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Journal articles on the topic 'Semantic relevance'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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Sartori, Giuseppe, and Luigi Lombardi. "Semantic Relevance and Semantic Disorders." Journal of Cognitive Neuroscience 16, no. 3 (2004): 439–52. http://dx.doi.org/10.1162/089892904322926773.

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Semantic features are of different importance in concept representation. The concept elephant may be more easily identified from the feature <trunk> than from the feature <four legs>. We propose a new model of semantic memory to measure the relevance of semantic features for a concept and use this model to investigate the controversial issue of category specificity. Category-specific patients have an impairment in one domain of knowledge (e.g., living), whereas the other domain (e.g., nonliving) is relatively spared. We show that categories differ in the level of relevance and that, when concepts belonging to living and nonliving categories are equated to this parameter, the category-specific disorder disappears. Our findings suggest that category specificity, as well as other semantic-related effects, may be explained by a semantic memory model in which concepts are represented by semantic features with associated relevance values.

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Dezani-Ciancaglini, Mariangiola, Alain Frisch, Elio Giovannetti, and Yoko Motohama. "The Relevance of Semantic Subtyping." Electronic Notes in Theoretical Computer Science 70, no. 1 (2003): 88–105. http://dx.doi.org/10.1016/s1571-0661(04)80492-4.

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WEDGWOOD, DANIEL. "Shared assumptions: Semantic minimalism and Relevance Theory." Journal of Linguistics 43, no. 3 (2007): 647–81. http://dx.doi.org/10.1017/s0022226707004793.

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Cappelen & Lepore (2005, 2006a, 2007) note that linguistic communication requires ‘shared content’ and claim that Relevance Theory makes content sharing impossible. This criticism rests upon two important errors. The first is a flawed understanding of Relevance Theory, shown in the application of an omniscient third party perspective to parts of Relevance Theory that depend only upon subjective judgements made by the addressee of an utterance. The second is confusion about different definitions of content. Cappelen & Lepore's evidence actually involves the communication of what they term Speech Act content, which need not be perfectly ‘shared’ according to their own position. Looking beyond this flawed criticism, a general comparison of Relevance Theory with Cappelen & Lepore's semantic minimalism reveals significant parallels, pointing to a notable convergence of two distinct approaches – one cognitive-pragmatic, the other philosophical-semantic – on the rejection of currently dominant assumptions in linguistic semantics. The key remaining difference is Cappelen & Lepore's claim that shared content is propositional. This contradicts other claims made for such content and in any case plays no active role in the explanation of communication. Cappelen & Lepore's position thus poses no threat to Relevance Theory; rather, Relevance Theory can benefit from their philosophical analysis of the state of semantic theory.

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Zhang, Huatian, Zhendong Mao, Kun Zhang, and Yongdong Zhang. "Show Your Faith: Cross-Modal Confidence-Aware Network for Image-Text Matching." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3262–70. http://dx.doi.org/10.1609/aaai.v36i3.20235.

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Image-text matching bridges vision and language, which is a crucial task in the field of multi-modal intelligence. The key challenge lies in how to measure image-text relevance accurately as matching evidence. Most existing works aggregate the local semantic similarities of matched region-word pairs as the overall relevance, and they typically assume that the matched pairs are equally reliable. However, although a region-word pair is locally matched across modalities, it may be inconsistent/unreliable from the global perspective of image-text, resulting in inaccurate relevance measurement. In this paper, we propose a novel Cross-Modal Confidence-Aware Network to infer the matching confidence that indicates the reliability of matched region-word pairs, which is combined with the local semantic similarities to refine the relevance measurement. Specifically, we first calculate the matching confidence via the relevance between the semantic of image regions and the complete described semantic in the image, with the text as a bridge. Further, to richly express the region semantics, we extend the region to its visual context in the image. Then, local semantic similarities are weighted with the inferred confidence to filter out unreliable matched pairs in aggregating. Comprehensive experiments show that our method achieves state-of-the-art performance on benchmarks Flickr30K and MSCOCO.

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BIMBÓ, KATALIN, J. MICHAEL DUNN, and ROGER D. MADDUX. "RELEVANCE LOGICS AND RELATION ALGEBRAS." Review of Symbolic Logic 2, no. 1 (2009): 102–31. http://dx.doi.org/10.1017/s1755020309090145.

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Relevance logics are known to be sound and complete for relational semantics with a ternary accessibility relation. This paper investigates the problem of adequacy with respect to special kinds of dynamic semantics (i.e., proper relation algebras and relevant families of relations). We prove several soundness results here. We also prove the completeness of a certain positive fragment of R as well as of the first-degree fragment of relevance logics. These results show that some core ideas are shared between relevance logics and relation algebras. Some details of certain incompleteness results, however, pinpoint where relevance logics and relation algebras diverge. To carry out these semantic investigations, we define a new tableaux formalization and new sequent calculi (with the single cut rule admissible) for various relevance logics.

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Patil, Pushpa B., and Manesh Kokare. "Semantic Image Retrieval Using Relevance Feedback." International journal of Web & Semantic Technology 2, no. 4 (2011): 139–48. http://dx.doi.org/10.5121/ijwest.2011.2411.

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Lumsden, David. "Does speaker's reference have semantic relevance?" Philosophical Studies 47, no. 1 (1985): 15–21. http://dx.doi.org/10.1007/bf00355084.

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Pu, Qiang, and Daqing He. "Semantic Clustering Based Relevance Language Model." Information Technology Journal 9, no. 2 (2010): 236–46. http://dx.doi.org/10.3923/itj.2010.236.246.

