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

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Efficient Unicode Ordinal Values for Text Embedding with FastText and Word2Vec

Efficient Unicode Ordinal Values for Text Embedding with Fas...

引用

International Joint Symposium on Artificial Intelligence and natural language processing (iSAI-NLP)

作者： Agnij Moitra Amity International School Saket Delhi India

ISBN: (数字)9798331509910

ISBN: (纸本)9798331509927

Accurately representing categorical data is crucial for enhancing machine learning model performance, especially when dealing with ordinal data, where the order of categories is significant. This work provides a novel use of ordinal encoding as an alternative to traditional one-hot encoding in word embedding methods. We highlight key theoretical results that demonstrate the benefits of ordinal encoding: improved model interpretability, robustness to noise, and effective out-of-vocabulary handling. Through a series of theorems and empirical benchmarks on publicly available datasets, we show that ordinal encoding can significantly enhance predictive accuracy in text classification tasks. Our findings underscore the importance of preserving ordinal relationships in categorical data and position ordinal encoding as a vital methodology for practitioners aiming for optimal performance. This contribution enriches the ongoing discussion in data representation, offering insights into using FastText and Word2Vec with ordinal values instead of one-hot-encoded vectors.

关键词： Measurement Accuracy Reviews Text categorization Noise Transformers Encoding Vectors Robustness natural language processing

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Pre-training Fine-tuning data Enhancement method based on active learning 21

Pre-training Fine-tuning data Enhancement method based on ac...

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21st IEEE International conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Cao, Deqi Ding, Zhaoyun Wang, Fei Ma, Haoyang Natl Univ Def Technol Key Lab Informat Syst Engn Changsha Peoples R China

ISBN: (纸本)9781665494250

With the development of Internet technology, the number of Internet users increases rapidly, and the amount of data generated on the Internet is very large every day. At the same time, with the development of storage technology and query technology, it is very easy to collect massive data, but the information value contained in these data is uneven, and most of them are unmarked. However, traditional supervised learning has a great demand for labeled samples. Faced with a large number of unlabeled samples, there is a problem of the lack of effective automatic labeling methods, and manual labeling costs are high. If the strategy of simple random sampling is used for annotation, it may lead to the selection of noisy information and waste of resources, and low-quality training data could also have an influence on the prediction accuracy of the model. Meanwhile, the training effect of traditional deep learning methods is very limited for small sample labeled training sets. This paper takes the text emotion analysis task in natural language processing as the background, selects IMDB film review data as the training set and test set, starts with the design of active learning algorithm based on clustering analysis, combined with the appropriate pre-training fine-tuning model, constructs a data enhancement method based on active learning. In the experiment, it is found that when the labeled training set is reduced by 90%, the prediction accuracy of the pre-training model is reduced by no more than 2%, which verifies the effectiveness of the data enhancement method combining active learning with the pretraining model.

关键词： Active learning Clustering analysis Pretraining model natural language processing

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Classifying Cyber-Risky Clinical Notes by Employing natural language processing 55

Classifying Cyber-Risky Clinical Notes by Employing Natural ...

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55th Annual Hawaii International conference on System Sciences (HICSS)

作者： Schmeelk, Suzanna Dogo, Martins Samuel Peng, Yifan Patra, Braja Gopal St Johns Univ Jamaica NY 11439 USA Queens Univ Belfast Belfast Antrim North Ireland Weill Cornell Med Dept Populat Hlth Sci New York NY USA

ISBN: (纸本)9780998133157

Clinical notes, which can be embedded into electronic medical records, document patient care delivery and summarize interactions between healthcare providers and patients. These clinical notes directly inform patient care and can also indirectly inform research and quality/safety metrics, among other indirect metrics. Recently, some states within the United States of America require patients to have open access to their clinical notes to improve the exchange of patient information for patient care. Thus, developing methods to assess the cyber risks of clinical notes before sharing and exchanging data is critical. While existing natural language processing techniques are geared to de-identify clinical notes, to the best of our knowledge, few have focused on classifying sensitive-information risk, which is a fundamental step toward developing effective, widespread protection of patient health information. To bridge this gap, this research investigates methods for identifying security/privacy risks within clinical notes. The classification either can be used upstream to identify areas within notes that likely contain sensitive information or downstream to improve the identification of clinical notes that have not been entirely de-identified. We develop several models using unigram and word2vec features with different classifiers to categorize sentence risk. Experiments on i2b2 de-identification dataset show that the SVM classifier using word2vec features obtained a maximum F1-score of 0.792. Future research involves articulation and differentiation of risk in terms of different global regulatory requirements.

