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4th natural Legal language processing workshop, NLLP 2022, co-located with the 2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Smădu, Răzvan-Alexandru Dinică, Ion-Robert Avram, Andrei-Marius Cercel, Dumitru-Clementin Pop, Florin Cercel, Mihaela-Claudia University Politehnica of Bucharest Faculty of Automatic Control and Computers Romania National Institute for Research & Development in Informatics - ICI Bucharest Romania First District Court of Buftea Romania

ISBN: (纸本)9781959429180

Named Entity Recognition (NER) is a well-explored area from Information Retrieval and natural language processing with an extensive research community. Despite that, few languages, such as English and German, are well-resourced, whereas many other languages, such as Romanian, have scarce resources, especially in domain-specific applications. In this work, we address the NER problem in the legal domain from both Romanian and German languages and evaluate the performance of our proposed method based on domain adaptation. We employ multi-task learning to jointly train a neural network on two legal and general domains and perform adaptation among them. The results show that domain adaptation increase performances by a small amount, under 1%, while considerable improvements are in the recall metric. © 2022 Association for Computational Linguistics.

关键词： natural language processing systems

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KGRefiner: Knowledge graph Refinement for Improving Accuracy of Translational Link Prediction methods 3

KGRefiner: Knowledge Graph Refinement for Improving Accuracy...

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3rd workshop on Simple and Efficient natural language processing, SustaiNLP 2022

作者： Saeedizade, Mohammad Javad Torabian, Najmeh Minae-Bidgol, Behrouz School of Computer Engineering Iran University of Science and Technology Tehran Iran Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran

ISBN: (纸本)9781959429241

Link Prediction is the task of predicting missing relations between knowledge graph(KG) entities. Recent work in link prediction mainly attempted to adapt a model to increase link prediction accuracy by using more layers in neural network architecture, which heavily rely on computational resources. This paper proposes the refinement of knowledge graphs to perform link prediction operations more accurately using relatively fast translational models. Translational link prediction models have significantly less complexity (faster) than deep learning approaches but are less accurate;this motivated us to improve their accuracy. Our method uses the ontologies of knowledge graphs to add information as auxiliary nodes to the graph. Then, these auxiliary nodes are connected to ordinary nodes of the KG that contain auxiliary information in their hierarchy. Our experiments show that our method can significantly increase the performance of translational link prediction methods in Hit@10, Mean Rank, and Mean Reciprocal Rank, with the same complexity as translational models. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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Sentiment Analysis of Lithuanian Online Reviews Using Large language Models 29

Sentiment Analysis of Lithuanian Online Reviews Using Large ...

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29th International Conference Information Society and University Studies, IVUS 2024

作者： Vileikyte, Brigita Lukoševičius, Mantas Stankevičius, Lukas Kaunas University of Technology Faculty of Informatics StudentuSt. 50 Lithuania

Sentiment analysis is a widely researched area within natural language processing (NLP), attracting significant interest due to the advent of automated solutions. Despite this, the task remains challenging because of the inherent complexity of languages and the subjective nature of sentiments. It is even more challenging for less-studied and lessresourced languages such as Lithuanian. Our review of existing Lithuanian NLP research reveals that traditional machine learning methods and classification algorithms have limited effectiveness for the task. In this work, we address sentiment analysis of Lithuanian five-star-based online reviews from multiple domains that we collect and clean. We apply transformer models to this task for the first time, exploring the capabilities of pre-trained multilingual Large language Models (LLMs), specifically focusing on fine-tuning BERT and T5 models. Given the inherent difficulty of the task, the fine-tuned models perform quite well, especially when the sentiments themselves are less ambiguous: 80.74% and 89.61% testing recognition accuracy of the most popular one- and five-star reviews respectively. They significantly outperform current commercial state-of-the-art general-purpose LLM GPT-4. We openly share our fine-tuned LLMs online. © 2024 CEUR-WS. All rights reserved.

关键词： Deep learning

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Temporal Extrapolation and Knowledge Transfer for Lifelong Temporal Knowledge graph Reasoning

Temporal Extrapolation and Knowledge Transfer for Lifelong T...