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Kussmaul, Paul. "Semantic Models and Translating." Target. International Journal of Translation Studies 6, no. 1 (1994): 1–13. http://dx.doi.org/10.1075/target.6.1.02kus.

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Abstract This paper examines the relevance of three semantic models for translation. Structural semantics, more specifically semantic feature analysis, has given rise to the maxim that we should translate "bundles of semantic features". Prototype semantics suggests that word-meanings have cores and fuzzy edges which are influenced by culture. For translation this means that we do not necessarily translate bundles of features but have to decide whether to focus on the core or the fuzzy edges of the meaning of a particular word. Scenesand-frames semantics suggests that word meaning is influenced by context and the situation we are in. Word-meaning is thus not static but dynamic, and it is this dynamism which should govern our decisions as translators.

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CUCCHIARA, RITA, COSTANTINO GRANA, and ANDREA PRATI. "SEMANTIC VIDEO TRANSCODING USING CLASSES OF RELEVANCE." International Journal of Image and Graphics 03, no. 01 (2003): 145–69. http://dx.doi.org/10.1142/s0219467803000956.

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In this work we present a framework for on-the-fly video transcoding that exploits computer vision-based techniques to adapt the Web access to the user requirements. The proposed transcoding approach aims at coping with both user bandwidth and resources capabilities, and with user interests in the video's content. We propose an object-based semantic transcoding that, according to the user-defined classes of relevance, applies different transcoding techniques to the objects segmented in a scene. Object extraction is provided by on-the-fly video processing, without manual annotation. Multiple transcoding policies are reviewed and a performance evaluation metric based on the Weighted Mean Square Error (and corresponding PSNR), that takes into account the perceptual user requirements by means of classes of relevance, is defined. Results are analyzed by varying transcoding techniques, bandwidth requirements and video types (with indoor and outdoor scenes), showing that the use of semantics can dramatically improve the bandwidth to distortion ratio.

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Gvoždiak, Vít. "Gesture, ostension, and relevance: pragmatics of semantic gesture." Slovo a slovesnost 85, no. 4 (2024): 290–312. http://dx.doi.org/10.58756/s7348527.

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Semantic gesture is considered one of Jan Mukařovský’s key conceptual contributions to the theory of (artistic) communication. It is also a contribution that is still commented on and further expanded upon today. As a semantic-synthetic principle of dynamic unification of a work of art, the mechanism of semantic gesture defies reduction to pure intention, content or simply coded meaning. This paper develops the view that semantic gesture represents a pragmatic mechanism with potential for elaboration within the framework of a pragmatic theory of relevance. In the first part, it attempts to substantiate the view that semantic gesture exhibits a distinctly pragmatic character, as it is both (i) dependent on the under-determination of linguistic input and (ii) applicable outside the domain of artistic texts. The second part attempts to define the context of relevance theory as a suitable field for further exploration of semantic gesture beyond traditional semiological boundaries, especially by pointing out two types of dynamism and by relating the notion of semantic gesture to the pragmatic mechanisms of poetic effects.

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Frandsen, Finn. "Semantisk isotopi og diskursanalyse." HERMES - Journal of Language and Communication in Business 1, no. 1 (2015): 45. http://dx.doi.org/10.7146/hjlcb.v1i1.21348.

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Even though the notion of semantic isotopy is being extensively used within the analysis of both literary texts and special-purpose texts, the theoretical problems yet unsolved are legion. In this article I first demonstrate the great relevance of this notion for discourse analysis and then give a survey of Greimas' classical definitions of semantic isotopy and the previous critique of this conception. Finally, I evaluate Rastier's reformulation of semantic isotopy from the point of view of interpretative semantics.

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Picariello, Antonio, and Antonio M. Rinaldi. "User Relevance Feedback in Semantic Information Retrieval." International Journal of Intelligent Information Technologies 3, no. 2 (2007): 36–50. http://dx.doi.org/10.4018/jiit.2007040103.

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Thukral, Anjali, Hema Banati, and Punam Bedi. "Ranking Tagged Resources Using Social Semantic Relevance." International Journal of Information Retrieval Research 1, no. 3 (2011): 15–34. http://dx.doi.org/10.4018/ijirr.2011070102.

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The WWW today is overwhelmed with information on almost every topic. Therefore, relevance ranking of web pages to a user’s expectations is a challenge, rather than retrieving a collection of thousands of web pages selected by keyword matching. This paper presents an approach to rank tagged web pages retrieved from a Social Bookmarking Site for a learner who needs web resources containing content on a given topic. Besides the popularity of the web page in the community, the relevance of a web page for ranking is computed based on the semantic distance between tags and a given topic using domain ontology. An experimental study has been conducted to evaluate the ranks generated by the proposed approach. The test collection was created using a questionnaire which was designed to judge the crawled web pages for their graded relevance on a topic.

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Lee, Kyoung-Mi. "Emotion Relevance Learning for Semantic Image Retrieval." Advanced Science Letters 9, no. 1 (2012): 393–98. http://dx.doi.org/10.1166/asl.2012.2529.

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Shi, Fei, Fangfang Yang, and Jiajun Wang. "Supervised Semantic Image Annotation Using Region Relevance." Physics Procedia 33 (2012): 912–18. http://dx.doi.org/10.1016/j.phpro.2012.05.154.

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SARTORI, G., G. NEGRI, I. MARIANI, and S. PRIONI. "Relevance of Semantic Features and Category Specificity." Cortex 40, no. 1 (2004): 191–93. http://dx.doi.org/10.1016/s0010-9452(08)70947-x.

