Academic literature on the topic 'Topic hierarchy'
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Journal articles on the topic "Topic hierarchy"
Zhou, Xue Mei, and Shan Ying Cheng. "Hierarchy Topic Detection and Hot Topic Identification." Applied Mechanics and Materials 701-702 (December 2014): 180–86. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.180.
Full textXue, Han, Bing Qin, Ting Liu, and Shen Liu. "Topic hierarchy construction from heterogeneous evidence." Frontiers of Computer Science 10, no. 1 (September 8, 2015): 136–46. http://dx.doi.org/10.1007/s11704-015-4548-5.
Full textKim, Han-joon, and Sang-goo Lee. "Building topic hierarchy based on fuzzy relations." Neurocomputing 51 (April 2003): 481–86. http://dx.doi.org/10.1016/s0925-2312(02)00726-9.
Full textPan, Tao, Qian Chen, Dong Dong Lv, Shu Han Yuan, and Xin Jin. "Study of Topic Life Cycle Based on Hierarchical HMM." Applied Mechanics and Materials 687-691 (November 2014): 1324–27. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1324.
Full textKoltcov, Sergei, Vera Ignatenko, Maxim Terpilovskii, and Paolo Rosso. "Analysis and tuning of hierarchical topic models based on Renyi entropy approach." PeerJ Computer Science 7 (July 29, 2021): e608. http://dx.doi.org/10.7717/peerj-cs.608.
Full textPradhan, Ligaj, Chengcui Zhang, and Steven Bethard. "Extracting Hierarchy of Coherent User-Concerns to Discover Intricate User Behavior from User Reviews." International Journal of Multimedia Data Engineering and Management 7, no. 4 (October 2016): 63–80. http://dx.doi.org/10.4018/ijmdem.2016100104.
Full textLiu, Shen, Bing Qin, Ting Liu, and Han Xue. "Tag recommendation based on topic hierarchy of folksonomy." International Journal of Computational Science and Engineering 20, no. 1 (2019): 49. http://dx.doi.org/10.1504/ijcse.2019.10024805.
Full textXue, Han, Bing Qin, Ting Liu, and Shen Liu. "Tag recommendation based on topic hierarchy of folksonomy." International Journal of Computational Science and Engineering 20, no. 1 (2019): 49. http://dx.doi.org/10.1504/ijcse.2019.103249.
Full textHoque, Enamul, and Giuseppe Carenini. "Interactive topic hierarchy revision for exploring a collection of online conversations." Information Visualization 18, no. 3 (February 23, 2018): 318–38. http://dx.doi.org/10.1177/1473871618757228.
Full textChen, Jing, Tian Tian Wang, and Quan Lu. "THC-DAT: a document analysis tool based on topic hierarchy and context information." Library Hi Tech 34, no. 1 (March 21, 2016): 64–86. http://dx.doi.org/10.1108/lht-07-2015-0074.
Full textDissertations / Theses on the topic "Topic hierarchy"
Knoll, Jonathan Corey. "PROLONGATION, EXPANDING VARIATION, AND PITCH HIERARCHY: A STUDY OF FRED LERDAHL'S WAVES AND COFFIN HOLLOW." Bowling Green State University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1162851214.
Full textSkwarcan-Bidakowski, Alexander. "Exotic Decays of a Vector-liketop Partner at the LHC." Thesis, Uppsala universitet, Högenergifysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392988.
Full textFan, Tiffany, and 范瓊文. "Topic Concept-Hierarchy Model:Concept-Based Search." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/15936346605349514248.
Full text國立中央大學
網路學習科技研究所
93
In viewpoint of the academic, Information Retrieval method is used to facilitate content search in a library environment. In a library, librarian needs to establish description information of digital content or physical content before stored. The description information will stored into repository including title, authors, published date, publisher, abstract, category, terms and the contents. Therefore, retrieval process is implemented based on comparison between user’s query and repository. In general, keyword matching is a common approach in information retrieval research. However, this approach can not always brings a lot of all relevant information. The main reason cause this result is that people may use different words to access a specified information. Therefore, the recall performance of keyword-match is poor. In our study, we proposed Topic/ Domain Concept-Hierarchy Model to transform domain knowledge into hierarchical category in a domain hierarchy. Each category is a concept node and has corresponding content set. The represented keyword of node is extracted from content set. The matching is executed in the domain hierarchy to compute the similarity between user’s query and keywords in domain hierarchy. If matched, it means user intend to browse corresponding content set. The Node is call relevant conceptual node (RCN) and its bellow nodes are relevant conceptual sub-node (RCS). Experiment result shows the proposed Topic/ Domain Concept-Hierarchy Model can be applied to information retrieval effectively. The recall and precision has been significantly improved comparison with traditional method. The responded result is ranked followed the correlation in domain hierarchy. In this way, users can retrieval suitable material in a short time.
