Keyphrase Extraction 17th

A Comparative Assessment of State-Of-The-Art Methods for Mul...

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17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE workshop / AI-BIO workshop / DAAI workshop / DARE workshop / EEAI workshop / 10th MHDW workshop

作者： Giarelis, Nikolaos Kanakaris, Nikos Karacapilidis, Nikos Univ Patras Ind Management & Informat Syst Lab MEAD Rion 26504 Greece

ISBN: (纸本)9783030791520;9783030791506;9783030791490

Keyphrase extraction is a fundamental task in information management, which is often used as a preliminary step in various information retrieval and natural language processing tasks. the main contribution of this paper lies in providing a comparative assessment of prominent multilingual unsupervised keyphrase extraction methods that build on statistical (RAKE, YAKE), graph-based (TextRank, SingleRank) and deep learning (KeyBERT) methods. For the experimentations reported in this paper, we employ well-known datasets designed for keyphrase extraction from five different natural languages (English, French, Spanish, Portuguese and Polish). We use the F1 score and a partial match evaluation framework, aiming to investigate whether the number of terms of the documents and the language of each dataset affect the accuracy of the selected methods. Our experimental results reveal a set of insights about the suitability of the selected methods in texts of different sizes, as well as the performance of these methods in datasets of different languages.

关键词： natural language processing Keyphrase extraction Unsupervised learning Deep learning graph-based models Empirical research

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Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks 59

Knowledge-Enriched Event Causality Identification via Latent...

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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： Cao, Pengfei Zuo, Xinyu Chen, Yubo Liu, Kang Zhao, Jun Chen, Yuguang Peng, Weihua Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Beijing Baidu Netcom Sci Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781954085527

Identifying causal relations of events is an important task in natural language processing area. However, the task is very challenging, because event causality is usually expressed in diverse forms that often lack explicit causal clues. Existing methods cannot handle well the problem, especially in the condition of lacking training data. Nonetheless, humans can make a correct judgement based on their background knowledge, including descriptive knowledge and relational knowledge. Inspired by it, we propose a novel Latent Structure Induction Network (LSIN) to incorporate the external structural knowledge into this task. Specifically, to make use of the descriptive knowledge, we devise a Descriptive graph Induction module to obtain and encode the graph-structured descriptive knowledge. To leverage the relational knowledge, we propose a Relational graph Induction module which is able to automatically learn a reasoning structure for event causality reasoning. Experimental results on two widely used datasets indicate that our approach significantly outperforms previous state-of-the-art methods.

关键词： natural language processing systems

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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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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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Document-level Event Extraction via Heterogeneous graph-based Interaction Model with a Tracker 59

Document-level Event Extraction via Heterogeneous Graph-base...

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作者： Xu, Runxin Liu, Tianyu Li, Lei Chang, Baobao Peking Univ Key Lab Computat Linguist MOE Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China ByteDance AI Lab Beijing Peoples R China

ISBN: (纸本)9781954085527

Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences;b) the correlation among events in a document is non-trivial to model. In this paper, we propose Heterogeneous graph-based Interaction Model with a Tracker (GIT) to solve the aforementioned two challenges. For the first challenge, GIT constructs a heterogeneous graph interaction network to capture global interactions among different sentences and entity mentions. For the second, GIT introduces a Tracker module to track the extracted events and hence capture the interdependency among the events. Experiments on a large-scale dataset (Zheng et al., 2019) show GIT outperforms the existing best methods by 2.8 F1. Further analysis reveals GIT is effective in extracting multiple correlated events and event arguments that scatter across the document.

