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检索条件"任意字段=Conference on empirical methods in natural language processing"

共 15258 条记录，以下是4721-4730 订阅

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Fine-Tuning Pre-Trained language Models Effectively by Optimizing Subnetworks Adaptively 36

Fine-Tuning Pre-Trained Language Models Effectively by Optim...

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Zhang, Haojie Li, Ge Li, Jia Zhang, Zhongjin Zhu, Yuqi Jin, Zhi Peking Univ Key Lab High Confidence Software Technol Minist Educ Inst SoftwareEECS Beijing Peoples R China

ISBN: (纸本)9781713871088

Large-scale pre-trained language models have achieved impressive results on a wide range of downstream tasks recently. However, fine-tuning an extremely large-scale pre-trained language model on limited target datasets is often plagued by overfitting and representation degradation. In this paper, we propose a Dynamic Parameter Selection (DPS) algorithm for the large-scale pre-trained models during fine-tuning, which adaptively selects a more promising subnetwork to perform staging updates based on gradients of back-propagation. Experiments on the GLUE benchmark show that DPS outperforms previous fine-tuning methods in terms of overall performance and stability, and consistently achieves better results with variable pre-trained language models. In addition, DPS brings a large magnitude of improvement in out-of-domain transferring experiments and low-resource scenarios, which shows that it can maintain stable general contextual features and reduce the representation collapse. We release our code at https://***/ZhangHaojie077/DPS.

关键词： Backpropagation

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Electric Power System Text Information Mining and Analysis Algorithm Combined with natural language processing

Electric Power System Text Information Mining and Analysis A...

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Telecommunications and Power Electronics (TELEPE), International conference on

作者： Runze Ma Fei Yang Fang Dong Huadong Engineering Co. Ltd Hangzhou China

ISBN: (数字)9798350369212

ISBN: (纸本)9798350380002

In EPS, the text data such as equipment fault records, operation and maintenance logs and maintenance reports contain abundant equipment status information. By using natural language processing (NLP) technology, a large number of text data in electric power system (EPS) can be automatically processed, thus extracting information related to the health status of equipment. The purpose of this article is to explore and design an EPS text information mining and analysis algorithm combined with NLP technology. Aiming at the limitations of traditional methods in EPS text information processing, this article proposes a novel algorithm to improve the accuracy of information extraction and processing efficiency. Methodologically, the algorithm integrates NLP, feature extraction and text classification, and realizes the deep mining of EPS text data. The results show that, compared with traditional methods, the proposed algorithm shows higher accuracy in text classification and information extraction, and significantly improves the efficiency of data processing. The algorithm provides strong technical support for EPS operation and maintenance management and fault prediction, and promotes the intelligent development of EPS.

关键词： Accuracy Text categorization Manuals Prediction algorithms Feature extraction natural language processing Classification algorithms Power systems Maintenance Data mining

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Enhancing Efficiency and Conservation via Speech processing in Lutyens's Delhi Residential Revitalization 26

Enhancing Efficiency and Conservation via Speech Processing ...

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26th conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques, O-COCOSDA 2023

作者： Sharma, Ar. Shweta Ashtt, Rashmi Wankar, Monali Indira Gandhi Delhi Technical University Women Department of Architecture and Planning Delhi India

ISBN: (纸本)9798350344028

This paper explores the intricate challenge of restoring historically significant urban areas, striking a delicate balance between cultural preservation, efficiency, and conservation, while adapting to a changing world. Our focus is on Lutyens's Delhi, a region renowned for its architectural grandeur but grappling with energy consumption, environmental sustainability, and public health *** propose a unique solution, leveraging speech processing technologies, to aid in the revitalization of residential districts within Lutyens's Delhi. This study investigates the potential of speech processing to enhance efficiency and conservation while preserving the city's distinctive character. We examine the impact of urban revitalization, historical preservation, and the practical applications of voice recognition and natural language processing, drawing insights from relevant *** research employs a mixed-methods approach, encompassing interviews, surveys, and data analysis of architectural plans and historical documents. We evaluate the ability of voice recognition systems to optimize energy consumption in residential areas and utilize natural language processing to glean valuable insights from resident feedback, facilitating informed decision-making during *** findings underscore the tangible benefits of integrating speech processing technologies. These tools enhance our understanding of resident preferences and concerns, fostering a more sustainable and culturally sensitive approach to urban development. Significant improvements in energy efficiency are also observed. Moreover, we address ethical considerations related to privacy, ensuring the rights of residents are upheld while harnessing the advantages of speech ***'s Delhi, an iconic urban area, grapples with the intricate task of preserving its cultural heritage while embracing modernity and sustainability. Our research contributes to the discourse on urban revitalization

