Добірка наукової літератури з теми "Semantic analyses"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Semantic analyses".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Semantic analyses"
Manor, Ruth. "Pragmatic considerations in semantic analyses." Pragmatics and Cognition 3, no. 2 (January 1, 1995): 225–45. http://dx.doi.org/10.1075/pc.3.2.03man.
Повний текст джерелаPeng, Yuhai. "Metaphorical Analyses of Russian Verbs of Thinking Activity Meaning." Russian and Chinese Studies 4, no. 3 (November 28, 2020): 244–55. http://dx.doi.org/10.17150/2587-7445.2020.4(3).244-255.
Повний текст джерелаWANG, YINGXU. "ON FORMAL AND COGNITIVE SEMANTICS FOR SEMANTIC COMPUTING." International Journal of Semantic Computing 04, no. 02 (June 2010): 203–37. http://dx.doi.org/10.1142/s1793351x10000833.
Повний текст джерелаGoddard, Cliff, and Anna Wierzbicka. "NSM analyses of the semantics of physical qualities." Studies in Language 31, no. 4 (August 14, 2007): 765–800. http://dx.doi.org/10.1075/sl.31.4.03god.
Повний текст джерелаHall, David Patrick, and Ivano Caponigro. "On the semantics of when-clauses." Semantics and Linguistic Theory, no. 20 (April 3, 2015): 544. http://dx.doi.org/10.3765/salt.v0i20.2566.
Повний текст джерелаHall, David Patrick, and Ivano Caponigro. "On the semantics of when-clauses." Semantics and Linguistic Theory 20 (August 14, 2010): 544. http://dx.doi.org/10.3765/salt.v20i0.2566.
Повний текст джерелаNoble, Jason, Etienne Thoret, Max Henry, and Stephen McAdams. "Semantic Dimensions of Sound Mass Music." Music Perception 38, no. 2 (November 25, 2020): 214–42. http://dx.doi.org/10.1525/mp.2020.38.2.214.
Повний текст джерелаAmaral, Luana Lopes. "Online resources for the syntactic-semantic classification of verbs." Texto Livre 15 (July 6, 2022): e38715. http://dx.doi.org/10.35699/1983-3652.2022.38715.
Повний текст джерелаLocatell, Christian. "Temporal Conjunctions and Their Semantic Extensions: The Case of in Biblical Hebrew." Journal of Semitic Studies 65, no. 1 (2020): 93–115. http://dx.doi.org/10.1093/jss/fgz040.
Повний текст джерелаKaraman, Burcu İlkay. "Semantic Analyses in Forensic Text Types." Bulletin of Legal Medicine 22, no. 3 (October 30, 2017): na. http://dx.doi.org/10.17986/blm.2017332897.
Повний текст джерелаДисертації з теми "Semantic analyses"
Ho, Man-yee. "Trendy expressions in Hong Kong Cantonese morphological, semantic and pragmatic analyses /." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31601029.
Повний текст джерелаHo, Man-yee, and 何敏兒. "Trendy expressions in Hong Kong Cantonese: morphological, semantic and pragmatic analyses." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31601029.
Повний текст джерелаKautz, Oliver [Verfasser]. "Model Analyses Based on Semantic Differencing and Automatic Model Repair / Oliver Kautz." Düren : Shaker, 2021. http://d-nb.info/1233548298/34.
Повний текст джерелаKossmann, Bianca. "Rich and poor in the history of English: corpus-based analyses of lexico-semantic variation and change in Old and Middle English." [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:bsz:25-opus-46897.
Повний текст джерелаZoltan, Kazi. "Ontološki zasnovana analiza semantičke korektnosti modela podataka primenom sistema automatskog rezonovanja." Phd thesis, Univerzitet u Novom Sadu, Tehnički fakultet Mihajlo Pupin u Zrenjaninu, 2014. https://www.cris.uns.ac.rs/record.jsf?recordId=85033&source=NDLTD&language=en.
