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

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Text Extraction and Mining methods Used in Data Science 2

Text Extraction and Mining Methods Used in Data Science

引用

2nd International conference on Edge Computing and Applications, ICECAA 2023

作者： Deepa, K. Perumal, P. Mathivanan, B. Sri Ramakrishna Engineering College Department of Information Technology Tamil nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil nadu Coimbatore India

ISBN: (纸本)9798350347579

Online Customer Reviews (OCRs) make it difficult for firms to examine them due to their number, diversity, pace, and validity. The big data analytics study predicts OCR reading and its usefulness. Titles with positive emotion and sentimental reviews with neutral polarity attract more readers. Online merchants may use this work to build scale automated processes for sorting and categorizing huge OCR data, benefiting vendors and consumers. Current OCR sorting approaches may prejudice readership and usefulness. Python crawled, processed, and displayed data using natural language processing (NLP). The crawling dataset collected literature using a Pubmed Application Programming Interface (API) module. natural language Toolkit (NLTK) processed text data. Tokens were processed into bigrams and trigrams using n-grams. According to study abstracts, West Java has the most stunting research. Text mining and NLP may enhance oral history and historical archaeology. Text mining algorithms were intended for enormous data and public texts, making them inappropriate for historical and archaeological interpretation. Text analysis can effectively handle and evaluate vast amounts of data, which may substantially enrich historical archaeology study, especially when dealing with digital data banks or extensive texts. © 2023 IEEE.

关键词： Application programming interfaces (API)

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News that Moves the Market: DSEX-News Dataset for Forecasting DSE Using BERT 16th

News that Moves the Market: DSEX-News Dataset for Forecastin...

引用

16th Asian conference on Intelligent Information and Database Systems (ACIIDS)

作者： Khan, Md Nabil Rahman Salsabil, Most Sadia Hasib, Khan Md Islam, Md Rafiqul Alam, Mohammad Shafiul Sanin, Cesar Szczerbicki, Edward Ahsanullah Univ Sci & Technol Dept Comp Sci & Engn Dhaka Bangladesh Bangladesh Univ Business & Technol Dept Comp Sci & Engn Dhaka 1216 Bangladesh Australian Inst Higher Educ Business Informat Syst Sydney NSW Australia Gdansk Univ Technol Fac Management & Econ Gdansk Poland

ISBN: (纸本)9789819759330;9789819759347

Stock market is a complex and dynamic industry that has always presented challenges for stakeholders and investors due to its unpredictable nature. This unpredictability motivates the need for more accurate prediction models. Traditional prediction models have limitations in handling the dynamic nature of the stock market. Additionally, previous methods have used less relevant data, leading to suboptimal performance. This study proposes the use of Bidirectional Encoder Representations from Transformers (BERT), a pre-trained Large language Model (LLM), to predict Dhaka Stock Exchange (DSE) market movements. We also introduce a new dataset designed specifically for this problem, capturing important characteristics and patterns that were missing in other datasets. We test our new dataset of headlines and stock market indexes on various machine learning techniques, including Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Linear Support Vector Machine (LSVM), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Bidirectional Long Short-Term Memory (Bi-LSTM), BERT, Financial Bidirectional Encoder Representations from Transformers (FinBERT), and RoBERTa, which are compared to assess their predictive capabilities. Our proposed model achieves 99.83% accuracy on the training set and 99.78% accuracy on the test set, outperforming previous methods.

关键词： Deep Learning natural language processing Sentiment Analysis Machine Learning Stock Exchange Large language Model

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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...

引用

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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Construction of Part of Speech Tagger for Malay language: A Review

Construction of Part of Speech Tagger for Malay Language: A ...

引用

natural language processing (ICNLP), International conference on

作者： Nurulhuda Mohamad Ali Goh Hui Ngo Amy Lim Hui Lan Department Faculty of Computing and Informatics (FCI) Multimedia University Cyberjaya Malaysia

Part-of-Speech (POS) Tagging is one of the fundamental tasks in natural language processing (NLP) in analyzing human languages. It is a process of identifying how words are used in a sentence by assigning the proper POS for each word. Thus far, most well-researched POS tagging is on European languages which are considered rich-resource languages due to the unlimited linguistic resources such as research studies and large standard corpus. However, POS tagging is arduous for low-resource languages due to the limitation of linguistic resources. The Malay language is considered as a low-resource language. Most POS tagging studies for the Malay language are using rule-based and stochastic methods. However, exploration in Deep Learning (DL) for Malay language is limited. Thus, studies with POS tagging methods that implement DL for other low-resource languages within South East Asia are included in this study. Hence, the aim of this study is to identify the state of the art, challenges, and future works of Malay POS tagger. This study provides a review of different methods, datasets, and performance measures used in POS tagging studies.

