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Artykuły w czasopismach na temat „Embedding Analysis”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 31 lipca 2025

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1

Lainscsek, Claudia, and Terrence J. Sejnowski. "Delay Differential Analysis of Time Series." Neural Computation 27, no. 3 (2015): 594–614. http://dx.doi.org/10.1162/neco_a_00706.

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Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Bot

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Mihail Mateev. "Comparative Analysis on Implementing Embeddings for Image Analysis." Journal of Information Systems Engineering and Management 10, no. 17s (2025): 89–102. https://doi.org/10.52783/jisem.v10i17s.2710.

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This research explores how artificial intelligence enhances construction maintenance and diagnostics, achieving 95% accuracy on a dataset of 10,000 cases. The findings highlight AI's potential to revolutionize predictive maintenance in the industry. The growing adoption of image embeddings has transformed visual data processing across AI applications. This study evaluates embedding implementations in major platforms, including Azure AI, OpenAI's GPT-4 Vision, and frameworks like Hugging Face, Replicate, and Eden AI. It assesses their scalability, accuracy, cost-effectiveness, and integration f

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Samko, Natasha. "Embeddings of weighted generalized Morrey spaces into Lebesgue spaces on fractal sets." Fractional Calculus and Applied Analysis 22, no. 5 (2019): 1203–24. http://dx.doi.org/10.1515/fca-2019-0064.

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Abstract We study embeddings of weighted local and consequently global generalized Morrey spaces defined on a quasi-metric measure set (X, d, μ) of general nature which may be unbounded, into Lebesgue spaces Ls(X), 1 ≤ s ≤ p < ∞. The main motivation for obtaining such an embedding is to have an embedding of non-separable Morrey space into a separable space. In the general setting of quasi-metric measure spaces and arbitrary weights we give a sufficient condition for such an embedding. In the case of radial weights related to the center of local Morrey space, we obtain an effective sufficien

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Sabbeh, Sahar F., and Heba A. Fasihuddin. "A Comparative Analysis of Word Embedding and Deep Learning for Arabic Sentiment Classification." Electronics 12, no. 6 (2023): 1425. http://dx.doi.org/10.3390/electronics12061425.

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Sentiment analysis on social media platforms (i.e., Twitter or Facebook) has become an important tool to learn about users’ opinions and preferences. However, the accuracy of sentiment analysis is disrupted by the challenges of natural language processing (NLP). Recently, deep learning models have proved superior performance over statistical- and lexical-based approaches in NLP-related tasks. Word embedding is an important layer of deep learning models to generate input features. Many word embedding models have been presented for text representation of both classic and context-based word embed

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He, Hongliang, Junlei Zhang, Zhenzhong Lan, and Yue Zhang. "Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12863–71. http://dx.doi.org/10.1609/aaai.v37i11.26512.

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Contrastive learning-based methods, such as unsup-SimCSE, have achieved state-of-the-art (SOTA) performances in learning unsupervised sentence embeddings. However, in previous studies, each embedding used for contrastive learning only derived from one sentence instance, and we call these embeddings instance-level embeddings. In other words, each embedding is regarded as a unique class of its own, which may hurt the generalization performance. In this study, we propose IS-CSE (instance smoothing contrastive sentence embedding) to smooth the boundaries of embeddings in the feature space. Specifi

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Srinidhi, K., T. L.S Tejaswi, CH Rama Rupesh Kumar, and I. Sai Siva Charan. "An Advanced Sentiment Embeddings with Applications to Sentiment Based Result Analysis." International Journal of Engineering & Technology 7, no. 2.32 (2018): 393. http://dx.doi.org/10.14419/ijet.v7i2.32.15721.

