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An encoding technique based on word importance for the clustering of Web documents
2002
Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.
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We use a two level self-organizing map architecture to generate clusters of words and documents. ...
A web document retrieval system is presented to demonstrate how this approach could be integrated into web search. ...
It uses a two level Kohonen's self-organizing map approach to group words and documents of similar contextual similarity. ...
doi:10.1109/iconip.2002.1201885
fatcat:yodgeufqlzhcvikwjaiijqa3jq
Using a Connectionist Approach for Enhancing Domain Ontologies: Self-Organizing Word Category Maps Revisited
[chapter]
2003
Lecture Notes in Computer Science
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The terms, which are extracted from domain-specific text documents, are mapped onto a two-dimensional map to provide an intuitive interface displaying semantically similar words in spatially similar regions ...
In this paper, we present an approach based on neural networks for organizing words of a specific domain according to their semantic relations. ...
In other words, the average contexts of words at displacements −1 and +1 constitute the contextual description. x i = x i (−1) x i (1) (2)
Self-Organizing Map Algorithm The self-organizing map (SOM) ...
doi:10.1007/978-3-540-45228-7_27
fatcat:tumnovrn25dnhebcndhzbc3txy
Integrating contextual information to enhance SOM-based text document clustering
2002
Neural Networks
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Exploration of text corpora using Self-Organizing Maps has shown promising results in recent years. ...
Topographic map approaches usually use the original vector space model known from Information Retrieval for text document representation. ...
The Self-Organizing Map (SOM) (Kohonen, 1995) , an unsupervised algorithm for clustering and topographic mapping, has been repeatedly used for this task, examples being some basic work in the ET-Map ...
doi:10.1016/s0893-6080(02)00082-5
pmid:12416697
fatcat:7j74aplxebbnxpwbdnvgctzwj4
Self-organizing Maps in Web Mining and Semantic Web
[chapter]
2010
Self-Organizing Maps
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Web Mining with Self-organizing Maps Applying SOM on natural language data means doing data mining on text data, for instance Web documents (Lagus, 2000) . ...
After training a SOM on all the words in a collection of documents -where the vectorial coding of words represents the contextual usage -, the result self-organizing map groups the words in semantic categories ...
Self-organizing Maps in Web Mining and Semantic Web, Self-Organizing Maps, George K Matsopoulos (Ed.), ISBN: 978-953-307-074-2, InTech, Available from: http://www.intechopen.com/books/self-organizing-maps ...
doi:10.5772/9172
fatcat:vqyekr43lnafloejqisnfbzv5m
Skim-Attention: Learning to Focus via Document Layout
[article]
2021
arXiv
pre-print
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Skim-Attention can be further combined with long-range Transformers to efficiently process long documents. ...
Transformer-based pre-training techniques of text and layout have proven effective in a number of document understanding tasks. ...
Acknowledgments JRR wants to thank the organizers for a fantastic conference in the Canadian wilderness. Many thanks who contributed the content of this discussion, among them are J. ...
arXiv:2109.01078v1
fatcat:43jhyddsjfhzjlcyjla6ybrsra
Towards a Linguistic Stylometric Model for the Authorship Detection in Cybercrime Investigations
2019
International Journal of English Linguistics
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It is also clear that the use of a self-organizing map (SOM) led to better clustering performance because of its capacity to integrate two different linguistic levels for each author profile. ...
This study proposes an integrated framework that considers letter-pair frequencies/combinations along with the lexical features of documents as a means to identifying the authorship of short texts posted ...
It is also clear that the use of a self-organizing map (SOM) leads to better clustering performance with its capacity to integrate two different linguistic levels (i.e., both morphological and lexical ...
doi:10.5539/ijel.v9n5p182
fatcat:2n5omcn7vfhwvh4js3thporcs4
Cybercrime and Authorship Detection in Very Short Texts A Quantitative Morpho-lexical Approach
2019
مجلة البحث العلمی فی الآداب
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It is also clear that the use of the self-organizing map (SOM) led to better clustering performance for its capacity to integrate two different linguistic levels of each author profile together. ...
The present study proposes an integrated framework that considers letterpair frequencies/combinations along with the lexical features of documents. ...
For classification purposes, the self-organizing maps (SOM) model is used. ...
doi:10.21608/jssa.2019.38725
fatcat:etmpfy6u4bekxonvo4vp4bnzb4
Helping Knowledge Cross Boundaries: Using Knowledge Visualization to Support Cross-Community Sensemaking
2007
2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07)
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The developed method enables the visualization of implicit structures of personal and community knowledge and their use for multi-perspective access to community information spaces. ...