关键词： Classification (of information)

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Example Driven Code Review Explanation 22

Example Driven Code Review Explanation

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16th ACM/IEEE International Symposium on empirical Software Engineering and Measurement (ESEM)

作者： Rahman, Shadikur Koana, Umme Ayman Nayebi, Maleknaz York Univ Toronto ON Canada

ISBN: (纸本)9781450394277

Background: Code reviewing is an essential part of software development to ensure software quality. However, the abundance of review tasks and the intensity of the workload for reviewers negatively impact the quality of the reviews. The short review text is often unactionable. Aims: We propose the Example Driven Review Explanation (EDRE) method to facilitate the code review process by adding additional explanations through examples. EDRE recommends similar code reviews as examples to further explain a review and help a developer to understand the received reviews with less communication overhead. Method: Through an empirical study in an industrial setting and by analyzing 3,722 Code reviews across three open-source projects, we compared five methods of data retrieval, text classification, and text recommendation. Results: EDRE using TF-IDF word embedding along with an SVM classifier can provide practical examples for each code review with 92% F-score and 90% Accuracy. Conclusions: The example-based explanation is an established method for assisting experts in explaining decisions. EDRE can accurately provide a set of context-specific examples to facilitate the code review process in software teams.

关键词： Code Review Decision Explanation Software Engineering natural language processing (NLP)

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E-Assessment of Question-Answering Scoring Using Semantic Text Matching In Arabic language

E-Assessment of Question-Answering Scoring Using Semantic Te...

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Emerging Technologies for Dependable Internet of Things (ICETI), International conference on

作者： Nooruddine F. Assarwie Mohammed Faris Aldhafer Yousef Khaled Ahmed Information Technology University of Science and Technology Ibb Yemen

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Smart technologies are vital for progressions in text processing. They support the enhancement of academic activities' efficiency. For e-assessment, they provide researchers and educators with tools for objective content evaluation and scoring. As demand for quality learning experiences increases, such technologies offer solutions to enrich education and expand understanding of course materials. Automated question-answering scoring (QAS) addresses the challenges of manual grading, which is labor-intensive and prone to mistakes. By integrating computer systems with natural language processing (NLP) and intelligent algorithms, student answers can be analyzed objectively, delivering accurate feedback to help improve skills. In this study, we have employed WordNet and TF-IDF for QAS tasks, based on text similarity matching methods. Text pre-processing techniques are utilized for reliable computational analysis of the Arabic language. This study combines two established methods in linguistic computing and text processing to automate the assessment of the question-answering method. Using a dataset of questions, teachers' templates, and students' answers. The results exhibited the distinction of WordNet over TF-IDF based on three evaluation metrics.

关键词： Measurement Accuracy Error analysis Semantics Knowledge based systems Linguistics natural language processing Reliability Internet of Things Text processing

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Read, Spell and Repeat: Scene Text Recognition with Vision-language Circular Refinement

Read, Spell and Repeat: Scene Text Recognition with Vision-L...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Taiwei Zhang Zhenghui Hu Weixin Li Qingjie Liu Yunhong Wang Hangzhou Innovation Institute Beihang University Hangzhou China Shanghai Artificial Intelligence Laboratory Shanghai China School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

Scene Text Recognition (STR) has long been considered an important yet challenging task in the field of computer vision. Recent works have demonstrated that utilizing language information is effective for the visually difficult images, like ones with occultation or blurring. However, the use of language information sometimes leads to the over-correction problem. For out-of-vocabulary samples (e.g. "hou" and "0x4a"), some methods have tended to be biased to language side and over-corrected (e.g. over-correct "hou" to "hot"). This imbalance of vision and language has limited the usage of models in practical scenarios, yet it is rarely occurs for human. To address this issue, we rethink the human’s recognition process and propose a model behaving in the order of "Read, Spell and Repeat". It refines the recognition process circularly with vision and language information. With this mechanism, our model integrates vision and language information in a more effective manner, achieving higher accuracy with less parameters compared to baseline and competitive performance with SOTA methods in the standard benchmarks.

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Beyond Audio Signals: Generative Model-Based Speaker Diarization in Portuguese 34th

Beyond Audio Signals: Generative Model-Based Speaker Diariza...