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We propose an advanced well-trained sentiment analysis based adoptive analysis “word specific embedding’s, dubbed sentiment embedding’s”. Using available word and phrase embedded learning and trained algorithms mainly make use of contexts of terms but ignore the sentiment of texts and analyzing the process of word and text classifications. sentimental analysis on unlike words conveying same meaning matched to corresponding word vector. This problem is bridged by combining encoding opinion carrying text with sentiment embeddings words. But performing sentimental analysis on e-commerce, social n

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Ruskanda, Fariska Zakhralativa, Stefanus Stanley Yoga Setiawan, Nadya Aditama, and Masayu Leylia Khodra. "Sentiment Analysis of Sentence-Level using Dependency Embedding and Pre-trained BERT Model." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 11, no. 1 (2023): 171–80. http://dx.doi.org/10.33558/piksel.v11i1.6938.

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Sentiment analysis is a valuable field of research in NLP with many applications. Dependency tree is one of the language features that can be utilized in this field. Dependency embedding, as one of the semantic representations of a sentence, has shown to provide more significant results compared to other embeddings, which makes it a potential way to improve the performance of sentiment analysis tasks. This study aimed to investigate the effect of dependency embedding on sentence-level sentiment analysis through experimental research. The study replaced the Vocabulary Graph embedding in the VGC

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Truică, Ciprian-Octavian, Elena-Simona Apostol, Maria-Luiza Șerban, and Adrian Paschke. "Topic-Based Document-Level Sentiment Analysis Using Contextual Cues." Mathematics 9, no. 21 (2021): 2722. http://dx.doi.org/10.3390/math9212722.

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Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data using pre-trained word embeddings without considering the local context that can be extracted from the dataset, or they detect the overall topic polarity without considering both the local and global context. In this paper, we propose a novel document-topic embedding model, DocTopic2Vec, for document-level polarity detection in large texts by emplo

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Li, Qizhi, Xianyong Li, Yajun Du, Yongquan Fan, and Xiaoliang Chen. "A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis." Applied Sciences 12, no. 20 (2022): 10236. http://dx.doi.org/10.3390/app122010236.

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Since some sentiment words have similar syntactic and semantic features in the corpus, existing pre-trained word embeddings always perform poorly in sentiment analysis tasks. This paper proposes a new sentiment-enhanced word embedding (S-EWE) method to improve the effectiveness of sentence-level sentiment classification. This sentiment enhancement method takes full advantage of the mapping relationship between word embeddings and their corresponding sentiment orientations. This method first converts words to word embeddings and assigns sentiment mapping vectors to all word embeddings. Then, wo

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Moudhich, Ihab, and Abdelhadi Fennan. "Evaluating sentiment analysis and word embedding techniques on Brexit." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 695–702. https://doi.org/10.11591/ijai.v13.i1.pp695-702.

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In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve t

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Beckner, William. "Estimates on Moser Embedding." Potential Analysis 20, no. 4 (2004): 345–59. http://dx.doi.org/10.1023/b:pota.0000009813.38619.47.

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KARLSSON, FRED. "Constraints on multiple center-embedding of clauses." Journal of Linguistics 43, no. 2 (2007): 365–92. http://dx.doi.org/10.1017/s0022226707004616.

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A common view in theoretical syntax and computational linguistics holds that there are no grammatical restrictions on multiple center-embedding of clauses. Syntax would thus be characterized by unbounded recursion. An analysis of 119 genuine multiple clausal center-embeddings from seven ‘Standard Average European’ languages (English, Finnish, French, German, Latin, Swedish, Danish) uncovers usage-based regularities, constraints, that run counter to these and several other widely held views, such as that any type of multiple self-embedding (of the same clause type) would be possible, or that se

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Liu, Ruoyu. "Exploring the Impact of Word2Vec Embeddings Across Neural Network Architectures for Sentiment Analysis." Applied and Computational Engineering 97, no. 1 (2024): 93–98. http://dx.doi.org/10.54254/2755-2721/97/2024melb0085.

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Abstract. Sentiment analysis is crucial for understanding public opinion, gauging customer satisfaction, and making informed business decisions based on the emotional tone of textual data. This study investigates the performance of different Word2Vec-based embedding strategies static, non-static, and multichannel for sentiment analysis across various neural network architectures, including Convolution Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). Despite the rise of advanced contextual embedding methods such as Bidirectional Encoder Representations fr

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Liu, Ruoyu. "Exploring the Impact of Word2Vec Embeddings Across Neural Network Architectures for Sentiment Analysis." Applied and Computational Engineering 94, no. 1 (2024): 106–11. http://dx.doi.org/10.54254/2755-2721/94/2024melb0085.