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Recent methods and Algorithms in Speech Segmentation Tasks 16th

Recent Methods and Algorithms in Speech Segmentation Tasks

引用

16th International conference on Computational Collective Intelligence (ICCCI)

作者： Oralbekova, Dina Mamyrbayev, Orken Kurmetkan, Turdybek Zhumazhan, Nurdaulet Inst Informat & Computat Technol Alma Ata Kazakhstan Inst Informat Secur Problems Alma Ata Kazakhstan Almaty Univ Power Engn & Telecommun Alma Ata Kazakhstan Al Farabi Kazakh Natl Univ Alma Ata Kazakhstan U Joldasbekov Inst Mech & Engn Alma Ata Kazakhstan

ISBN: (纸本)9783031702587;9783031702594

The article addresses challenges in human-computer interaction through natural language, particularly in the context of collaborative conversations. The issue of overlapping audio data affects the accuracy of speech recognition and synthesis systems, especially in scenarios like meetings and negotiations. Emphasis is placed on the need for segregating and clustering speech from multiple speakers, highlighting challenges arising from diverse sound conditions in everyday life. The article then delves into the task of diarization, underscoring the importance of segmenting and processing speech data for effective voice control of devices. Subsequently, it explores the combination of GMM and i-vectors, as well as the evolution of approaches using deep learning, including convolutional and recurrent neural networks. Considering recent trends, the authors analyze the application of Transformers for handling long-term dependencies in data. The concluding section of the article provides a comprehensive overview and analysis of contemporary diarization methods, encompassing algorithms, error evaluation metrics, and descriptions of popular tools, with a focus on more modern approaches. This work constitutes a significant contribution to the field of speech diarization research, covering more current methods and trends compared to previous reviews in this domain.

关键词： Diarization Speech Recognition Speaker Separation Segmentation Speaker Clustering

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Vision, Deduction and Alignment: An empirical Study on Multi-Modal Knowledge Graph Alignment 48

Vision, Deduction and Alignment: An Empirical Study on Multi...

引用

48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Yangning Chen, Jiaoyan Li, Yinghui Xiang, Yuejia Chen, Xi Zheng, Hai-Tao Tsinghua University Shenzhen International Graduate School China Peng Cheng Laboratory China The University of Manchester Department of Computer Science United Kingdom Tencent China

ISBN: (纸本)9781728163277

Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering. Existing EA methods mostly focus on utilizing the graph structures and entity attributes (including literals), but ignore images that are common in modern multi-modal KGs. In this study we first constructed Multi-OpenEA - eight large-scale, image-equipped EA benchmarks, and then evaluated some existing embedding-based methods for utilizing images. In view of the complementary nature of visual modal information and logical deduction, we further developed a new multi-modal EA method named LODEME using logical deduction and multi-modal KG embedding, with state-of-the-art performance achieved on Multi-OpenEA and other existing multi-modal EA benchmarks. © 2023 IEEE.

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Desiderata for the Context Use of Question Answering Systems 18

Desiderata for the Context Use of Question Answering Systems

引用

18th conference of the European-Chapter of the Association-for-Computational-Linguistics (EACL)

作者： Shaier, Sagi Hunter, Lawrence E. von der Wense, Katharina Univ Colorado Boulder CO 80309 USA Johannes Gutenberg Univ Mainz Mainz Germany

ISBN: (纸本)9798891760882

Prior work has uncovered a set of common problems in state-of-the-art context-based question answering (QA) systems: a lack of attention to the context when the latter conflicts with a model's parametric knowledge, little robustness to noise, and a lack of consistency with their answers. However, most prior work focus on one or two of those problems in isolation, which makes it difficult to see trends across them. We aim to close this gap, by first outlining a set of - previously discussed as well as novel - desiderata for QA models. We then survey relevant analysis and methods papers to provide an overview of the state of the field. The second part of our work presents experiments where we evaluate 15 QA systems on 5 datasets according to all desiderata at once. We find many novel trends, including (1) systems that are less susceptible to noise are not necessarily more consistent with their answers when given irrelevant context;(2) most systems that are more susceptible to noise are more likely to correctly answer according to a context that conflicts with their parametric knowledge;and (3) the combination of conflicting knowledge and noise can reduce system performance by up to 96%. As such, our desiderata help increase our understanding of how these models work and reveal potential avenues for improvements. Code and data can be found here: https://***/Shaier/context_usage_***.