"Website summarization: a topic hierarchy based approach." 2006. http://library.cuhk.edu.hk/record=b5893077.
Full textThesis (M.Phil.)--Chinese University of Hong Kong, 2006.
Includes bibliographical references (leaves 84-88).
Abstracts in English and Chinese.
Abstract --- p.1
Acknowledgements --- p.3
Contents --- p.4
List of Figures --- p.6
List of Tables --- p.7
Chapter Chapter 1 --- Introduction --- p.8
Chapter Chapter 2 --- Related Work --- p.12
Chapter 2.1 --- Web Structure Mining --- p.12
Chapter 2.1.1 --- HITS Algorithm --- p.13
Chapter 2.1.2 --- PageRank Algorithm --- p.13
Chapter 2.2 --- Website Mining --- p.14
Chapter 2.2.1 --- Website Classification --- p.14
Chapter 2.2.2 --- Web Unit Mining --- p.16
Chapter 2.2.3 --- Logical Domain Extraction --- p.16
Chapter 2.2.4 --- Web Thesaurus Construction --- p.17
Chapter Chapter 3 --- Website Topic Hierarchy Generation --- p.19
Chapter 3.1 --- Problem Definition --- p.19
Chapter 3.2 --- Graph Based Algorithms --- p.21
Chapter 3.2.1 --- Breadth First Search --- p.21
Chapter 3.2.2 --- Shortest Path Search --- p.23
Chapter 3.2.3 --- Minimum Directed Spanning Tree --- p.24
Chapter 3.2.4 --- Discussion --- p.27
Chapter 3.3 --- Edge Weight Function --- p.28
Chapter 3.3.1 --- Relevance Method --- p.29
Chapter 3.3.2 --- Machine Learning Method --- p.32
Chapter 3.4 --- Experiments --- p.47
Chapter 3.4.1 --- Data Preparation --- p.47
Chapter 3.4.2 --- Performances of Breadth-first Search --- p.50
Chapter 3.4.3 --- Performances of Shortest-path Search --- p.50
Chapter 3.4.4 --- Performances of Directed Minimum Spanning Tree --- p.54
Chapter 3.4.5 --- Comparison of Different Algorithms --- p.55
Chapter Chapter 4 --- Website Summarization Through Keyphrase Extraction --- p.58
Chapter 4.1 --- Introduction --- p.58
Chapter 4.2 --- Background --- p.60
Chapter 4.3 --- Keyphrase Extraction --- p.69
Chapter 4.3.1 --- Candidate Phrases Idenfication --- p.69
Chapter 4.3.2 --- Feature Calculation without Topic Hierarchy --- p.70
Chapter 4.3.3 --- Feature Calculation with Topic Hierarchy --- p.72
Chapter 4.3.4 --- Extraction of Keyphrases --- p.75
Chapter 4.4 --- Experiments --- p.76
Chapter Chapter 5 --- Conclusion and Future Work --- p.82
References: --- p.84
Chen, Leng-chin, and 陳泠謹. "Applying Feature Extraction Technique to Build Topic Hierarchy Model for News Topic Chronicle." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/62559068987940080161.
Full text國立雲林科技大學
資訊管理系碩士班
95
The news reports have become an important way to acquire knowledge. In order to solve multiple topics problem, this study proposed a feature extraction technique to automatically construct a topic hierarchy. The fundamental idea of this technique is to extract a set of common features from the event clusters for topics. In addition, this study proposed a novel presentation of news information which is named Topic Chronicle. We employed the multi-document summarization technique to generate Topic Chronicle and provided person-oriented and event-oriented aspects of topic hierarchy for readers that they could choose a suitable topic according to their need. The information quality experiment showed that the average F-measure of person-oriented topic is 0.78 and the average F-measure of event-oriented topic is 0.53. The system quality experiment suggested that more than 70% of participants agreed that the quality of Topic Chronicle System is satisfactory. Topic Chronicle could expound the connotation of topic development to help readers understand the overall scenario.