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Abstract. Sentiment analysis is crucial for understanding public opinion, gauging customer satisfaction, and making informed business decisions based on the emotional tone of textual data. This study investigates the performance of different Word2Vec-based embedding strategies static, non-static, and multichannel for sentiment analysis across various neural network architectures, including Convolution Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). Despite the rise of advanced contextual embedding methods such as Bidirectional Encoder Representations fr

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Moudhich, Ihab, and Abdelhadi Fennan. "Evaluating sentiment analysis and word embedding techniques on Brexit." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 695. http://dx.doi.org/10.11591/ijai.v13.i1.pp695-702.

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<p>In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly

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Yadav, Aditya Kumar. "Refined Global Word Embeddings Based on Sentiment Concept for Sentiment Analysis." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49245.

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ABSTRACT Sentiment analysis is a significant area of study in natural language processing that finds extensive use in journalism, politics, and other domains. In sentiment analysis, word embeddings are important. The sentiment lexicons are directly incorporated into conventional word representation using the current senstiment embeddings techniques. This sentiment representation technique is unable to offer precise sentiment information for words in many situations since it can only distinguish the sentiment information of distinct words, not the same word in several settings. To address the i

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Górka, Przemysław, Tomasz Kostrzewa, and Enrique G. Reyes. "Sobolev Spaces on Locally Compact Abelian Groups: Compact Embeddings and Local Spaces." Journal of Function Spaces 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/404738.

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We continue our research on Sobolev spaces on locally compact abelian (LCA) groups motivated by our work on equations with infinitely many derivatives of interest for string theory and cosmology. In this paper, we focus on compact embedding results and we prove an analog for LCA groups of the classical Rellich lemma and of the Rellich-Kondrachov compactness theorem. Furthermore, we introduce Sobolev spaces on subsets of LCA groups and study its main properties, including the existence of compact embeddings intoLp-spaces.

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Pietrasik, Marcin, and Marek Z. Reformat. "Probabilistic Coarsening for Knowledge Graph Embeddings." Axioms 12, no. 3 (2023): 275. http://dx.doi.org/10.3390/axioms12030275.

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Knowledge graphs have risen in popularity in recent years, demonstrating their utility in applications across the spectrum of computer science. Finding their embedded representations is thus highly desirable as it makes them easily operated on and reasoned with by machines. With this in mind, we propose a simple meta-strategy for embedding knowledge graphs using probabilistic coarsening. In this approach, a knowledge graph is first coarsened before being embedded by an arbitrary embedding method. The resulting coarse embeddings are then extended down as those of the initial knowledge graph. Al

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Liu, Yi, Chengyu Yin, Jingwei Li, Fang Wang, and Senzhang Wang. "Predicting Dynamic User–Item Interaction with Meta-Path Guided Recursive RNN." Algorithms 15, no. 3 (2022): 80. http://dx.doi.org/10.3390/a15030080.

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Accurately predicting user–item interactions is critically important in many real applications, including recommender systems and user behavior analysis in social networks. One major drawback of existing studies is that they generally directly analyze the sparse user–item interaction data without considering their semantic correlations and the structural information hidden in the data. Another limitation is that existing approaches usually embed the users and items into the different embedding spaces in a static way, but ignore the dynamic characteristics of both users and items. In this paper

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Tan, Eugene, Shannon Algar, Débora Corrêa, Michael Small, Thomas Stemler, and David Walker. "Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 3 (2023): 032101. http://dx.doi.org/10.1063/5.0137223.