关键词： graphic methods

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Attention Flows are Shapley Value Explanations 59

Attention Flows are Shapley Value Explanations

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作者： Ethayarajh, Kawin Jurafsky, Dan Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781954085534

Shapley Values, a solution to the credit assignment problem in cooperative game theory, are a popular type of explanation in machine learning, having been used to explain the importance of features, embeddings, and even neurons. In NLP, however, leave-oneout and attention-based explanations still predominate. Can we draw a connection between these different methods? We formally prove that - save for the degenerate case - attention weights and leave-one-out values cannot be Shapley Values. Attention flow is a post-processed variant of attention weights obtained by running the max-flow algorithm on the attention graph. Perhaps surprisingly, we prove that attention flows are indeed Shapley Values, at least at the layerwise level. Given the many desirable theoretical qualities of Shapley Values - which has driven their adoption among the ML community - we argue that NLP practitioners should, when possible, adopt attention flow explanations alongside more traditional ones.

关键词： natural language processing systems

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CitationIE: Leveraging the Citation graph for Scientific Information Extraction 59

CitationIE: Leveraging the Citation Graph for Scientific Inf...

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作者： Viswanathan, Vijay Neubig, Graham Liu, Pengfei Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9781954085527

Automatically extracting key information from scientific documents has the potential to help scientists work more efficiently and accelerate the pace of scientific progress. Prior work has considered extracting document-level entity clusters and relations end-to-end from raw scientific text, which can improve literature search and help identify methods and materials for a given problem. Despite the importance of this task, most existing works on scientific information extraction (SciIE) consider extraction solely based on the content of an individual paper, without considering the paper's place in the broader literature. In contrast to prior work, we augment our text representations by leveraging a complementary source of document context: the citation graph of referential links between citing and cited papers. On a test set of English-language scientific documents, we show that simple ways of utilizing the structure and content of the citation graph can each lead to significant gains in different scientific information extraction tasks. When these tasks are combined, we observe a sizable improvement in end-to-end information extraction over the state-of-the-art, suggesting the potential for future work along this direction. We release software tools to facilitate citation-aware SciIE development.(1)

关键词： Information retrieval

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A Multi-Level Attention Model for Evidence-based Fact Checking

A Multi-Level Attention Model for Evidence-Based Fact Checki...

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作者： Kruengkrai, Canasai Yamagishi, Junichi Wang, Xin Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781954085541

Evidence-based fact checking aims to verify the truthfulness of a claim against evidence extracted from textual sources. Learning a representation that effectively captures relations between a claim and evidence can be challenging. Recent state-of-the-art approaches have developed increasingly sophisticated models based on graph structures. We present a simple model that can be trained on sequence structures. Our model enables inter-sentence attentions at different levels and can benefit from joint training. Results on a large-scale dataset for Fact Extraction and VERification (FEVER) show that our model outperforms the graph-based approaches and yields 1.09% and 1.42% improvements in label accuracy and FEVER score, respectively, over the best published model.

关键词： graphic methods

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LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking 59

LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linki...

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作者： Jiang, Hang Gurajada, Sairam Lu, Qiuhao Neelam, Sumit Popa, Lucian Sen, Prithviraj Li, Yunyao Gray, Alexander MIT Cambridge MA 02139 USA IBM Res Yorktown Hts NY USA Univ Oregon Eugene OR 97403 USA

ISBN: (纸本)9781954085527

Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversational systems. Entity linking on short text (e.g., single sentence or question) poses particular challenges due to limited context. While prior approaches use either heuristics or black-box neural methods, here we propose LNNEL, a neuro-symbolic approach that combines the advantages of using interpretable rules based on first-order logic with the performance of neural learning. Even though constrained to using rules, LNN-EL performs competitively against SotA black-box neural approaches, with the added benefits of extensibility and transferability. In particular, we show that we can easily blend existing rule templates given by a human expert, with multiple types of features (priors, BERT encodings, box embeddings, etc), and even scores resulting from previous EL methods, thus improving on such methods. For instance, on the LC-QuAD-1.0 dataset, we show more than 4% increase in F1 score over previous SotA. Finally, we show that the inductive bias offered by using logic results in learned rules that transfer well across datasets, even without fine tuning, while maintaining high accuracy.

关键词： Heuristic methods

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