Повний текст джерелаWork presents a theoretical study and analysis of existing theories and solutions in the area of data model validation and quality checking. It is created a theoretical model of ontology based analysis of data model semantic correctness by applying automated reasoning system which is practicaly implemented and confirmed by the conducted experimental research. A software application is developed for data model formalization and ontology mapping in Prolog clauses form. Reasoning rules are formed the in first-order predicate logic, which are integrated with the data model and domain ontology. Semantic correctness of the data model is checked with queries within Prolog system. Metrics of ontological quality of the data model are defined which are based on automated reasoning system replies.
Malmqvist, Anita. "Sparsamkeit und Geiz, Grosszügigkeit und Verschwendung : ethische Konzepte im Spiegel der Sprache." Doctoral thesis, Umeå universitet, Moderna språk, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-61584.
Повний текст джерелаdigitalisering@umu
Gao, Boyang. "Contributions to music semantic analysis and its acceleration techniques." Thesis, Ecully, Ecole centrale de Lyon, 2014. http://www.theses.fr/2014ECDL0044/document.
Повний текст джерелаDigitalized music production exploded in the past decade. Huge amount of data drives the development of effective and efficient methods for automatic music analysis and retrieval. This thesis focuses on performing semantic analysis of music, in particular mood and genre classification, with low level and mid level features since the mood and genre are among the most natural semantic concepts expressed by music perceivable by audiences. In order to delve semantics from low level features, feature modeling techniques like K-means and GMM based BoW and Gaussian super vector have to be applied. In this big data era, the time and accuracy efficiency becomes a main issue in the low level feature modeling. Our first contribution thus focuses on accelerating k-means, GMM and UBM-MAP frameworks, involving the acceleration on single machine and on cluster of workstations. To achieve the maximum speed on single machine, we show that dictionary learning procedures can elegantly be rewritten in matrix format that can be accelerated efficiently by high performance parallel computational infrastructures like multi-core CPU, GPU. In particular with GPU support and careful tuning, we have achieved two magnitudes speed up compared with single thread implementation. Regarding data set which cannot fit into the memory of individual computer, we show that the k-means and GMM training procedures can be divided into map-reduce pattern which can be executed on Hadoop and Spark cluster. Our matrix format version executes 5 to 10 times faster on Hadoop and Spark clusters than the state-of-the-art libraries. Beside signal level features, mid-level features like harmony of music, the most natural semantic given by the composer, are also important since it contains higher level of abstraction of meaning beyond physical oscillation. Our second contribution thus focuses on recovering note information from music signal with musical knowledge. This contribution relies on two levels of musical knowledge: instrument note sound and note co-occurrence/transition statistics. In the instrument note sound level, a note dictionary is firstly built i from Logic Pro 9. With the musical dictionary in hand, we propose a positive constraint matching pursuit (PCMP) algorithm to perform the decomposition. In the inter-note level, we propose a two stage sparse decomposition approach integrated with note statistical information. In frame level decomposition stage, note co-occurrence probabilities are embedded to guide atom selection and to build sparse multiple candidate graph providing backup choices for later selections. In the global optimal path searching stage, note transition probabilities are incorporated. Experiments on multiple data sets show that our proposed approaches outperform the state-of-the-art in terms of accuracy and recall for note recovery and music mood/genre classification
Krull, Kirsten. "Lieber Gott, mach mich fromm ... : Zum Wort und Konzept “fromm” im Wandel der Zeit." Doctoral thesis, Umeå : Institutionen för moderna språk, Umeå univ, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-286.
Повний текст джерелаDang, Qinran. "Brouillard de pollution en Chine. Analyse sémantique différentielle de corpus institutionnels, médiatiques et de microblogues." Thesis, Paris, INALCO, 2020. http://www.theses.fr/2020INAL0009.