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Lou, Ying, Zhanhuai Li, and Qun Chen. "Semantic relevance ranking for XML keyword search." Information Sciences 190 (May 2012): 127–43. http://dx.doi.org/10.1016/j.ins.2011.12.011.

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Bujang, George Romiko, and Mary Fatimah Subet. "MEMPERKASAKAN KEMAHIRAN BERFIKIR ARAS TINGGI (KBAT) DALAM MENGUNGKAI MAKNA DI SEBALIK BAHAN SENI BAHASA: SATU PENDEKATAN SEMANTIK INKUISITIF." Asian People Journal (APJ) 5, no. 1 (2022): 101–13. http://dx.doi.org/10.37231/apj.2022.5.1.317.

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Abstract: Rhymes is a language’s art material that should be learned in Malay Language Education especially at the primary school education level. However, a number of students are facing the difficulty of discovering the implicit meaning contained in the language's art material. Thus, this study was carried out to describe either the Inquisitive Semantic (IS) approach introduced by Nor Hashimah Jalaluddin (2014) can stimulate the cognitive of students to unravel the implicit meaning of the rhymes. This qualitative study used a text analysis and observation 7 respondents consisting of Year Five students. The findings showed that pupils able to carry out an implicit meaning through systematic and multidisciplinary methods. The using of Relevance Theory in the second stage of resonance semantic analysis makes learning Malay education more interesting and meaningful as well as providing better quality learning outcomes. Inquisitive Semantics approach is expected to help students empower High Order Thinking Skills (HOTS) in the teaching and learning language arts’ process. Keywords: Language arts; Resonance Semantic;, Inquisitive Semantics; Relevance Theory; HOTS.  Abstrak: Sajak merupakan bahan seni bahasa yang harus dipelajari dalam Pendidikan Bahasa Melayu lebih-lebih lagi di peringkat pendidikan rendah. Walau bagaimanapun, masih terdapat sebilangan pelajar menghadapi kesukaran untuk mencungkil makna tersirat yang terkandung di dalam bahan seni bahasa itu. Justeru, kajian ini dijalankan untuk menyingkap sejauh mana pendekatan Semantik Inkuisitif (SI) yang diperkenalkan oleh Nor Hashimah Jalaluddin (2014) dapat merangsang kognitif murid mengungkai makna tersirat di sebalik sajak. Kajian kualitatif ini menggunakan kaedah analisis teks bahan seni bahasa dan pemerhatian terhadap 7 orang responden yang terdiri daripada pelajar Tahun Lima. Hasil kajian mendapati pelajar berupaya mencungkil makna tersirat melalui kaedah pengesanan makna yang bersistematik dan multidisiplin. Penggunaan Teori Relevans dalam peringkat kedua analisis semantik resonans menjadikan pembelajaran pendidikan Bahasa Melayu lebih menarik dan bermakna serta memberikan hasil pembelajaran yang lebih berkualiti. Pendekatan SI ini diharap akan menjadi satu lagi pendekatan yang utama untuk membantu pelajar memperkasakan Kemahiran Berfikir Aras Tinggi (KBAT) dalam proses pengajaran dan pembelajaran seni bahasa. Kata kunci: Seni bahasa; Semantik Resonans; Semantik Inkuisitif; Teori Relevans; KBAT

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Guo, Yu Tang, and Chang Gang Han. "Automatic Image Annotation Using Semantic Subspace Graph Spectral Clustering Algorithm." Advanced Materials Research 271-273 (July 2011): 1090–95. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1090.

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Due to the existing of the semantic gap, images with the same or similar low level features are possibly different on semantic level. How to find the underlying relationship between the high-level semantic and low level features is one of the difficult problems for image annotation. In this paper, a new image annotation method based on graph spectral clustering with the consistency of semantics is proposed with detailed analysis on the advantages and disadvantages of the existed image annotation methods. The proposed method firstly cluster image into several semantic classes by semantic similarity measurement in the semantic subspace. Within each semantic class, images are re-clustered with visual features of region Then, the joint probability distribution of blobs and words was modeled by using Multiple-Bernoulli Relevance Model. We can annotate a unannotated image by using the joint distribution. Experimental results show the the effectiveness of the proposed approach in terms of quality of the image annotation. the consistency of high-level semantics and low level features is efficiently achieved.

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Vijayan, Naveen Edapurath. "Enhancing Chatbot Response Relevance through Semantic Similarity Measures." Journal of Artificial Intelligence & Cloud Computing 1, no. 1 (2022): 1–5. http://dx.doi.org/10.47363/jaicc/2022(1)e182.

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Semantic similarity measures have shown promise in enhancing natural language understanding by quantifying the likeness between textual elements. This paper investigates the application of semantic similarity measures to improve chatbot response relevance.

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Petrea, Galina. "Thruthconditional Character of the Utterance - the Semantic Default and Post-Semantic Pragmatics." Intertext, no. 2 (January 2025): 60–69. https://doi.org/10.54481/intertext.2024.2.06.

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The research objective for the present article is to explore the landscape of theories related to the semantic vericonditional aspect, which is a basic feature of the utterance. Collecting the theoretical kit in order to aggregate the relevant conceptual framework will allow us to operationalize and apply them in the architecture of the theoretical interpretative process as well as in the analysis of the subject of interest: parliamentary discourse - a subspecies of political discourse. The given investigation proceeds from the establishment of a conceptual framework, a delimitation of the distinctions between semantics and pragmatics, the complex phrase being the fundamental unit of syntax and formal semantics with vericonditional compositional content that needs to be saturated with meaning - the default interpretation. The utterance, the unit of discourse mobilizes all contextual information, resolves the ambiguities of lexical semantics - disambiguates the elements with the scalar palette of meaning - generalized common ground implicature but also arising from the context taking into account the principle of consistency and relevance. The level of postpragmatic, particularized implicature are subjects of preferred interpretation and do not depend on the content of the concrete utterance in context. In the case study examples, they concern loyalty to the homeland, ideology and party affiliation, dismantling right-wing extremist-radicalist tendencies, hack-ing the anti-Semitic concept and using the term as a weapon for different situations and contexts. The article is informed by several evolved key works on pragmatist theories including formal semantic minimalism theory, semantic underdetermination theory, cognitivist relevance theory, scalarity of meaning theory, and factorization theory. All of these theories bring their contribution to the theoretical understanding and pragmatic analysis of sophisticated public discourse.