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34th Brazilian conference on Intelligent Systems

作者： Boll, Antonio Oss Puttlitz, Leticia Maria Boll, Heloisa Oss Malossi, Rodrigo Mor Univ Sao Paulo Inst Math & Stat Rua Matao BR-05508090 Sao Paulo SP Brazil Univ Fed Rio Grande do Sul Inst Informat Ave Bento Goncalves 9500 BR-91501970 Porto Alegre RS Brazil

ISBN: (纸本)9783031790287;9783031790294

Speaker diarization, the task of automatically identifying different speakers in audio and video, is frequently performed using probabilistic models and deep learning techniques. However, existing methods usually rely on direct analysis of the audio signal, which presents challenges for languages that lack established diarization methodologies, such as Portuguese. In this article, we propose a new approach to speaker diarization that leverages generative models for automatic speaker identification in Portuguese. We employed two generative models: one for refining the transcribed audio and another for performing the diarization task, as well as a model for initially transcribing the audio. Our method simplifies the diarization process by capturing and analyzing speaker style patterns from transcribed audio and achieves high accuracy without depending on direct signal analysis. This approach not only increases the effectiveness of speaker identification but also extends the usefulness of generative models to new domains. It opens a new perspective for diarization research, especially for the development of accurate systems for under-researched languages in audio and video applications.

关键词： Speaker Diarization Generative Models natural language processing

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Coherentice: Invertible Concept-Based Explainability Framework for CNNs beyond Fidelity

Coherentice: Invertible Concept-Based Explainability Framewo...

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IEEE International conference on Multimedia and Expo (ICME)

作者： Ugochukwu Ejike Akpudo Yongsheng Gao Jun Zhou Andrew Lewis

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In their natural form, convolutional neural networks (CNNs) lack interpretability despite their effectiveness in visual categorization. Concept activation vectors (CAVs) offer human-interpretable quantitative explainability, utilizing feature maps from intermediate layers of CNNs. Current concept-based explainability methods assess explainer faithfulness primarily through Fidelity. However, relying solely on this metric has limitations. This study extends the Invertible Concept-based Explainer (ICE) to introduce a new ingredient measuring concept consistency. We propose the CoherentICE explainability framework for CNNs, expanding beyond Fidelity. Our analysis, for the first time, highlights that Coherence provides a more reliable faithfulness evaluation for CNNs, supported by empirical validations. Our findings emphasize that accurate concepts are meaningful only when consistently accurate and improve at deeper CNN layers.

关键词： Measurement Visualization Computer vision Accuracy Current measurement Computational modeling Vectors natural language processing Convolutional neural networks Reliability

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Comparative Analysis of News Articles Summarization using LLMs

Comparative Analysis of News Articles Summarization using LL...

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Innovation in Technology (APCIT), Asia Pacific conference on

作者： Archanaa. N Shivanesh. B Suwin Kumar. J.D. T Bharathi Mohan G Srinath Doss Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Engineering and Technology Botho University Botswana

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

In the rapidly evolving domain of natural language processing (NLP), the efficiency of Large language Models (LLMs) in generating abstractive text summaries plays a pivotal role in information synthesis. This study advances the understanding of LLM performance by conducting a comprehensive evaluation of seven cutting-edge models on four distinct datasets. The models selected for comparison include Distilbart-cnn-12-6, Led-base-16384, Google’s Bigbird, Microsoft’s ProphetNet, Facebook’s BART, T5 fine-tuned, and Google’s PEGASUS . Each model’s summarization prowess is rigorously assessed using a battery of metrics: ROUGE, METEOR, BERTScore, Cosine Similarity, and BLEU. The goal is to discern the intricate relationship between dataset characteristics and model efficacy, delivering insights into the inherent advantages and limitations of each model in handling specific data contexts. The results contribute to a refined understanding of LLM applicability, offering empirical evidence to aid in the selection of the most suitable model for varied summarization needs.

关键词： Measurement Technological innovation Analytical models Large language models Asia natural language processing Data models

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What Events Do Pre-trained language Models Learn from Text? Probing Event-Based Commonsense Knowledge by Confidence Sorting 12th

What Events Do Pre-trained Language Models Learn from Text?...

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12th National CCF conference on natural language processing and Chinese Computing, NLPCC 2023

作者： Li, Jiachun Wang, Chenhao Chen, Yubo Liu, Kang Zhao, Jun The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031446924

Recently, there are a lot of works trying to probe knowledge in pre-trained language models (PLMs). Most probing works use data in knowledge bases to create a "fill-in-the-blank" task form and probe entity knowledge in auto-encoding PLMs (e.g. BERT). Though these works have got success, their methods can not be applied to some complicated knowledge like event-based commonsense knowledge and other PLMs such as auto-regressive models (like GPT). In this paper, we develop a new knowledge probe based on confidence sorting and detect event-based commonsense knowledge with it. To make the probe suitable for different types of PLMs, we integrate different knowledge scoring methods with a new method called probability difference log-likelihood score (PDL) among them. Finally, we conduct extensive experiments on several representative PLMs, explore their commonsense abilities and analyze the factors that influence their performances. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sorting

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