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Delay embedding methods are a staple tool in the field of time series analysis and prediction. However, the selection of embedding parameters can have a big impact on the resulting analysis. This has led to the creation of a large number of methods to optimize the selection of parameters such as embedding lag. This paper aims to provide a comprehensive overview of the fundamentals of embedding theory for readers who are new to the subject. We outline a collection of existing methods for selecting embedding lag in both uniform and non-uniform delay embedding cases. Highlighting the poor dynamic

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Agustiningsih, Kartikasari Kusuma, Ema Utami, and Muhammad Altoumi Alsyaibani. "Sentiment Analysis of COVID-19 Vaccines in Indonesia on Twitter Using Pre-Trained and Self-Training Word Embeddings." Jurnal Ilmu Komputer dan Informasi 15, no. 1 (2022): 39–46. http://dx.doi.org/10.21609/jiki.v15i1.1044.

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Sentiment analysis regarding the COVID-19 vaccine can be obtained from social media because users usually express their opinions through social media. One of the social media that is most often used by Indonesian people to express their opinion is Twitter. The method used in this research is Bidirectional LSTM which will be combined with word embedding. In this study, fastText and GloVe were tested as word embedding. We created 8 test scenarios to inspect performance of the word embeddings, using both pre-trained and self-trained word embedding vectors. Dataset gathered from Twitter was prepar

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Martina, Toshevska, Stojanovska Frosina, and Kalajdjiesk Jovan. "The Ability of Word Embeddings to Capture Word Similarities." International Journal on Natural Language Computing (IJNLC) Vol.9, No.3, June 2020 9, no. 3 (2023): 18. https://doi.org/10.5281/zenodo.7827290.

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Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different appr

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Chen, Xi. "Performance analysis of robustness of BERT model under attack." Journal of Physics: Conference Series 2580, no. 1 (2023): 012022. http://dx.doi.org/10.1088/1742-6596/2580/1/012022.

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Abstract With the aim of testing the robustness of machine learning models, this paper tests the performance of five classification models based on IMDB datasets. Furthermore, in this work, two types of sentence embeddings generated by word2vec and BERT are added with the noise of the normal distribution with different intensities. They are fed into the Support Vector Machine for testing. The experimental results show that the performance of the model slowly decreases as the noise intensity is increased. BERT-based sentiment embedding reduces less than Word2vec-based sentiment embedding. This

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Li, Quanzhi, Sameena Shah, Xiaomo Liu, and Armineh Nourbakhsh. "Data Sets: Word Embeddings Learned from Tweets and General Data." Proceedings of the International AAAI Conference on Web and Social Media 11, no. 1 (2017): 428–36. http://dx.doi.org/10.1609/icwsm.v11i1.14859.

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A word embedding is a low-dimensional, dense and real-valued vector representation of a word. Word embeddings have been used in many NLP tasks. They are usually generated from a large text corpus. The embedding of a word captures both its syntactic and semantic aspects. Tweets are short, noisy and have unique lexical and semantic features that are different from other types of text. Therefore, it is necessary to have word embeddings learned specifically from tweets. In this paper, we present ten word embedding data sets. In addition to the data sets learned from just tweet data, we also built

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Ayo-Soyemi, Olusola. "Market Sentiment Analysis Using NLP: Understanding Trends and Buyer Preferences in Real Estate and Environmental Sectors." Technix International Journal for Engineering Research 12, no. 3 (2025): 974–88. https://doi.org/10.5281/zenodo.15120636.

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The explosive growth of digital platforms has resulted in an explosion of unstructured textual data, such as customer reviews, social media posts, and feedback forms, which can provide significant insights into consumer preferences and industry trends.  To improve market sentiment research in the real estate and environmental sectors, this study investigates the use of Natural Language Processing (NLP) approaches, including word embedding models like Word2Vec, FastText, GloVe, and custom-developed embeddings.  The study's goal is to use these models to transform raw textual data into

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Garg, Nikhil, Londa Schiebinger, Dan Jurafsky, and James Zou. "Word embeddings quantify 100 years of gender and ethnic stereotypes." Proceedings of the National Academy of Sciences 115, no. 16 (2018): E3635—E3644. http://dx.doi.org/10.1073/pnas.1720347115.