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Wang, Ming Wei, Shu Sheng Zhang, Jing Tao Zhou, and Han Zhao. "Semantic Integration of Manufacturing Data Sources." Materials Science Forum 532-533 (December 2006): 1156–59. http://dx.doi.org/10.4028/www.scientific.net/msf.532-533.1156.

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In order to gain insight into business processes, multiple autonomous data sources residing in the manufacture enterprise need to integrate not only on storage and access methods but also capturing the meaning of data to get a coherent and meaningful data views for different applications requirements. This paper presents a semantic-based architecture for the integration of heterogeneous manufacturing data sources. The integration is realized on a semantic level by the explicit presentation of data semantics with ontology and relationships between ontologies. During applications usage, heterogeneous data sources which represent relations of relevance are dynamically organized in terms of their semantics. The paper discusses some major problems in the architecture: unified schema transformation, semi-automatic ontology generation and mediation.

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Jin, Hanqi, Tianming Wang, and Xiaojun Wan. "SemSUM: Semantic Dependency Guided Neural Abstractive Summarization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8026–33. http://dx.doi.org/10.1609/aaai.v34i05.6312.

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In neural abstractive summarization, the generated summaries often face semantic irrelevance and content deviation from the input sentences. In this work, we incorporate semantic dependency graphs about predicate-argument structure of input sentences into neural abstractive summarization for the problem. We propose a novel semantics dependency guided summarization model (SemSUM), which can leverage the information of original input texts and the corresponding semantic dependency graphs in a complementary way to guide summarization process. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. Experiments show that the proposed model improves semantic relevance and reduces content deviation, and also brings significant improvements on automatic evaluation ROUGE metrics.

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Huang, Wenhao, Xiaoyan Li, Xiao Zhou, et al. "A Semantic and Optimized Focused Crawler Based on Semantic Graph and Genetic Algorithm." Symmetry 16, no. 11 (2024): 1439. http://dx.doi.org/10.3390/sym16111439.

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A focused crawler automatically retrieves, organizes, and extracts specific topic-related information from the internet for analysis and application. Currently, most focused crawlers assess the relevance of web pages to a given topic through methods such as keyword matching, semantic analysis, and link structures. However, these existing focused crawlers suffer from issues such as misleading directions and reduced accuracy due to the lack of semantic analysis of topic terms, as well as biased computation of topic relevance caused by the absence of effective weighting factors. To solve the above-mentioned problems, this study proposes a semantic and optimized focused crawler based on Semantic Graph and Genetic Algorithm. The proposed crawler eliminates ambiguous terms by constructing a semantic graph, optimizes the weighting factors of topic relevance with asymmetry by using a genetic algorithm, and combines both above two points to predict the priority of each unvisited hyperlink. The experiment results indicate that the proposed SG-GA Crawler improves the evaluation indicators compared with the other three focused crawlers, including VSM Crawler, SSRM Crawler, and SG Crawler. More specifically, the percentage improvement achieved by the proposed method exceeds 19%, 19%, and 13% in terms of three evaluation indicators, including the number of relevant web pages, acquisition rate, and average relevance, respectively. In conclusion, the proposed focused crawler can grab more quantity and higher quality topic-related web pages from the Internet.

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Xu, Qianli, Ana Garcia Del Molino, Jie Lin, et al. "Lifelog Image Retrieval Based on Semantic Relevance Mapping." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 3 (2021): 1–18. http://dx.doi.org/10.1145/3446209.

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Lifelog analytics is an emerging research area with technologies embracing the latest advances in machine learning, wearable computing, and data analytics. However, state-of-the-art technologies are still inadequate to distill voluminous multimodal lifelog data into high quality insights. In this article, we propose a novel semantic relevance mapping ( SRM ) method to tackle the problem of lifelog information access. We formulate lifelog image retrieval as a series of mapping processes where a semantic gap exists for relating basic semantic attributes with high-level query topics. The SRM serves both as a formalism to construct a trainable model to bridge the semantic gap and an algorithm to implement the training process on real-world lifelog data. Based on the SRM, we propose a computational framework of lifelog analytics to support various applications of lifelog information access, such as image retrieval, summarization, and insight visualization. Systematic evaluations are performed on three challenging benchmarking tasks to show the effectiveness of our method.

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Yang, Guan, Ayou Han, Xiaoming Liu, Yang Liu, Tao Wei, and Zhiyuan Zhang. "Enhancing Semantic-Consistent Features and Transforming Discriminative Features for Generalized Zero-Shot Classifications." Applied Sciences 12, no. 24 (2022): 12642. http://dx.doi.org/10.3390/app122412642.