Chang, Chi-Feng, and 張啟峰. "Design and Evaluation of Algorithms for Topic Hierarchy Integration." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/02471981001283162993.
Full text國立中正大學
資訊工程研究所
90
In this thesis, we study the problem of integrating documents from different sources into a comprehensive topic hierarchy. Our objective is to develop efficient techniques that improve the accuracy of traditional categorization methods by incorporating categorization information provided by data sources into categorization process. Notice that in the World-Wide Web, categorization information is often available from information sources. For example, news from newspapers, books from publishers, items from electronic commercial sites, or even web pages archived by web information portals are categorized. Observe that many of the topic hierarchies adopted by current information sources are highly related. We believe that categorization information can be used to improve classification accuracy. We present several techniques that explore relations between topic hierarchies and incorporate categorization information from source hierarchies into traditional classification methods such as Baysian methods and support vector machines. Experiment on collections from Openfind and Yam, and Google and Yahoo, well-known popular web sites in Taiwan and USA, respectively, shows that incorporating categorization information from source hierarchies can significantly improve the classification accuracy.
Chou, Li-Ling, and 周麗玲. "Topic Hierarchy Generation Based on Anchor Text and Term-correlation." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/65841329574921528897.
Full text國立臺灣科技大學
資訊工程系
93
As Internet booms prosperously, there is various information available for user to obtain, such as new technique information and course contents for instance. It has become an important task to provide "Topic Directory Query" Service in order to help users understanding relevant subtopics of their interested techniques within a short period of time. In this thesis, we propose an approach that utilizes Anchor Text and Term-correlation technique to construct and generate topic hierarchy, in order to facilitate users search effectively and efficiently the scope of their interested topics that differs from manually constructed topic hierarchy, such as Open Directory Project or Yahoo Web Directory for instance. In our experiment analysis results, our proposed approach was proved to be effective in searching relevant hierarchical subtopics, especially those topics that cannot be found from manually constructed topic directory search engine mentioned previously but can be found in our system. Therefore, with regard to "Topic Directory Query" Service, there are still many issues need to be resolved, such as precision rate enhancement and new topic detection. However, we still hope that learning new techniques for everyone will never be a troublesome problem. Furthermore, by promoting the concept of topic hierarchy generation, we hope the issues mentioned previously can be researched continuously.
(14030507), Deepani B. Guruge. "Effective document clustering system for search engines." Thesis, 2008. https://figshare.com/articles/thesis/Effective_document_clustering_system_for_search_engines/21433218.
Full textPeople use web search engines to fill a wide variety of navigational, informational and transactional needs. However, current major search engines on the web retrieve a large number of documents of which only a small fraction are relevant to the user query. The user then has to manually search for relevant documents by traversing a topic hierarchy, into which a collection is categorised. As more information becomes available, it becomes a time consuming task to search for required relevant information.
This research develops an effective tool, the web document clustering (WDC) system, to cluster, and then rank, the output data obtained from queries submitted to a search engine, into three pre-defined fuzzy clusters. Namely closely related, related and not related. Documents in closely related and related documents are ranked based on their context.
The WDC output has been compared against document clustering results from the Google, Vivisimo and Dogpile systems as these where considered the best at the fourth Search Engine Awards [24]. Test data was from standard document sets, such as the TREC-8 [118] data files and the Iris database [38], or 3 from test text retrieval tasks, "Latex", "Genetic Algorithms" and "Evolutionary Algorithms". Our proposed system had as good as, or better results, than that obtained by these other systems. We have shown that the proposed system can effectively and efficiently locate closely related, related and not related, documents among the retrieved document set for queries submitted to a search engine.
We developed a methodology to supply the user with a list of keywords filtered from the initial search result set to further refine the search. Again we tested our clustering results against the Google, Vivisimo and Dogpile systems. In all cases we have found that our WDC performs as well as, or better than these systems.