关键词： Sustainable development

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Back to the Future: Towards Explainable Temporal Reasoning with Large language Models 24

Back to the Future: Towards Explainable Temporal Reasoning w...

引用

33rd ACM Web conference, WWW 2024

作者： Yuan, Chenhan Xie, Qianqian Huang, Jimin Ananiadou, Sophia The University of Manchester Manchester United Kingdom Wuhan University Wuhan China

ISBN: (纸本)9798400701719

Temporal reasoning is a crucial natural language processing (NLP) task, providing a nuanced understanding of time-sensitive contexts within textual data. Although recent advancements in Large language Models (LLMs) have demonstrated their potential in temporal reasoning, the predominant focus has been on tasks such as temporal expression detection, normalization, and temporal relation extraction. These tasks are primarily designed for the extraction of direct and past temporal cues from given contexts and to engage in simple reasoning processes. A significant gap remains when considering complex reasoning tasks such as event forecasting, which requires multi-step temporal reasoning on events and prediction on the future timestamp. Another notable limitation of existing methods is their incapability to illustrate their reasoning process for explaining their prediction, hindering explainability. In this paper, we introduce the first task of explainable temporal reasoning, to predict an event's occurrence at a future timestamp based on context which requires multiple reasoning over multiple events, and subsequently provide a clear explanation for their prediction. Our task offers a comprehensive evaluation of both the LLMs' complex temporal reasoning ability, the future event prediction ability, and explainability-a critical attribute for AI applications. To support this task, we present the first instruction-tuning dataset of explainable temporal reasoning (ExpTime) with 26k derived from the temporal knowledge graph datasets, using a novel knowledge-graph-instructed-generation strategy. Based on the dataset, we propose the first open-source LLM series TimeLlaMA based on the foundation LLM LlaMA2, with the ability of instruction following for explainable temporal reasoning. We compare the performance of our method and a variety of LLMs, where our method achieves the state-of-the-art performance of temporal prediction and explanation generation. We also explore the impa

关键词： Forecasting

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A Review Study for Arabic Machine Learning and Deep Learning methods

A Review Study for Arabic Machine Learning and Deep Learning...

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2022 ASU International conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Al-Shalabi, Riyad Kanaan, Ghassan Kanan, Tarek Elbes, Mohammed Applied Science University-Bahrain Bahrain International Center for Scientific Research and Studies Jordan Al Zaytoonah University of Jordan-Jordan Jordan

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

The fields of natural language processing (NLP), Computer vision and speech processing witnessed great breakthroughs caused by the continuous advances in Deep Learning (DL). The implementation of DL techniques in Online Social Networking and Sentiment Analysis have proved to provide a state-of-the-art result in these areas. In this research, we survey the papers that claims to use the DL methods in NLP. We focus on the research that is related to the Arabic language due to the scares resources in Arabic NLP. We concluded that most of the early research in Arabic NLP focusses of OCR-Digitization and most recently is focusing on applying DL methods in Sentiment Analysis, diacrization and machine translation. We present this survey to provide the growing community of researchers in ANLP in the hope of bridging the gap between the ANLP and the English NLP. © 2022 IEEE.

关键词： Sentiment analysis

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Evaluation of Basic Modules for Isolated Spelling Error Correction in Polish Texts 9th

Evaluation of Basic Modules for Isolated Spelling Error Corr...