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Generalized zero-shot learning (GZSL) aims to classify classes that do not appear during training. Recent state-of-the-art approaches rely on generative models, which use correlating semantic embeddings to synthesize unseen classes visual features; however, these approaches ignore the semantic and visual relevance, and visual features synthesized by generative models do not represent their semantics well. Although existing GZSL methods based on generative model disentanglement consider consistency between visual and semantic models, these methods consider semantic consistency only in the training phase and ignore semantic consistency in the feature synthesis and classification phases. The absence of such constraints may lead to an unrepresentative synthesized visual model with respect to semantics, and the visual and semantic features are not modally well aligned, thus causing the bias between visual and semantic features. Therefore, an approach for GZSL is proposed to enhance semantic-consistent features and discriminative features transformation (ESTD-GZSL). The proposed method can enhance semantic-consistent features at all stages of GZSL. A semantic decoder module is first added to the VAE to map synthetic and real features to the corresponding semantic embeddings. This regularization method allows synthesizing unseen classes for a more representative visual representation, and synthetic features can better represent their semantics. Then, the semantic-consistent features decomposed by the disentanglement module and the features output by the semantic decoder are transformed into enhanced semantic-consistent discriminative features and used in classification to reduce the ambiguity between categories. The experimental results show that our proposed method achieves more competitive results on four benchmark datasets (AWA2, CUB, FLO, and APY) of GZSL.

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Chan, Patrick, Yoshinori Hijikata, Toshiya Kuramochi, and Shogo Nishida. "Semantic Relatedness Estimation using the Layout Information of Wikipedia Articles." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 2 (2013): 30–48. http://dx.doi.org/10.4018/ijcini.2013040103.

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Computing the semantic relatedness between two words or phrases is an important problem in fields such as information retrieval and natural language processing. Explicit Semantic Analysis (ESA), a state-of-the-art approach to solve the problem uses word frequency to estimate relevance. Therefore, the relevance of words with low frequency cannot always be well estimated. To improve the relevance estimate of low-frequency words and concepts, the authors apply regression to word frequency, its location in an article, and its text style to calculate the relevance. The relevance value is subsequently used to compute semantic relatedness. Empirical evaluation shows that, for low-frequency words, the authors’ method achieves better estimate of semantic relatedness over ESA. Furthermore, when all words of the dataset are considered, the combination of the authors’ proposed method and the conventional approach outperforms the conventional approach alone.

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Xue, Ruo Juan. "An Effective Approach for Instructional Resource Database Construction with Web Images." Key Engineering Materials 439-440 (June 2010): 1361–66. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1361.

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In order to effectively utilize Web images to construct instructional resource database, a novel approach is proposed in this paper. With this approach, Web images and their semantics can be automatically downloaded, extracted and stored in resource database and the semantics can be refined by user feedback in retrieval progress. Image topic dictionary is built as the basis to extract semantics. Eight kinds of text are extracted as semantic source from Web pages. Based on image topic dictionary, image semantics can be extracted from the eight kinds of text. In order to further improve the accuracy of semantic extraction, we propose relevance feedback mechanism. Users can provide feedback to refine semantic annotation. The experimental results show that the approach is effective, in which high construction efficiency and quality can be achieved. The approach is better than manual annotation in efficiency and better than automatic annotation in accuracy. The similar methods can be applied to construct resource database of other forms of multimedia.

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Montefinese, Maria, Glyn Hallam, Hannah Elizabeth Thompson, and Elizabeth Jefferies. "The interplay between control processes and feature relevance: Evidence from dual-task methodology." Quarterly Journal of Experimental Psychology 73, no. 3 (2019): 384–95. http://dx.doi.org/10.1177/1747021819877163.

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Neuropsychological studies suggest a distinction between (a) semantic knowledge and (b) control processes that shape the retrieval of conceptual information to suit the task or context. These aspects of semantic cognition are specifically impaired in patients with semantic dementia and semantic aphasia, respectively. However, interactions between the structure of knowledge and control processes that are expected during semantic retrieval have not been fully characterised. In particular, domain-general executive resources may not have equal relevance for the capacity to promote weak yet task relevant features (i.e., “controlled retrieval) and to ignore or suppress distracting information (i.e., “selection”). Here, using a feature selection task, we tested the contribution of featural relevance to semantic performance in healthy participants under conditions of divided attention. Healthy participants showed greater dual-task disruption as the relevance value of the distractor feature linearly increased, supporting the emerging view that semantic relevance is one of the organising principles of the structure of semantic representation. Moreover, word frequency, and inter-correlational strength affected overall performance, but they did not show an interaction with dual-task conditions. These results suggest that domain-general control processes, disrupted by divided attention, are more important to the capacity to efficiently avoid distracting information during semantic decision-making than to the promotion of weak target features. The present study therefore provides novel information about the nature of the interaction between structured conceptual knowledge and control processes that support the retrieval of appropriate information and relates these results to a new theoretical framework, termed controlled semantic cognition.

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Jafarova, A. "LEXICAL-SEMANTIC FEATURES OF VERBS DERIVED FROM THE NAMES OF PROFESSIONS IN THE RUSSION LANGUAGE." Sciences of Europe, no. 160 (March 11, 2025): 12–14. https://doi.org/10.5281/zenodo.15004404.

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The article deals with analysis of the lexical-semantic features of verbs derived from the names of professions in the context of modern word-formation processes in the Russian language. The relevance of the study is due to the limited knowledge of this group of verbs, which have a complex semantic structure and rich stylistic potential. Productive word-formation models are identified, the main semantic peculiarities are analyzed, including direct designation of professional activity, metaphorization and figurative meanings. The article highlights the functional and stylistic features of verbs in various discursive practices and outlines prospects for further research in the field of derivational semantics.

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Dai, Jiaojian, Peipeng Liang, Xiaohuan Li, et al. "Semantic predictability and semantic relevance through different neural mechanisms to improve memory performance." Brain and Cognition 186 (June 2025): 106300. https://doi.org/10.1016/j.bandc.2025.106300.