Eliciting personal points of view To construct such maps based on users personal points of view we combine statistical text-analysis and self-organized clustering with methods for supervised learning of ...
The Structuring View lets the user organize his information seeking results into personal maps (adding and grouping documents, naming clusters etc.) ...
doi:10.1109/hicss.2007.245
dblp:conf/hicss/Novak07
fatcat:evi7lpyx7rgh5awpjpxg6yifoe
Semi-Automated Extraction of Targeted Data fromWeb Pages
2006
22nd International Conference on Data Engineering Workshops (ICDEW'06)
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Such rules mainly record a semantic interpretation of recurring types of information in a cluster of similar Web documents and their location in those documents. ...
The World Wide Web can be considered an infinite source of information for both individuals and organizations. ...
The paper is organized as follows: in Section 2, the main concepts of our approach are defined: page cluster, page component and mapping rule. ...
doi:10.1109/icdew.2006.135
dblp:conf/icde/EstievenartMHT06
fatcat:vwhfskctujcr7egcynig6mbire
A multimedia interactive search engine based on graph-based and non-linear multimodal fusion
2016
2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)
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This paper presents an interactive multimedia search engine, which is capable of searching into multimedia collections by fusing textual and visual information. ...
Apart from multimedia search, the engine is able to perform text search and image retrieval independently using both high-level and lowlevel information. ...
Using Self Organizing Maps [12] , all images are clustered, hence, all images of the collection are organized by color. ...
doi:10.1109/cbmi.2016.7500276
dblp:conf/cbmi/MoumtzidouGMLVK16
fatcat:zx3tgnl6dbcljaaezmakgmc72y
Dragon Toolkit: Incorporating Auto-Learned Semantic Knowledge into Large-Scale Text Retrieval and Mining
2007
19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007)
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Our method extracts explicit topic signatures from documents and then statistically maps them into singleword features. ...
The dragon toolkit reflects our method and its effectiveness is demonstrated by three tasks, text retrieval, text classification, and text clustering. ...
Examples of semantic mapping are shown in Figure 2 . If topic signatures such as multiword phrases and ontological concepts self-contain contextual information, the mapping is context-sensitive. ...
doi:10.1109/ictai.2007.117
dblp:conf/ictai/ZhouZH07
fatcat:n7oe6nms3vhphpa4f26zzoo2d4
A Detailed Survey on Topic Modeling for Document and Short Text Data
2019
International Journal of Computer Applications
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Text mining is one of the most significant field in the digital era due to the rapid growth of textual information. Topic models are gaining popularity in the last few years. ...
These methods gained popularity in extracting hidden themes from the document (corpus). ...
It finds more meaningful contextual structure. Kandemir et al., [77] worked on by integrating LDA and sparse Gaussian processes. ...
doi:10.5120/ijca2019919265
fatcat:jmti3vkmufa3xkywpo3pebravi
Projection: A Mixed-Initiative Research Process
[article]
2022
arXiv
pre-print
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The interface supports adding context to searches and visualizing information in multiple dimensions with techniques such as hierarchical clustering and spatial projections. ...
Communication of dense information between humans and machines is relatively low bandwidth. ...
The tool clusters documents on a map based on their similarity (as seen in Figure 1 ), the goal being that similar documents show up on the map next to each other and can be grouped into hierarchical ...
arXiv:2201.03107v1
fatcat:xvv7nxfrszgc7phrbl5z5retry
Semantic Research for Digital Libraries
1999
D-Lib Magazine
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TNs proved »<k» esses 1 • Automatic Categorization: A category map is the result of performing a neural network-based clustering (self-organizing) of similar documents and automatic category labeling. ...
of heterogeneous repositories with disparate semantics, clustering and automatic hierarchical organization of information, and algorithms for automatic rating, ranking, and evaluation of information quality ...
doi:10.1045/october99-chen
fatcat:gxlv5znrxza2xmpcak7h4noway
Text Analysis for Constructing Design Representations
[chapter]
1996
Artificial Intelligence in Design '96
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We integrate the design document learning system with an agent-based collaborative design system for fetching design information based on the "smart drawings" paradigm. ...
Along with the benefits of this concurrency comes the complexity of sharing and accessing design information. ...
Acknowledgements The authors would like to acknowledge William H Wood III for his valuable comments and converting the scanned document images into ASCII text, and John Wiley and Sons, Inc. for their permission ...
doi:10.1007/978-94-009-0279-4_2
fatcat:c2h7iwaxr5dnrnhibo564hrnsi
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