The contributions of this research are:
- A post-retrieval fuzzy document clustering algorithm that groups documents into closely related, related and not related clusters. This algorithm uses modified fuzzy c-means (FCM) algorithm to cluter documents into predefined intelligent fuzzy clusters and this approach has not been used before.
- The fuzzy WDC system satisfies the user's information need as far as possible by allowing the user to reformulate the initial query. The system prepares an initial word list by selecting a few characteristics terms of high frequency from the first twenty documents in the initial search engine output. The user is then able to use these terms to input a secondary query. The WDC system then creates a second word list, or the context of the user query (COQ), from the closely related documents to provide training data to refine the search. Documents containing words with high frequency from the training list, based on a pre-defined threshold value, are then presented to the user to refine the search by reformulating the query. In this way the context of the user query is built, enabling the user to learn from the keyword list. This approach is not available in current search engine technology.
- A number of modifications were made to the FCM algorithm to improve its performance in web document clustering. A factor swkq is introduced into the membership function as a measure of the amount of overlaping between the components of the feature vector and the cluster prototype. As the FCM algorithm is greatly affected by the values used to initialise the components of cluster prototypes a machine learning approach, using an Evolutionary Algorithm, was used to resolve the initialisation problem.
- Experimental results indicate that the WDC system outperformed Google, Dogpile and the Vivisimo search engines. The post-retrieval fuzzy web document clustering algorithm designed in this research improves the precision of web searches and it also contributes to the knowledge of document retrieval using fuzzy logic.
- A relational data model was used to automatically store data output from the search engine off-line. This takes the processing of data of the Internet off-line, saving resources and making better use of the local CPU.
- This algorithm uses Latent Semantic Indexing (LSI) to rank documents in the closely related and related clusters. Using LSI to rank document is wellknown, however, we are the first to apply it in the context of ranking closely related documents by using COQ to form the term x document matrix in LSI, to obtain better ranking results.
- Adjustments based on document size are proposed for dealing with problems associated with varying document size in the retrieved documents and the effect this has on cluster analysis.
Books on the topic "Topic hierarchy"
De Zordo, Ornella, and Fiorenzo Fantaccini, eds. altri canoni / canoni altri. Florence: Firenze University Press, 2011. http://dx.doi.org/10.36253/978-88-6453-012-3.
Full textKalaichelvi, Dr K., and Sowmya K. A HANDBOOK OF E-BUSINESS AND E-COMMERCE. KAAV PUBLICATIONS, DELHI, 2022. http://dx.doi.org/10.52458/9789391842192.2022.tb.
Full textMeeker, John D. Occupational and Environmental Hygiene. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190662677.003.0008.
Full textIrving, John. Performing Topics in Mozart’s Chamber Music with Piano. Edited by Danuta Mirka. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199841578.013.0021.
Full textSher, George. Me, You, Us. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190660413.001.0001.
Full textEpstein, Lee, and Jack Knight. The Economic Analysis of Judicial Behavior. Edited by Lee Epstein and Stefanie A. Lindquist. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199579891.013.24.
Full textCraig, Paul, and Gráinne de Búrca. EU Law. Oxford University Press, 2020. http://dx.doi.org/10.1093/he/9780198856641.001.0001.
Full textCraig, Paul, and Gráinne de Búrca. EU Law. Oxford University Press, 2020. http://dx.doi.org/10.1093/he/9780198859840.001.0001.
Full textBook chapters on the topic "Topic hierarchy"
Tiun, Sabrina, Rosni Abdullah, and Tang Enya Kong. "Automatic Topic Identification Using Ontology Hierarchy." In Computational Linguistics and Intelligent Text Processing, 444–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44686-9_43.
Full textZhu, Jiahui, Xuhui Li, Min Peng, Jiajia Huang, Tieyun Qian, Jimin Huang, Jiping Liu, Ri Hong, and Pinglan Liu. "Coherent Topic Hierarchy: A Strategy for Topic Evolutionary Analysis on Microblog Feeds." In Web-Age Information Management, 70–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21042-1_6.
Full textGelbukh, Alexander, Grigori Sidorov, and Adolfo Guzman-Arénas. "Use of a Weighted Topic Hierarchy for Document Classification." In Text, Speech and Dialogue, 133–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48239-3_24.