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Semenenko, Natalia N. "Axiology of Proverbs in the Focus of the Problem of Cognitive-discursive Modeling of Semantics of Russian Proverbs." RUDN Journal of Language Studies, Semiotics and Semantics 11, no. 2 (2020): 213–32. http://dx.doi.org/10.22363/2313-2299-2020-11-2-213-232.

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The article is devoted to the analysis of the problem of determining the value factor in the semantic structure of proverbs in relation to the problem of cognitive-pragmatic modeling of proverbial semantics. The relevance of the appeal to the value component of paremic semantics is due to the approach to solving the problems of semantics of signs of indirect-derived nomination in the system “Language - text - discourse”, which has already become traditional for anthropocentric linguistics. Paremia is considered as a syncretic sign that combines the properties of a texteme (a language sign) and a statement (a discursive unit). Many features of it’s speech implementation are due to the combination of the properties of a mini-text with a “folded” semantic structure and a discourse unit that contributes to the formation of meaning in it’s space. In the course of determining the essence of the approach to the description of the problem specified linguophilosophical cultural status values, and proposes the author's concept define cognitive-pragmatic status values as linguaitaliana units allocated in the cognitive basis of the values of folk aphorism and implemented in a pragmatic component paramedische values. The description of axiological paremic representation is carried out in line with the cognitive-pragmatic approach to the distinction between axiological and evaluative in the semantic content of proverbs. Grounded in the study of the model description of the axiological accentually pareticheski semantics is illustrated by a thematic group of proverbs and in describing the discursive potential of the proverbs included in the context. The relevance of the study is due to the consideration of paremic semantics as a field of linguosinergic interaction of value and evaluation, expressed in the semantic structure of proverbs.

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Yazdi, Hadi Sadoghi, Malihe Javidi, and Hamid Reza Pourreza. "SVM-based Relevance Feedback for semantic video retrieval." International Journal of Signal and Imaging Systems Engineering 2, no. 3 (2009): 99. http://dx.doi.org/10.1504/ijsise.2009.033722.

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Sun, Kun, and Haitao Liu. "Attention-aware semantic relevance predicting Chinese sentence reading." Cognition 255 (February 2025): 105991. http://dx.doi.org/10.1016/j.cognition.2024.105991.

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Leifman, George, Ron Meir, and Ayellet Tal. "Semantic-oriented 3d shape retrieval using relevance feedback." Visual Computer 21, no. 8-10 (2005): 865–75. http://dx.doi.org/10.1007/s00371-005-0341-z.

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Shajalal, Md, and Masaki Aono. "Coverage-based query subtopic diversification leveraging semantic relevance." Knowledge and Information Systems 62, no. 7 (2020): 2873–91. http://dx.doi.org/10.1007/s10115-020-01470-3.

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Zhao, Ji, Dan Peng, Chuhan Wu, et al. "Incorporating Semantic Similarity with Geographic Correlation for Query-POI Relevance Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1270–77. http://dx.doi.org/10.1609/aaai.v33i01.33011270.

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Point-of-interest (POI) retrieval that searches for relevant destination locations plays a significant role in on-demand ridehailing services. Existing solutions to POI retrieval mainly retrieve and rank POIs based on their semantic similarity scores. Although intuitive, quantifying the relevance of a Query-POI pair by single-field semantic similarity is subject to inherent limitations. In this paper, we propose a novel Query-POI relevance model for effective POI retrieval for ondemand ride-hailing services. Different from existing relevance models, we capture and represent multi-field and local&global semantic features of a Query-POI pair to measure the semantic similarity. Besides, we observe a hidden correlation between origin-destination locations in ride-hailing scenarios, and propose two location embeddings to characterize the specific correlation. By incorporating the geographic correlation with the semantic similarity, our model achieves better performance in POI ranking. Experimental results on two real-world click-through datasets demonstrate the improvements of our model over state-of-the-art methods.

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Demenchuk, O. V. "Lexical Semantic Modelling in Cross-Linguistic Perspective." Scientific Journal of National Pedagogical Dragomanov University. Series 9. Current Trends in Language Development, no. 17 (August 21, 2018): 32–42. http://dx.doi.org/10.31392/npu-nc.series9.2018.17.03.

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The paper focuses on model of situation concept – a representation that is thought to constitute the basis for comparison (tertium comparationis) of lexical semantics in related and non-related languages. The model is considered to represent the content of a lexical item through a sampling of a multidimensional concept. The relevance of the model being a representation of multidimensional semantic space is verified based on the results of the subjective scaling experiment. The concept of situation is considered as a configuration of semantic linguistic dimensions – the characteristics that encode information on how a situation is construed by the semantics of a lexical item. It has been ascertained that the lexical items’ semantics of contrasted languages (English, German, Polish, and Ukrainian) encode and distribute information within four semantic linguistic dimensions: representational, sentential, constructional, and epidigmatic. It is posited that the representational semantic dimension encodes information on the gnoseological values of situation participants, the sentential one – on a set and relations of situation participants, the constructional one – on the degree of positional activities or salience of situation participants, the epidigmatic one – on the semantic associations of situation participants. In order to ascertain the content of a situation concept and to establish the ways the information is encoded and distributed in the semantics of lexical items, a directed associative experiment has been used. In reproducing the specificity of the worldview, the expediency of further psycholinguistic research of lexical semantic models is claimed.