Повний текст джерелаAir pollution has increasingly become a serious problem in China, more and more journalistic articles and miniblogs (weibo in Chinese, equivalent to tweet), comming from governmental or media websites, social networks, blogs and forums, etc., discuss the issue of «雾 霾» (wumai in Chinese, means smog) in China through several angles : political, ecological, economic, sociological, health, etc. The semantics of the themes adressed in these texts differ significantly from each other according to their textual genre. In the framework of our research, our objectif is double-fold : on the one hand, to identify different themes of a digital propose-bulit corpus relating to wumai ; and on the other hand, to interpret differentially the semantics of these themes. Firstly, we collect the textual data written in chinese and related to wumai. These journalistic articles and weibo deriving from three traditional chinese and the social network are divided into four genres of sub-corpus. Secondly, we constitute our corpus through a series of data processing : data cleaning, word segmentation, normalization, POS tagging, benchmarking and data organization. We study the characteristics of the four genres of sub-corpus through a series of discriminating variables - hyperstructural, lexical, semiotic, rhetorical, modal and syntactic - distributed at the infratextual and intratextual level. After that, based on the characteristics of each textual genre, we identify the main themes exposed in each genre of sub-corpus, and analyze the semantics of these identified themes in a contrastive way. Our analysis results are interpreted from two angles : quantitative and qualitative. All statistical analysis are assisted by textometric tools ; and the semantic interpretations are implemented on several fundamental concepts of SI (Sémantique interprétative) proposed by Rastier (1987)
Steinmetz, Nadine. "Context-aware semantic analysis of video metadata." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2014/7055/.
Повний текст джерелаThe Semantic Web provides information contained in the World Wide Web as machine-readable facts. In comparison to a keyword-based inquiry, semantic search enables a more sophisticated exploration of web documents. By clarifying the meaning behind entities, search results are more precise and the semantics simultaneously enable an exploration of semantic relationships. However, unlike keyword searches, a semantic entity-focused search requires that web documents are annotated with semantic representations of common words and named entities. Manual semantic annotation of (web) documents is time-consuming; in response, automatic annotation services have emerged in recent years. These annotation services take continuous text as input, detect important key terms and named entities and annotate them with semantic entities contained in widely used semantic knowledge bases, such as Freebase or DBpedia. Metadata of video documents require special attention. Semantic analysis approaches for continuous text cannot be applied, because information of a context in video documents originates from multiple sources possessing different reliabilities and characteristics. This thesis presents a semantic analysis approach consisting of a context model and a disambiguation algorithm for video metadata. The context model takes into account the characteristics of video metadata and derives a confidence value for each metadata item. The confidence value represents the level of correctness and ambiguity of the textual information of the metadata item. The lower the ambiguity and the higher the prospective correctness, the higher the confidence value. The metadata items derived from the video metadata are analyzed in a specific order from high to low confidence level. Previously analyzed metadata are used as reference points in the context for subsequent disambiguation. The contextually most relevant entity is identified by means of descriptive texts and semantic relationships to the context. The context is created dynamically for each metadata item, taking into account the confidence value and other characteristics. The proposed semantic analysis follows two hypotheses: metadata items of a context should be processed in descendent order of their confidence value, and the metadata that pertains to a context should be limited by content-based segmentation boundaries. The evaluation results support the proposed hypotheses and show increased recall and precision for annotated entities, especially for metadata that originates from sources with low reliability. The algorithms have been evaluated against several state-of-the-art annotation approaches. The presented semantic analysis process is integrated into a video analysis framework and has been successfully applied in several projects for the purpose of semantic video exploration of videos.
Книги з теми "Semantic analyses"
Park, Young Gil. Semantic analyses for storage management optimizations in functional language implementations. New York: Courant Institute of Mathematical Sciences, New York University, 1992.
Знайти повний текст джерелаSeshadri, Venkatadri. Concurrent Semantic Analysis. Toronto: Computer Systems Research Institute, University of Toronto, 1988.
Знайти повний текст джерелаMinker, Wolfgang. Stochastically-based semantic analysis. Boston: Kluwer Academic, 1999.
Знайти повний текст джерелаMinker, Wolfgang, Alex Waibel, and Joseph Mariani. Stochastically-Based Semantic Analysis. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5255-0.
Повний текст джерелаMinker, Wolfgang. Stochastically-based semantic analysis. New York: Springer Science+Business Media, 1999.
Знайти повний текст джерелаJanáková, Martina, Matěj Kos, Michaela Koutská, Veronika Kubecová, Tereza Nahodilová, Lucie Petreková, Anastasia Syrota, et al. Já z hvězd svou moudrost nevyčet… Edited by Alice Jedličková and Stanislava Fedrová. Brno: Masaryk University Press, 2021. http://dx.doi.org/10.5817/cz.muni.m210-8469-2021.