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Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely w

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Divya, Jatain, and Singh Vikram. "An Approach for Aspect Extraction Using Double Embedding Technique based Machine Learning Model." Indian Journal of Science and Technology 16, no. 19 (2023): 1408–12. https://doi.org/10.17485/IJST/v16i19.62.

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Abstract <strong>Objectives:</strong> To perform aspect extraction for opinion mining of learner comments in the online teaching-learning scenario.<strong> Methods :</strong> A machine learning model is developed for aspect extraction. The authors collected the dataset consisting of around 5000 learner comments from Coursera and performed analysis on the dataset. To validate the results, the standard SemEval2014 dataset is used. <strong>Findings:</strong> For both the contextualised and non contextualised word embeddings, the authors compiled the results of their model

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Zhang, Xiang. "Embedding smooth diffeomorphisms in flows." Journal of Differential Equations 248, no. 7 (2010): 1603–16. http://dx.doi.org/10.1016/j.jde.2009.09.013.

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Begga, Ahmed, Francisco Escolano Ruiz, and Miguel Ángel Lozano. "Edge-Centric Embeddings of Digraphs: Properties and Stability Under Sparsification." Entropy 27, no. 3 (2025): 304. https://doi.org/10.3390/e27030304.

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In this paper, we define and characterize the embedding of edges and higher-order entities in directed graphs (digraphs) and relate these embeddings to those of nodes. Our edge-centric approach consists of the following: (a) Embedding line digraphs (or their iterated versions); (b) Exploiting the rank properties of these embeddings to show that edge/path similarity can be posed as a linear combination of node similarities; (c) Solving scalability issues through digraph sparsification; (d) Evaluating the performance of these embeddings for classification and clustering. We commence by identifyi

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Altuntas, Volkan. "NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning." Applied Sciences 14, no. 2 (2024): 775. http://dx.doi.org/10.3390/app14020775.

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Network node embedding captures structural and relational information of nodes in the network and allows for us to use machine learning algorithms for various prediction tasks on network data that have an inherently complex and disordered structure. Network node embedding should preserve as much information as possible about important network properties where information is stored, such as network structure and node properties, while representing nodes as numerical vectors in a lower-dimensional space than the original higher dimensional space. Superior node embedding algorithms are a powerful

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Li, Yu, Yuan Tian, Jiawei Zhang, and Yi Chang. "Learning Signed Network Embedding via Graph Attention." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4772–79. http://dx.doi.org/10.1609/aaai.v34i04.5911.

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Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks. Recently graph convolutional networks (GCNs) have revolutionized the field of network embedding, and led to state-of-the-art performance in network analysis tasks such as link prediction and node classification. Nevertheless, most of the existing GCN-based network embedding methods are proposed for unsigned networks. However, in the real world, some of the networks are signed, where the links are annotated with different polarities, e

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Wang, Zhuo-yuan, Xiao-yan Li, Xiao-jun Gou, et al. "Network Meta-Analysis of Acupoint Catgut Embedding in Treatment of Simple Obesity." Evidence-Based Complementary and Alternative Medicine 2022 (May 23, 2022): 1–16. http://dx.doi.org/10.1155/2022/6408073.

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Objective. To evaluate the clinical efficacy of acupoint catgut embedding in the treatment of simple obesity through network meta-analysis. Methods. PubMed, Cochrane, Embase, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP database (VIP) were searched by using computer from 2011 to August 2021, and 35 RCT studies were retrieved. The quality of the literature was evaluated using the modified Jadad scoring table, and Stata 15.0 software was used for traditional meta-analysis and network meta-analysis. Results. Thirty-five RCTs (3040 cases in total) were included. Acupoint embedd

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Zhang, Hailin, Penghao Zhao, Xupeng Miao, et al. "Experimental Analysis of Large-Scale Learnable Vector Storage Compression." Proceedings of the VLDB Endowment 17, no. 4 (2023): 808–22. http://dx.doi.org/10.14778/3636218.3636234.

