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Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016)
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- Date
- 1. 2.2016 18:25:22
-
Cao, N.; Sun, J.; Lin, Y.-R.; Gotz, D.; Liu, S.; Qu, H.: FacetAtlas : Multifaceted visualization for rich text corpora (2010)
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Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004)
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- Date
- 16.11.2008 16:22:48
-
Robertson, A.M.; Willett, P.: Applications of n-grams in textual information systems (1998)
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- Abstract
- Provides an introduction to the use of n-grams in textual information systems, where an n-gram is a string of n, usually adjacent, characters, extracted from a section of continuous text. Applications that can be implemented efficiently and effectively using sets of n-grams include spelling errors detection and correction, query expansion, information retrieval with serial, inverted and signature files, dictionary look up, text compression, and language identification
- Object
- n-grams
-
Brandão, W.C.; Santos, R.L.T.; Ziviani, N.; Moura, E.S. de; Silva, A.S. da: Learning to expand queries using entities (2014)
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- Date
- 22. 8.2014 17:07:50
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Qiu, Y.; Frei, H.P.: Concept based query expansion (1993)
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Lee, Y.-Y.; Ke, H.; Yen, T.-Y.; Huang, H.-H.; Chen, H.-H.: Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement (2020)
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Stojanovic, N.: On the query refinement in the ontology-based searching for information (2005)
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Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002)
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- Date
- 22. 2.2003 17:25:39
22. 2.2003 18:17:40
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Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
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- 30. 3.2001 13:32:22
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Jiang, Y.; Zhang, X.; Tang, Y.; Nie, R.: Feature-based approaches to semantic similarity assessment of concepts using Wikipedia (2015)
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Qu, R.; Fang, Y.; Bai, W.; Jiang, Y.: Computing semantic similarity based on novel models of semantic representation using Wikipedia (2018)
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Bettencourt, N.; Silva, N.; Barroso, J.: Semantically enhancing recommender systems (2016)
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Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998)
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Nie, J.-Y.: Query expansion and query translation as logical inference (2003)
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Nakashima, M.; Sato, K.; Qu, Y.; Ito, T.: Browsing-based conceptual information retrieval incorporating dictionary term relations, keyword associations, and a user's interest (2003)
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Wang, Y.-H.; Jhuo, P.-S.: ¬A semantic faceted search with rule-based inference (2009)
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Morato, J.; Llorens, J.; Genova, G.; Moreiro, J.A.: Experiments in discourse analysis impact on information classification and retrieval algorithms (2003)
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- Abstract
- Researchers in indexing and retrieval systems have been advocating the inclusion of more contextual information to improve results. The proliferation of full-text databases and advances in computer storage capacity have made it possible to carry out text analysis by means of linguistic and extra-linguistic knowledge. Since the mid 80s, research has tended to pay more attention to context, giving discourse analysis a more central role. The research presented in this paper aims to check whether discourse variables have an impact on modern information retrieval and classification algorithms. In order to evaluate this hypothesis, a functional framework for information analysis in an automated environment has been proposed, where the n-grams (filtering) and the k-means and Chen's classification algorithms have been tested against sub-collections of documents based on the following discourse variables: "Genre", "Register", "Domain terminology", and "Document structure". The results obtained with the algorithms for the different sub-collections were compared to the MeSH information structure. These demonstrate that n-grams does not appear to have a clear dependence on discourse variables, though the k-means classification algorithm does, but only on domain terminology and document structure, and finally Chen's algorithm has a clear dependence on all of the discourse variables. This information could be used to design better classification algorithms, where discourse variables should be taken into account. Other minor conclusions drawn from these results are also presented.
-
Bayer, O.; Höhfeld, S.; Josbächer, F.; Kimm, N.; Kradepohl, I.; Kwiatkowski, M.; Puschmann, C.; Sabbagh, M.; Werner, N.; Vollmer, U.: Evaluation of an ontology-based knowledge-management-system : a case study of Convera RetrievalWare 8.0 (2005)
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Roy, R.S.; Agarwal, S.; Ganguly, N.; Choudhury, M.: Syntactic complexity of Web search queries through the lenses of language models, networks and users (2016)
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- Abstract
- Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication.