Повний текст джерелаGoswami, Bijoya. The metaphor, a semantic analysis. Calcutta: Sanskrit Pustak Bhandar, 1992.
Знайти повний текст джерелаGoddard, Cliff. Semantic analysis: A practical introduction. 2nd ed. Oxford: Oxford University Press, 2011.
Знайти повний текст джерелаSemantic analysis: A practical introduction. Oxford [U.K.]: Oxford University Press, 1998.
Знайти повний текст джерелаD, Pittelman Susan, ed. Semantic feature analysis: Classroom applications. Newark, Del: International Reading Association, 1991.
Знайти повний текст джерелаЧастини книг з теми "Semantic analyses"
Landragin, Frédéric. "Semantic Analyses and Representations." In Man-Machine Dialogue, 75–94. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118578681.ch5.
Повний текст джерелаTiddi, Ilaria, Daniel Balliet, and Annette ten Teije. "Fostering Scientific Meta-analyses with Knowledge Graphs: A Case-Study." In The Semantic Web, 287–303. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49461-2_17.
Повний текст джерелаKarmacharya, Ashish, Christophe Cruz, Frank Boochs, and Franck Marzani. "Use of Geospatial Analyses for Semantic Reasoning." In Knowledge-Based and Intelligent Information and Engineering Systems, 576–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15387-7_61.
Повний текст джерелаGulden, Jens. "Semantic Support for Visual Data Analyses in Electronic Commerce Settings." In HCI in Business, Government, and Organizations: eCommerce and Innovation, 198–209. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39396-4_18.
Повний текст джерелаHertogh, C. P. "Semantic Analyses of Hilary Putnam’s Twin Earth (Ft. TE Diagram)." In Integrated Science, 501–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94651-7_24.
Повний текст джерелаCiavotta, Michele, Vincenzo Cutrona, Flavio De Paoli, Nikolay Nikolov, Matteo Palmonari, and Dumitru Roman. "Supporting Semantic Data Enrichment at Scale." In Technologies and Applications for Big Data Value, 19–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-78307-5_2.
Повний текст джерелаKhelif, Khaled, Rose Dieng-Kuntz, and Pascal Barbry. "Semantic Web Technologies for Interpreting DNA Microarray Analyses: The MEAT System." In Lecture Notes in Computer Science, 148–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11581062_12.
Повний текст джерелаEl Ghali, Btihal, Abderrahim El Qadi, Mohamed Ouadou, and Driss Aboutajdine. "Context-Based Query Expansion Method for Short Queries Using Latent Semantic Analyses." In Networked Systems, 468–73. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26850-7_33.
Повний текст джерелаGailing. "The Analyses of the E-Government Service Portal Based on the Semantic WEB." In Lecture Notes in Electrical Engineering, 481–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-26001-8_63.
Повний текст джерелаSchmidt-Schauß, Manfred. "Concurrent Programming Languages and Methods for Semantic Analyses (Extended Abstract of Invited Talk)." In Lecture Notes in Computer Science, 21–30. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08918-8_2.
Повний текст джерелаТези доповідей конференцій з теми "Semantic analyses"
Selvaraj, Ganesh, Christof Lutteroth, and Gerald Weber. "Efficient and Standardised Program Analyses using Semantic Methodologies." In 2021 IEEE 15th International Conference on Semantic Computing (ICSC). IEEE, 2021. http://dx.doi.org/10.1109/icsc50631.2021.00018.
Повний текст джерелаBarbuta, Ion. "Verb Semantics and Actantial Structures in Romanian." In Conferință științifică internațională "Filologia modernă: realizări şi perspective în context european". “Bogdan Petriceicu-Hasdeu” Institute of Romanian Philology, Republic of Moldova, 2022. http://dx.doi.org/10.52505/filomod.2022.16.19.
Повний текст джерелаSelvaraj, Ganesh, Gerald Weber, and Christof Lutteroth. "Efficient Program Analyses Using Deductive and Semantic Methodologies." In 2017 IEEE 13th International Conference on e-Science (e-Science). IEEE, 2017. http://dx.doi.org/10.1109/escience.2017.61.