关键词： natural language processing systems

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A Comparative Analysis of Depression Detection with and without natural language processing

A Comparative Analysis of Depression Detection with and with...

引用

International conference on ent Computing and Control Systems (ICICCS)

作者： L.Mary Gladence Chukka Saipooja Pranathi Naman Raj Merlin Mary Jenitha School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

In the recent times, intensifying struggles for sustenance and education, have sharply escalated competitive tensions among individuals, leaving many students especially the adolescents in prolonged states of stress and apprehension, due to the lack of proper guidance, trauma or other aspects leading to a significant surge in mental health issues. The recent advancement in social media like Instagram, Facebook, Twitter, etc. has made the communication easy but also has become something that is offering these teenagers a fresh opportunity or method to release or express their emotions which they might otherwise keep to themselves. This research focuses on analyzing adolescents' Twitter posts for detection of depression. Textual data will be harvested from these posts and transformed into input suitable for machine learning algorithms. Different forms of machine learning algorithms are going to be experimented upon, using and not using NLP— natural language processing, to design a system for the recognition of patterns of depression in individuals.

关键词： Support vector machines Analytical models Machine learning algorithms Social networking (online) Blogs Mental health Nearest neighbor methods Depression natural language processing Web sites

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LogPS: A Robust Log Sequential Anomaly Detection Approach Based on natural language processing 22

LogPS: A Robust Log Sequential Anomaly Detection Approach Ba...

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22nd IEEE International conference on Communication Technology, ICCT 2022

作者： Li, Dongjiang Zhang, Jing Zhang, Xianbo Lin, Feng Wang, Chao Chang, Liang Jd Tech Beijing China

ISBN: (纸本)9781665470674

System logs are widely used by engineers to record runtime status in the information technology (IT) field. The sequential anomaly detection of logs is crucial for building a secure and stable system and is beneficial for the discovery, location, and analysis of system failures. Conventional manual log anomaly detection suffers high costs and unsustainable development. Thus, automatic methods based on natural language processing (NLP) technology are proposed to improve the accuracy and efficiency of log anomaly detection. In this paper, we propose a new log anomaly detection model, named LogPS. LogPS utilizes the Part-of-Speech (PoS) technique to extract semantic information from log messages. By allocating the learned PoS-based weights to different tokens in a log template, LogPS can improve the representation quality of the log template vector. In the final anomaly detection stage, we treat a system log as a natural language sequence and build a Bidirectional Long Short-Term Memory (BiLSTM) neural network as the LogPS detection model. Therefore, LogPS can capture sufficient and contextual information from input log sequences from the forward pass and the backward pass. And LogPS can automatically learn log patterns and detect anomalies. The effectiveness of our model is tested on three datasets and is compared with other state-of-the-art models. The experimental results show that, compared with other log anomaly detection methods, the proposed LogPS performs well. © 2022 IEEE.

关键词： Anomaly detection

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The Sentiment Attitude of Weibo Users towards Annual Individual Income Tax Return: Based on natural language processing and Machine Learning methods 6

The Sentiment Attitude of Weibo Users towards Annual Individ...

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6th International conference on Big Data and Artificial Intelligence, BDAI 2023

作者： Chen, Yuanyan Liu, Wei Capital University of Economics and Business School of Public Finance and Taxation BeiJing China

ISBN: (纸本)9798350303667

This passage discusses a study that collected Weibo posts from different users related to China's Annual Individual Income Tax Return(AIITR), and compared the performance of six machine learning models (support vector machine, random forest, naive Bayes, K-nearest neighbor, decision tree, and logistic regression), as well as a deep learning LSTM model, to choose the more accurate LSTM model for sentiment analysis. The study aims to explore the compliance status of individuals in tax-paying and uses the Latent Direct Allocation (LDA) model to display the topics discussed in the Weibo posts, in order to gain a deeper understanding of the issues that Weibo users are concerned about related to AIITR, and provide policy recommendations. © 2023 IEEE.

关键词： Taxation

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