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9th language and Technology conference (LTC)

作者： Rutkowski, Szymon Univ Warsaw Krakowskie Przedmiescie 26-28 PL-00927 Warsaw Poland

ISBN: (纸本)9783031053283;9783031053276

Spelling error correction is an important problem in natural language processing, as a prerequisite for good performance in downstream tasks as well as an important feature in user-facing applications. For texts in Polish language, there exist works on specific error correction solutions, often developed for dealing with specialized corpora, but not evaluations of many different approaches on big resources of errors. We begin to address this problem by testing some basic and promising methods on PlEWi, a corpus of annotated spelling extracted from Polish Wikipedia. We focus on isolated correction (without context) of non-word errors (ones producing forms that are out-of-vocabulary). The modules may be further combined with appropriate solutions for error detection and context awareness. Following our results, combining edit distance with cosine distance of semantic vectors may be suggested for interpretable systems, while an LSTM network, particularly enhanced by contextualized character embeddings such as ELMo, seems to offer the best raw performance.

关键词： Spelling correction center dot Polish language ELMo

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Clinical Text Classification in Cancer Real-World Data in Spanish 10th

Clinical Text Classification in Cancer Real-World Data in Sp...

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10th International Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Moreno-Barea, Francisco J. Mesa, Hector Ribelles, Nuria Alba, Emilio Jerez, Jose M. Univ Malaga Escuela Tecn Super Ingn Informat Dept Lenguajes & Ciencias Computac Malaga Spain Hosp Univ Reg & Virgen de la Victoria Unidad Gest Clin Interctr Oncol Malaga Spain

ISBN: (纸本)9783031349522;9783031349539

Healthcare systems currently store a large amount of clinical data, mostly unstructured textual information, such as electronic health records (EHRs). Manually extracting valuable information from these documents is costly for healthcare professionals. For example, when a patient first arrives at an oncology clinical analysis unit, clinical staff must extract information about the type of neoplasm in order to assign the appropriate clinical specialist. Automating this task is equivalent to text classification in natural language processing (NLP). In this study, we have attempted to extract the neoplasm type by processing Spanish clinical documents. A private corpus of 23, 704 real clinical cases has been processed to extract the three most common types of neoplasms in the Spanish territory: breast, lung and colorectal neoplasms. We have developed methodologies based on state-of-the-art text classification task, strategies based on machine learning and bag-of-words, based on embedding models in a supervised task, and based on bidirectional recurrent neural networks with convolutional layers (C-BiRNN). The results obtained show that the application of NLP methods is extremely helpful in performing the task of neoplasm type extraction. In particular, the 2-BiGRU model with convolutional layer and pre-trained fastText embedding obtained the best performance, with a macro-average, more representative than the micro-average due to the unbalanced data, of 0.981 for precision, 0.984 for recall and 0.982 for F1-score.

关键词： Text Classification natural language processing Electronic Health Records Neoplasm cancer Spanish

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EcoDialTest: Adaptive Mutation Schedule for Automated Dialogue Systems Testing 30

EcoDialTest: Adaptive Mutation Schedule for Automated Dialog...

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30th IEEE International conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Shen, Xiangchen Chen, Haibo Chen, Jinfu Zhang, Jiawei Wang, Shuhui Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Peoples R China

ISBN: (纸本)9781665452786

With the rapid growth of Artificial Intelligence, dialogue systems have become increasingly powerful. Though Recurrent Neural Network power the dialogue systems, it also bring challenges to the systems' testing. In order to ensure the safety of these systems, which we must pay attention to, DialTest showed up. DialTest broke the traditional test methods, it made innovation at many levels. We have to acknowledge this great contribution. However, DialTest has a smattering of shortcomings. It treats all seeds as equal, implying that it cannot adjust the energy assignment quickly, resulting in energy waste. Moreover, DialTest's mutant sentences generated by a few original seed sentences in the late stage of variation. This paper presents an improved DialTest with an adaptive mutation schedule, we called it EcoDialTest. EcoDialTest divides all the seed into three states, different states have different energy distribution strategies. We devise a new mutation strategy to improve the effectiveness and dependability of the seeds in the transformed seed set. All of these were implemented based on DialTest, we still adopt DeepGini impurity as the main guidance to guide the test generation process and utilize the three mutation operators as it does. Through ATIS, Snips and Facebook datasets, EcoDialTest was evaluated by two state-of-the-art models in the experiment. According to the result, we found that EcoDialTest attained lower values in both intent accuracy and slot accuracy than DialTest.