Повний текст джерелаGrif, Michael, and Yuliya Manueva. "Analyses of computer Russian sign language translation system with implemented semantic analyses unit." In 2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). IEEE, 2017. http://dx.doi.org/10.1109/sibircon.2017.8109874.
Повний текст джерелаBugheşiu, Alina. "Translating film titles: between language conversion and name coinage." In International Conference on Onomastics “Name and Naming”. Editura Mega, 2022. http://dx.doi.org/10.30816/iconn5/2019/73.
Повний текст джерелаZhan, Qin, Deren Li, Xia Zhang, and Yu Xia. "Ontology-based geographic information semantic metadata integration." In Geoinformatics 2008 and Joint Conference on GIS and Built environment: Advanced Spatial Data Models and Analyses, edited by Lin Liu, Xia Li, Kai Liu, and Xinchang Zhang. SPIE, 2009. http://dx.doi.org/10.1117/12.813113.
Повний текст джерелаGuiu, Jordi Maja. "Using latent semantic analyses and propositionalist methods in text comprehension." In 2017 Computing Conference. IEEE, 2017. http://dx.doi.org/10.1109/sai.2017.8252102.
Повний текст джерелаGrif, Michael, and Yuliya Manueva. "Semantic analyses of text to translate to Russian sign language." In 2016 11th International Forum on Strategic Technology (IFOST). IEEE, 2016. http://dx.doi.org/10.1109/ifost.2016.7884107.
Повний текст джерелаAkba, Firat, Ihsan Tolga Medeni, Mehmet Serdar Guzel, and Iman Askerzade. "Assessment of Iterative Semi-Supervised Feature Selection Learning for Sentiment Analyses: Digital Currency Markets." In 2020 IEEE 14th International Conference on Semantic Computing (ICSC). IEEE, 2020. http://dx.doi.org/10.1109/icsc.2020.00088.
Повний текст джерелаRong, V., and L. Jiang. "The semantic network and social network analyses of Vancl's micro-blog." In 2011 International Conference on Advanced Intelligence and Awareness Internet (AIAI 2011). IET, 2011. http://dx.doi.org/10.1049/cp.2011.1487.
Повний текст джерелаЗвіти організацій з теми "Semantic analyses"
Білоконенко, Л. А. Semantic integrity of overtext of conflict. Vědecko vydavatelské centrum «Sociosféra-CZ», 2014. http://dx.doi.org/10.31812/0564/1811.
Повний текст джерелаAIR FORCE TEST BED WRIGHT-PATTERSON AFB OH. The Semantic Analyzer. Fort Belvoir, VA: Defense Technical Information Center, April 1991. http://dx.doi.org/10.21236/ada312762.
Повний текст джерелаZelenskyi, Arkadii A. Relevance of research of programs for semantic analysis of texts and review of methods of their realization. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2884.
Повний текст джерелаMaddox III, William H. Incremental Static Semantic Analysis. Fort Belvoir, VA: Defense Technical Information Center, May 1997. http://dx.doi.org/10.21236/ada604432.
Повний текст джерелаCai, Jiazhen. A Language for Semantic Analysis. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada453254.
Повний текст джерелаLee, Hyun-Jung, HyunJu Shin, Kyu-Hye Lee, Seulah Lee, and Ye-Jin In. Semantic Network Analysis of Gorpcore. Ames (Iowa): Iowa State University. Library, January 2019. http://dx.doi.org/10.31274/itaa.8218.
Повний текст джерелаWoodbridge, Diane, and Randolph Brost. Geospatial-Temporal Semantic Graph Evaluation for Induced Seismicity Analysis. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1562818.
Повний текст джерелаHendrickson, Bruce Alan. Algorithms and architectures for high performance analysis of semantic graphs. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/974408.
Повний текст джерелаSparks, Randall, and Rex Hartson. The Software Therapist: Usability Problem Diagnosis Through Latent Semantic Analysis. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada458771.
Повний текст джерелаShapovalov, Yevhenii B., Viktor B. Shapovalov, Roman A. Tarasenko, Stanislav A. Usenko, and Adrian Paschke. A semantic structuring of educational research using ontologies. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4433.
Повний текст джерела