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Learnable embedding vector is one of the most important applications in machine learning, and is widely used in various database-related domains. However, the high dimensionality of sparse data in recommendation tasks and the huge volume of corpus in retrieval-related tasks lead to a large memory consumption of the embedding table, which poses a great challenge to the training and deployment of models. Recent research has proposed various methods to compress the embeddings at the cost of a slight decrease in model quality or the introduction of other overheads. Nevertheless, the relative perfo

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Kokane, Chandrakant D., Sachin D. Babar, Parikshit N. Mahalle, and Shivprasad P. Patil. "Word sense disambiguation: Mathematical modelling of adaptive word embedding technique for word vector." Journal of Interdisciplinary Mathematics 26, no. 3 (2023): 475–82. http://dx.doi.org/10.47974/jim-1675.

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Word embedding is the method of representing ambiguous words into word vectors. The existing methods of word embedding are applicable for homonymous words. Constructing word vector of polysemous words is the challenge. The word vector of polysemous words are made by considering context information. The proposed adaptive word embedding technique is discussed in this article. The adaptive word embedding technique is applicable for both homosemous and polysemous words. While representing ambiguous word into word vector the context information is considered. The adaptive word embedding technique g

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Goel, Mukta, and Rohit Goel. "Comparative Analysis of Hybrid Transform Domain Image Steganography Embedding Techniques." International Journal of Scientific Research 2, no. 2 (2012): 388–90. http://dx.doi.org/10.15373/22778179/feb2013/131.

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Baalous, Rawan, and Ronald Poet. "Utilizing Sentence Embedding for Dangerous Permissions Detection in Android Apps' Privacy Policies." International Journal of Information Security and Privacy 15, no. 1 (2021): 173–89. http://dx.doi.org/10.4018/ijisp.2021010109.

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Privacy policies analysis relies on understanding sentences meaning in order to identify sentences of interest to privacy related applications. In this paper, the authors investigate the strengths and limitations of sentence embeddings to detect dangerous permissions in Android apps privacy policies. Sent2Vec sentence embedding model was utilized and trained on 130,000 Android apps privacy policies. The terminology extracted by the sentence embedding model was then compared with the gold standard on a dataset of 564 privacy policies. This work seeks to provide answers to researchers and develo

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Spector, Daniel. "An optimal Sobolev embedding for L1." Journal of Functional Analysis 279, no. 3 (2020): 108559. http://dx.doi.org/10.1016/j.jfa.2020.108559.

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Friz, Peter, and Nicolas Victoir. "A variation embedding theorem and applications." Journal of Functional Analysis 239, no. 2 (2006): 631–37. http://dx.doi.org/10.1016/j.jfa.2005.12.021.

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Netzer, Tim, and Andreas Thom. "Tracial algebras and an embedding theorem." Journal of Functional Analysis 259, no. 11 (2010): 2939–60. http://dx.doi.org/10.1016/j.jfa.2010.08.010.

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Shi, Yaxin, Donna Xu, Yuangang Pan, Ivor W. Tsang, and Shirui Pan. "Label Embedding with Partial Heterogeneous Contexts." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4926–33. http://dx.doi.org/10.1609/aaai.v33i01.33014926.

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Label embedding plays an important role in many real-world applications. To enhance the label relatedness captured by the embeddings, multiple contexts can be adopted. However, these contexts are heterogeneous and often partially observed in practical tasks, imposing significant challenges to capture the overall relatedness among labels. In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges. Categorizing heterogeneous contexts into two groups, relational context and descriptive context, we design tailor-made matrix facto

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Xiao, Bai, Edwin Hancock, and Hang Yu. "Manifold embedding for shape analysis." Neurocomputing 73, no. 10-12 (2010): 1606–13. http://dx.doi.org/10.1016/j.neucom.2009.10.023.

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Junping Zhang, Qi Wang, Li He, and Zhi-Hua Zhou. "Quantitative Analysis of Nonlinear Embedding." IEEE Transactions on Neural Networks 22, no. 12 (2011): 1987–98. http://dx.doi.org/10.1109/tnn.2011.2171991.