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Mammadov, Eshgin. "MATHEMATİCAL FOUNDATİONS OF SEMANTİC SEARCH İN INTERNET ENGİNES." Deutsche internationale Zeitschrift für zeitgenössische Wissenschaft 77 (April 4, 2024): 47–54. https://doi.org/10.5281/zenodo.10929008.

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The advancement of semantic search algorithms relies heavily on the integration of sophisticated mathematical frameworks to decipher and interpret the semantics of user queries and web documents. This article provides an in-depth exploration of three key mathematical models utilized in semantic search: Vector Space Models (VSM), Latent Semantic Analysis (LSA), and Word Embeddings. Each model is meticulously examined, elucidating their mathematical foundations, operational principles, and integration into semantic search algorit hms. From the mathematical representation of documents and queries in vector space to the application of Singular Value Decomposition (SVD) in uncovering latent semantic structures, the article delves into the intricacies of these models. Furthermore, it explores how Word Embeddings, exemplified by Word2Vec and GloVe, revolutionize semantic understanding through dense vector representations of words. By synthesizing these mathematical frameworks into semantic search algorithms, search engines can bridge the semantic gap between user intent and search results, ultimately enhancing the accuracy, relevance, and user experience of information retrieval. Through this nuanced analysis, the article underscores the indispensable role of mathematics in propelling the evolution of semantic search technology towards more intuitive and efficient information retrieval systems in the digital

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Zhou, Mo, Zhenxing Niu, Le Wang, Zhanning Gao, Qilin Zhang, and Gang Hua. "Ladder Loss for Coherent Visual-Semantic Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 13050–57. http://dx.doi.org/10.1609/aaai.v34i07.7006.

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For visual-semantic embedding, the existing methods normally treat the relevance between queries and candidates in a bipolar way – relevant or irrelevant, and all “irrelevant” candidates are uniformly pushed away from the query by an equal margin in the embedding space, regardless of their various proximity to the query. This practice disregards relatively discriminative information and could lead to suboptimal ranking in the retrieval results and poorer user experience, especially in the long-tail query scenario where a matching candidate may not necessarily exist. In this paper, we introduce a continuous variable to model the relevance degree between queries and multiple candidates, and propose to learn a coherent embedding space, where candidates with higher relevance degrees are mapped closer to the query than those with lower relevance degrees. In particular, the new ladder loss is proposed by extending the triplet loss inequality to a more general inequality chain, which implements variable push-away margins according to respective relevance degrees. In addition, a proper Coherent Score metric is proposed to better measure the ranking results including those “irrelevant” candidates. Extensive experiments on multiple datasets validate the efficacy of our proposed method, which achieves significant improvement over existing state-of-the-art methods.

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Popova, Larisa Georgievna, Lyubov' Mikhailovna Shatilova, and Nadezhda Viktorovna Samarina. "Semantic content of indefinite pronouns in the English literary texts." Litera, no. 10 (October 2021): 148–55. http://dx.doi.org/10.25136/2409-8698.2021.10.36364.

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The goal of this article lies in analysis of the semantics of indefinite pronouns of the English language, which form the core of the semantic field of uncertainty from the perspective of functional linguistics. The subject of this research is the semantic content of English indefinite pronouns in the literary and publicistic texts. The relevance of this work is defined by the lack of works in both domestic and foreign modern linguistics dedicated to indefinite pronouns of the English language. The scientific novelty consists systematization of indefinite pronouns in form of semantic field and determination of possible varieties of uncertainty through the semantics of this group of pronouns in the English literary texts. It is established that in literary texts, the indefinite pronouns of the core of the semantic field of uncertainty of the English language are used for conveying quite very peculiar; however, the key components are uncertainty and generality in evaluation of the unfolding events. Uncertainty can be explicit or conditional. Generality also correlates with uncertainty, leaving out particularities. The absence of particularities is the main semantic indicator of the use of nuclear indefinite pronouns in the literary texts.

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Abdiyeva, Gulnara, Almagul Maimakova, Feruza Yerzhanova, Alla Tsoy, and Lyaila Togzhanova. "About lexical-semantic aspects." XLinguae 15, no. 4 (2022): 58–66. http://dx.doi.org/10.18355/xl.2022.15.04.06.

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The relevance of the topic is determined by the need to deepen and describe the functioning of the inhomogeneous French language in the French-speaking cantons of Switzerland using specific lexical material. The analysis of intra and extralinguistic influences and their reflection in the Franco-Swiss literary version of the language is important for contact linguistics, and its relevance is due to the lack of depth of analysis of this issue. The analyst is particularly interested in the fact that in Switzerland, the French language exists within the legal framework of official multilingualism, in active interaction with three other equal national languages of the Confederation: German, Italian and Romansh. At the same time, the deviations of the French-Swiss national variant from the general French linguistic norm manifest themselves mainly at the lexical and semantic level. The object of the study is the French language in the Swiss Confederation, its lexical, semantic and stylistic characteristics.

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Jia, Bingjing, Chenglong Wang, Haiyan Zhao, and Lei Shi. "An Entity Linking Algorithm Derived from Graph Convolutional Network and Contextualized Semantic Relevance." Symmetry 14, no. 10 (2022): 2060. http://dx.doi.org/10.3390/sym14102060.