Full textButler, Alastair, Chidori Nakamura, and Kei Yoshimoto. "Topic/Subject Coreference in the Hierarchy of Japanese Complex Sentences." In New Frontiers in Artificial Intelligence, 119–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00609-8_12.
Full textClinton, David. "Topic 104: Devices, Linux Filesystems, and the Filesystem Hierarchy Standard." In Practical LPIC-1 Linux Certification Study Guide, 53–72. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2358-1_4.
Full textDrissi, Amani, Ahmed Khemiri, Salma Sassi, Anis Tissaoui, Richard Chbeir, and Abderrazek Jemai. "LDA+: An Extended LDA Model for Topic Hierarchy and Discovery." In Recent Challenges in Intelligent Information and Database Systems, 14–26. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8234-7_2.
Full textAbramowicz, Witold, Tomasz Kaczmarek, and Marek Kowalkiewicz. "Automatic Topic Map Creation Using Term Crawling and Clustering Hierarchy Projection." In Constructing the Infrastructure for the Knowledge Economy, 555–67. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-4852-9_42.
Full textSakai, Yuta, Yui Matsuoka, and Masayuki Goto. "Purchasing Behavior Analysis Model that Considers the Relationship Between Topic Hierarchy and Item Categories." In Social Computing and Social Media: Applications in Education and Commerce, 344–58. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05064-0_26.
Full textDe Luca Tamajo, Raffaele. "Valori e tecniche in tema di disciplina dei poteri datoriali." In Studi e saggi, 177–81. Florence: Firenze University Press, 2022. http://dx.doi.org/10.36253/978-88-5518-484-7.12.
Full textReus, Bernhard. "Hierarchy Theorems." In Undergraduate Topics in Computer Science, 183–94. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27889-6_15.
Full textConference papers on the topic "Topic hierarchy"
Martinez Seis, Bella, and Xiaoou Li. "Topic hierarchy in social networks." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6974217.
Full textLiu, Nan, and C. Yang. "Mining web site's topic hierarchy." In Special interest tracks and posters of the 14th international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1062745.1062828.
Full textChoi, Ikkyu, and Minkoo Kim. "Topic distillation using hierarchy concept tree." In the 26th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/860435.860506.
Full textSmet, Win De, Win De Smet, Marie-Francine Moens, and Marie-Francine Moens. "Generating a Topic Hierarchy from Dialect Texts." In 18th International Conference on Database and Expert Systems Applications (DEXA 2007). IEEE, 2007. http://dx.doi.org/10.1109/dexa.2007.149.
Full textSmet, Win De, Win De Smet, Marie-Francine Moens, and Marie-Francine Moens. "Generating a Topic Hierarchy from Dialect Texts." In 18th International Conference on Database and Expert Systems Applications (DEXA 2007). IEEE, 2007. http://dx.doi.org/10.1109/dexa.2007.4312895.
Full textGelbukh, A., G. Sidorov, and A. Guzman-Arenas. "Document comparison with a weighted topic hierarchy." In Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99. IEEE, 1999. http://dx.doi.org/10.1109/dexa.1999.795247.
Full textWalha, Afef, Faiza Ghozzi, and Faïez Gargouri. "ETL4Social-Data: Modeling Approach for Topic Hierarchy." In 9th International Conference on Knowledge Engineering and Ontology Development. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006588901070118.
Full textLi, Tao, Shenghuo Zhu, and Mitsunori Ogihara. "Topic hierarchy generation via linear discriminant projection." In the 26th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/860435.860531.
Full textNiu, Yue, and Hongjie Zhang. "A Self-Aggregated Hierarchical Topic Model for Short Texts." In 2nd International Conference on Machine Learning, IOT and Blockchain (MLIOB 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111212.
Full textLiu, Nan, and Christopher C. Yang. "Keyphrase extraction for labeling a website topic hierarchy." In the 11th International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1593254.1593266.
Full textReports on the topic "Topic hierarchy"
Downes, Jane, ed. Chalcolithic and Bronze Age Scotland: ScARF Panel Report. Society for Antiquaries of Scotland, September 2012. http://dx.doi.org/10.9750/scarf.09.2012.184.
Full text