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Fleischmann, Oliver, Lennart Wietzke, and Gerald Sommer. "Image Analysis by Conformal Embedding." Journal of Mathematical Imaging and Vision 40, no. 3 (2011): 305–25. http://dx.doi.org/10.1007/s10851-011-0263-5.

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Jangi, S., and Y. Jain. "Embedding spectral analysis in equipment." IEEE Spectrum 28, no. 2 (1991): 40–43. http://dx.doi.org/10.1109/6.100909.

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Ravindran, Renjith P., and Kavi Narayana Murthy. "Syntactic Coherence in Word Embedding Spaces." International Journal of Semantic Computing 15, no. 02 (2021): 263–90. http://dx.doi.org/10.1142/s1793351x21500057.

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Word embeddings have recently become a vital part of many Natural Language Processing (NLP) systems. Word embeddings are a suite of techniques that represent words in a language as vectors in an n-dimensional real space that has been shown to encode a significant amount of syntactic and semantic information. When used in NLP systems, these representations have resulted in improved performance across a wide range of NLP tasks. However, it is not clear how syntactic properties interact with the more widely studied semantic properties of words. Or what the main factors in the modeling formulation

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Zhao, Peilian, Cunli Mao, and Zhengtao Yu. "Semi-Supervised Aspect-Based Sentiment Analysis for Case-Related Microblog Reviews Using Case Knowledge Graph Embedding." International Journal of Asian Language Processing 30, no. 03 (2020): 2050012. http://dx.doi.org/10.1142/s2717554520500125.

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Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal fiel

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Jawale, Shila Sumol, and S. D. Sawarker. "Amalgamation of Embeddings With Model Explainability for Sentiment Analysis." International Journal of Applied Evolutionary Computation 13, no. 1 (2022): 1–24. http://dx.doi.org/10.4018/ijaec.315629.

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Regarding the ubiquity of digitalization and electronic processing, an automated review processing system, also known as sentiment analysis, is crucial. There were many architectures and word embeddings employed for effective sentiment analysis. Deep learning is now-a-days becoming prominent for solving these problems as huge amounts of data get generated per second. In deep learning, word embedding acts as a feature representative and plays an important role. This paper proposed a novel deep learning architecture which represents hybrid embedding techniques that address polysemy, semantic and

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Khushhal, Saquib, Abdul Majid, Syed Ali Abass, Rabia Riaz, Mohammad Babar, and Shafiq Ahmad. "Cword2vec: a novel morphological rule-based word embedding approach for Urdu text sentiment analysis." PeerJ Computer Science 11 (July 15, 2025): e2937. https://doi.org/10.7717/peerj-cs.2937.

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Word embeddings are essential to natural language processing tasks because they contain a single word’s syntactic and semantic information. Word embeddings have been developed widely for numerous spoken languages across the globe like English. The research community needs to pay more attention to the Urdu language despite its significant number of speakers, which amounts to approximately 231.3 million individuals. Urdu is a complex language because word boundaries in Urdu are unspecified, as it does not employ delimiters between words. The compound word, a multiword expression, is a more compl

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Franchetti, Carlo, and E. W. Cheney. "The embedding of proximinal sets." Journal of Approximation Theory 48, no. 2 (1986): 213–25. http://dx.doi.org/10.1016/0021-9045(86)90006-7.

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Khine, Aye Hninn, Wiphada Wettayaprasit, and Jarunee Duangsuwan. "A novel meta-embedding technique for drug reviews sentiment analysis." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1938–46. https://doi.org/10.11591/ijai.v12.i4.pp1938-1946.

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Traditional word embedding models have been used in the feature extraction process of deep learning models for sentiment analysis. However, these models ignore the sentiment properties of words while maintaining the contextual relationships and have inadequate representation for domain-specific words. This paper proposes a method to develop a meta embedding model by exploiting domain sentiment polarity and adverse drug reaction (ADR) features to render word embedding models more suitable for medical sentiment analysis. The proposed lexicon is developed from the medical blogs corpus. The polari

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