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In the era of big data, a large amount of unstructured text data springs up every day. Entity linking involves relating the mentions found in the texts to the corresponding entities, which stand for objective things in the real world, in a knowledge base. This task can help computers understand semantics in the texts correctly. Although there have been numerous approaches employed in research such as this, some challenges are still unresolved. Most current approaches utilize neural models to learn important features of the entity and mention context. However, the topic coherence among the referred entities is frequently ignored, which leads to a clear preference for popular entities but poor accuracy for less popular ones. Moreover, the graph-based models face much noise information and high computational complexity. To solve the problems above, the paper puts forward an entity linking algorithm derived from the asymmetric graph convolutional network and the contextualized semantic relevance, which can make full use of the neighboring node information as well as deal with unnecessary noise in the graph. The semantic vector of the candidate entity is obtained by continuously iterating and aggregating the information from neighboring nodes. The contextualized relevance model is a symmetrical structure that is designed to realize the deep semantic measurement between the mentions and the entities. The experimental results show that the proposed algorithm can fully explore the topology information of the graph and dramatically improve the effect of entity linking compared with the baselines.

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KISELЕVA, LARISA KISELЕVA. "СЕМАНТИЧНАТА ДЕРИВАЦИЯ КАТО ЕДИН ОТ НАЙ-ВАЖНИТЕ ВИДОВЕ ЕЗИКОВИ ИНОВАЦИИ (ВЪРХУ МАТЕРИАЛ ОТ СЪВРЕМЕННИ МЕДИЙНИ ТЕКСТОВЕ) / SEMANTIC DERIVATION AS A MAJOR TYPE OF LANGUAGE INNOVATION (BASED ON CONTEMPORARY MEDIA TEXTS)". Journal of Bulgarian Language 68, PR (2021): 102–11. http://dx.doi.org/10.47810/bl.68.21.pr.07.

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The study focuses on the most productive patterns of semantic derivation, namely metaphorical and metonymic transfer, exemplified by contemporary Russian media texts. The metaphorical and metonymic patterns outlined in the study are based on the principle of anthropocentrism, which is realized in two interrelated directions highlighting the cognitive nature of semantic relations: 1) person → surrounding world (external personalization); 2) surrounding world → person (description in view of the extralinguistic reality). Specific micro patterns are based on the concrete → abstract macro pattern. It can be argued that semantic derivation functions as a means of linguistic ex-pression of ideas about intangible entities, thus creating the respective images in the mind of the addressee. The results of the study are of relevance to lexical semantics, cognitive linguistics, media linguistics, etc. Keywords: semantic derivation, metaphor, metonymy, Russian, media texts, anthropocentrism

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Tsuda, Tetsuya, Hikaru Nakamura, Hiroyo Yoshihata, Masumi Watanabe, Mineko Bouoka, and Norimasa Fujimoto. "Performance of Non-verbal Semantic Decision Tasks Controlling Semantic Relevance in Patients with Aphasia." Higher Brain Function Research 34, no. 4 (2014): 394–400. http://dx.doi.org/10.2496/hbfr.34.394.

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Peng, Lin, Xu-peng Kou, and Lin-nan Yang. "Research on Semantic Information Retrieval Model of Bamboo Rattan Domain Based on Semantic Relevance." Journal of Physics: Conference Series 1187, no. 5 (2019): 052094. http://dx.doi.org/10.1088/1742-6596/1187/5/052094.

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Al-Mohamade, Abeer, Ouiem Bchir, and Mohamed Maher Ben Ismail. "Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning." Journal of Imaging 6, no. 1 (2020): 2. http://dx.doi.org/10.3390/jimaging6010002.

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We propose a novel multiple query retrieval approach, named weight-learner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual feature/descriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the user’s high-level semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database.

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Kalisz, Roman. "A Concept of General Meaning: Selected Theories in Comparison to Selected Semantic and Pragmatic Theories." Research in Language 11, no. 3 (2013): 239–49. http://dx.doi.org/10.2478/v10015-012-0024-6.

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The paper discusses a concept of general meaning with reference to various relevant semantic and pragmatic theories. It includes references to Slavic axiological semantics (e.g. Krzeszowski (1997); Puzynina (1992)), Wierzbicka’s (e.g. 1980, 1987) atomic expressions and classical pragmatics theories, such as speech acts, Gricean theory of conversational implicature, politeness theory and and relevance theory.

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Butakova, L. O. "The Book – a Source of Knowledge? (Subjective Semantics of Lexemes Book, Computer, Tablet, Internet)." Russian language at school 80, no. 4 (2019): 36–44. http://dx.doi.org/10.30515/0131-6141-2019-80-4-36-44.

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The purpose of the article is to model and analyze fragments of the language consciousness of schoolchildren associated with modern and traditional sources of information. The material for the study are the results of associative experiments conducted in several stages in 2015–2018. The author used psycholinguistic method of a free associative experiment with the subsequent construction of the traditional and cognitive structure of the associative field to determine the composition of the resulting components of the incentive semantics. The identification of trends in semantic development was carried out using the method of semantic comparison of the resulting field structures with essays on the themes «If there were no computers and tablets ...», «Read or see?», «If there were no books, then ...». The results of the research are descriptions of cognitive structures of associative fields, identifying a significant number of layers in them, establishing similarities and differences in their semantic composition, creating semantic portraits of stimuli, identifying the leading semantic areas of the texts of the works, which reflect the composition of the actualized components of these fragments. Conclusions: the intersection of the semantic areas of these phenomena with the global categories “knowledge”, “entertainment”, “person”, “information” in schoolchildren’s consciousness, the relevance of meanings ‘development / degradation’, ‘mind / stupidity’; ‘health, benefit / harm’; the stability of the lines of interaction between the semantic areas «Internet», «Computer», «Tablet», «Game», «Information», «Communication», the subjects (who are in friendly and family relations especially); the relevance of the semantic space of the game in the AF «Computer», «Tablet», «Internet», the space of knowledge in the AF «Book»; the age specificity of the composition of the fields, the appearance with different frequency of reactions associated with school, study; depletion of associative fields in recent experiments.

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