关键词： dialogue system testing fuzzing deep learning testing natural language processing

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Automated Rule-Based Data Cleaning Using NLP 32

Automated Rule-Based Data Cleaning Using NLP

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32nd conference of Open-Innovations-Association-FRUCT

作者： Mavrogiorgos, Konstantinos Mavrogiorgou, Argyro Kiourtis, Athanasios Zafeiropoulos, Nikolaos Kleftakis, Spyridon Kyriazis, Dimosthenis Univ Piraeus Piraeus Greece

ISBN: (数字)9789526924489

ISBN: (纸本)9789526924489

Data Cleaning is a subfield of Data Mining that is thriving in the recent years. Ensuring the reliability of data, either when generated or received, is of vital importance to provide the best services possible to users. Accomplishing the aforementioned task is easier said than done, since data are complex, generated at an extremely high rate and are of enormous size. A variety of techniques and methods that are part of other subfields from the domain of the Computer Science have been invoked to assist in making Data Cleaning the most efficient and effective possible. Those suhfields include, among others, natural language processing (NIP), which in essence refers to the interaction among computers and human language, seeking to find a way to program computers to be able to process and analyze huge volumes of human language data. NLP is a concept that exists for a long time, but, as time goes by, it is proposed that it can be applied to a variety of concepts that are not solely NLP-related. In this paper, a rule-based data cleaning mechanism is proposed, which utilizes NLP to ensure data reliability. Making use of NLP enabled the mechanism not only to be extremely effective but also to be a lot more efficient compared to other corresponding mechanisms that do not utilize NLP. The mechanism was evaluated upon diverse healthcare datasets, not however being limited to the healthcare domain, but supporting a generalized data cleaning concept.

关键词： Computer science Data privacy Technological innovation Transportation Medical services Cleaning natural language processing

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GraphERT- Transformers-based Temporal Dynamic Graph Embedding 23

GraphERT- Transformers-based Temporal Dynamic Graph Embeddin...

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32nd ACM International conference on Information and Knowledge Management (CIKM)

作者： Beladev, Moran Katz, Gilad Rokach, Lior Singer, Uriel Radinsky, Kira Ben Gurion Univ Negev Beer Sheva Israel Technion Haifa Israel

ISBN: (纸本)9798400701245

Dynamic temporal graphs evolve over time, adding and removing nodes and edges between time snapshots. The tasks performed on such graphs are diverse and include detecting temporal trends, finding graph-to-graph similarities, and graph visualization and clustering. For all these tasks, it is necessary to embed the entire graph in a low-dimensional space by using graph-level representations instead of the more common node-level representations. This embedding requires handling the appearance of new nodes over time as well as capturing temporal patterns of the entire graph. Most existing methods perform temporal node embeddings and focus on different methods of aggregating them for a graph-based representation. In this work, we propose an end-to-end architecture that captures both the node embeddings and their influence in a structural context during a specific time period of the graph. We present GraphERT (Graph Embedding Representation using Transformers), a novel approach to temporal graph-level embeddings. Our method pioneers the use of Transformers to seamlessly integrate graph structure learning with temporal analysis. By employing a masked language model on sequences of graph random walks, together with a novel temporal classification task, our model not only comprehends the intricate graph dynamics but also unravels the temporal significance of each node and path. This novel training paradigm empowers GraphERT to capture the essence of both the structural and temporal aspects of graphs, surpassing state-of-the-art approaches across multiple tasks on real-world datasets.

关键词： graph neural networks temporal graph embedding social networks natural language processing time-series anomaly detection

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