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Conference on Empirical methods in natural language processing (EMNLP)

作者： Chen, Zhongwu Xu, Chengjin Su, Fenglong Huang, Zhen Dou, Yong Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Comp Changsha Peoples R China Int Digital Econ Acad IDEA Shenzhen Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9798891760615

Real-world Temporal Knowledge graphs keep growing with time and new entities and facts emerge continually, necessitating a model that can extrapolate to future timestamps and transfer knowledge for new components. Therefore, our work first dives into this more realistic issue, lifelong TKG reasoning, where existing methods can only address part of the challenges. Specifically, we formulate lifelong TKG reasoning as a temporal-path-based reinforcement learning (RL) framework. Then, we add temporal displacement into the action space of RL to extrapolate for the future and further propose a temporal-rule-based reward shaping to guide the training. To transfer and update knowledge, we design a new edge-aware message passing module, where the embeddings of new entities and edges are inductive. We conduct extensive experiments on three newly constructed benchmarks for lifelong TKG reasoning. Experimental results show the outperforming effectiveness of our model against all well-adapted baselines.

关键词： Knowledge graph

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MG-BERT: Multi-graph Augmented BERT for Masked language Modeling 15

MG-BERT: Multi-Graph Augmented BERT for Masked Language Mode...

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

作者： Ghader, Parishad Behnam Zakerinia, Hossein Baghshah, Mahdieh Soleymani Sharif University of Technology Tehran Iran

ISBN: (纸本)9781954085381

Pre-trained models like Bidirectional Encoder Representations from Transformers (BERT), have recently made a big leap forward in natural language processing (NLP) tasks. However, there are still some shortcomings in the Masked language Modeling (MLM) task performed by these models. In this paper, we first introduce a multi-graph including different types of relations between words. Then, we propose Multi-graph augmented BERT (MG-BERT) model that is based on BERT. MG-BERT embeds tokens while taking advantage of a static multi-graph containing global word co-occurrences in the text corpus beside global real-world facts about words in knowledge graphs. The proposed model also employs a dynamic sentence graph to capture local context effectively. Experimental results demonstrate that our model can considerably enhance the performance in the MLM task. © 2021 Association for Computational Linguistics.

关键词： Knowledge graph

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A comprehensive large-scale biomedical knowledge graph for AI-powered data-driven biomedical research

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NATURE MACHINE INTELLIGENCE 2025年第4期7卷 1-13页

作者： Zhang, Yuan Sui, Xin Pan, Feng Yu, Kaixian Li, Keqiao Tian, Shubo Erdengasileng, Arslan Han, Qing Wang, Wanjing Wang, Jianan Wang, Jian Sun, Donghu Chung, Henry Zhou, Jun Zhou, Eric Lee, Ben Zhang, Peili Qiu, Xing Zhao, Tingting Zhang, Jinfeng Florida State Univ Dept Stat Tallahassee FL 32306 USA Insilicom LLC Tallahassee FL 32303 USA Forward Informat Winchester MA USA Univ Rochester Dept Biostat & Computat Biol Rochester NY USA Florida State Univ Dept Geog Tallahassee FL USA

To address the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have become a critical tool for integrating large volumes of heterogeneous data to enable efficient information retrieval and automated knowledge discovery. However, transforming unstructured scientific literature into KGs remains a significant challenge, with previous methods unable to achieve human-level accuracy. Here we used an information extraction pipeline that won first place in the LitCoin natural language processing Challenge (2022) to construct a large-scale KG named iKraph using all PubMed abstracts. The extracted information matches human expert annotations and significantly exceeds the content of manually curated public databases. To enhance the KG's comprehensiveness, we integrated relation data from 40 public databases and relation information inferred from high-throughput genomics data. This KG facilitates rigorous performance evaluation of automated knowledge discovery, which was infeasible in previous studies. We designed an interpretable, probabilistic-based inference method to identify indirect causal relations and applied it to real-time COVID-19 drug repurposing from March 2020 to May 2023. Our method identified around 1,200 candidate drugs in the first 4 months, with one-third of those discovered in the first 2 months later supported by clinical trials or PubMed publications. These outcomes are very challenging to attain through alternative approaches that lack a thorough understanding of the existing literature. A cloud-based platform (https://***) was developed for academic users to access this rich structured data and associated tools.

关键词： Drug discovery

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Leveraging Large language Models (LLMs) as Domain Experts in a Validation Process 40

Leveraging Large Language Models (LLMs) as Domain Experts in...

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40th Annual Conference of the Spanish Association for natural language processing, SEPLN-P 2024

作者： Badenes-Olmedo, Carlos García-Cuesta, Esteban Sánchez-González, Alejandro Corcho, Oscar Ontology Engineering Group Departamento de Sistemas Informáticos Universidad Politécnica de Madrid Spain Ontology Engineering Group Departamento de Inteligencia Artificial Universidad Politécnica de Madrid Spain

The explosion of information requires robust methods to validate knowledge claims. On the other hand, there is also an increase interest on understanding and creating methods that helps on the interpretation of machine learning models. Both approaches converge on the necessity of a validation step that clarifies or helps end-users to better understand if the decision or information provided by the model is what is needed or if there is some mismatch between what the artificial intelligent system is suggesting and reality. Large language Models (LLMs), with their ability to process and synthesize vast amounts of text data, have emerged as potential tools for this purpose. This study explores the utility of LLMs in hypothesis validation in two different scenarios. The first relies on hypothesis generated from explanations obtained by XAI methods or by inherently explainable models. We propose a method to transform the inferences provided by a machine learning model into explanations in natural language, hence linking the symbolic and sub-symbolic areas. The second relies on hypothesis generated with techniques that automatically extract answers from text. The results show that LLMs can complement other XAI techniques and although all LLMs analyzed are able to provide truthfulness-related answers, not all are equally successful. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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Piecewise graph convolutional network with edge-level attention for relation extraction

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NEURAL COMPUTING & APPLICATIONS 2022年第19期34卷 16739-16751页

作者： Yuan, Changsen Huang, Heyan Feng, Chong Cao, Qianwen Beijing Inst Technol Beijing Peoples R China

graph Convolutional Network (GCN) is a critical method to capture non-sequential information of sentences and recognize long-distance syntactic information. However, the adjacency matrix of GCN has two problems: redundant syntactic information and wrong dependency parsing results. Because the syntactic information is represented by unweighted adjacency matrices in most existing GCN methods. Toward this end, we propose a novel model, PGCN-EA, using Piecewise graph Convolutional Network with Edge-level Attention to address these two problems. In specific, we first employ the piecewise adjacency matrix based on entity pair, which aims to dynamically reduce the sentence's redundant features. Second, we propose Edge-level Attention to assign the different weights among nodes based on GCN's input and create the weight adjacency matrix, emphasizing the importance of child words with the target word and alleviating the influence of wrong dependency parsing. Our model on a benchmark dataset has carried out extensive experiments and achieved the best PR curve as compared to seven baseline models, which are at least more than 2:3%.

关键词： natural language processing Information extraction Relation extraction graph convolutional network

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A Clique-based graphical Approach to Detect Interpretable Adjectival Senses in Hungarian 16

A Clique-based Graphical Approach to Detect Interpretable Ad...

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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： Héja, Eniko Ligeti-Nagy, Noémi Hungarian Research Centre for Linguistics Institute of Language Technology and Applies Sciences Language Technology Research Group Hungary

The present paper introduces an ongoing research which aims to detect interpretable adjectival senses from monolingual corpora applying an unsupervised WSI approach. According to our expectations the findings of our investigation are going to contribute to the work of lexicographers, linguists and also facilitate the creation of benchmarks with semantic information for the NLP community. For doing so, we set up four criteria to distinguish between senses. We experiment with a graphical approach to model our criteria and then perform a detailed, linguistically motivated manual evaluation of the results. © 2022 proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Beyond Static Models and Test Sets: Benchmarking the Potential of Pre-trained Models Across Tasks and languages 1

Beyond Static Models and Test Sets: Benchmarking the Potenti...

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1st workshop on Efficient Benchmarking in NLP (NLP Power)

作者： Ahuja, Kabir Dandapat, Sandipan Sitaram, Sunayana Choudhury, Monojit Microsoft Res Bengaluru India Microsoft R&D Hyderabad India

ISBN: (纸本)9781955917476

Although recent Massively Multilingual language Models (MMLMs) like mBERT and XLMR support around 100 languages, most existing multilingual NLP benchmarks provide evaluation data in only a handful of these languages with little linguistic diversity. We argue that this makes the existing practices in multilingual evaluation unreliable and does not provide a full picture of the performance of MMLMs across the linguistic landscape. We propose that the recent work done in Performance Prediction for NLP tasks can serve as a potential solution in fixing benchmarking in Multilingual NLP by utilizing features related to data and language typology to estimate the performance of an MMLM on different languages. We compare performance prediction with translating test data with a case study on four different multilingual datasets, and observe that these methods can provide reliable estimates of the performance that are often on-par with the translation based approaches, without the need for any additional translation as well as evaluation costs.

关键词： natural language processing systems

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