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Subspace Representations of Unstructured Text

by F.B. Holt
"... Since 1970 vector-space models have been used for information retrieval from unstructured text. The initial simple vector-space models su#ered the same problems encountered today in searching the internet. These di#culties were significantly relieved by Latent Semantic Indexing (LSI), introduced ..."
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Since 1970 vector-space models have been used for information retrieval from unstructured text. The initial simple vector-space models su#ered the same problems encountered today in searching the internet. These di#culties were significantly relieved by Latent Semantic Indexing (LSI), introduced

Retrieval of Unstructured Text

by Dushan Badal, Dushan Badal
"... ..."
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Abstract not found

Unstructured text = ⇒ Structured forms

by Yifan Peng, Yifan Peng, Kernel-based Methods, Relation Extraction, Theme Theme, Theme Theme , 2014
"... Relation extraction Publications/documents grow dramatically and continually We need information extraction methods ..."
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Relation extraction Publications/documents grow dramatically and continually We need information extraction methods

An Evaluation of Unstructured Text Mining Software

by Micah J. Crowsey, A R. Ramstad, David H. Gutierrez, Gregory W. Paladino, K. P. White
"... Abstract — Five text mining software tools were evaluated by four undergraduate students inexperienced in the text mining field. The software was run on the Microsoft Windows XP operating system, and employed a variety of techniques to mine unstructured text for information. The considerations used ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract — Five text mining software tools were evaluated by four undergraduate students inexperienced in the text mining field. The software was run on the Microsoft Windows XP operating system, and employed a variety of techniques to mine unstructured text for information. The considerations used

Detecting and browsing events in unstructured text

by David A. Smith - In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , 2002
"... Previews and overviews of large, heterogeneous information resources help users comprehend the scope of collections and focus on particular subsets of interest. For narrative docu-ments, questions of “what happened? where? and when?” are natural points of entry. Building on our earlier work at the P ..."
Abstract - Cited by 29 (1 self) - Add to MetaCart
Previews and overviews of large, heterogeneous information resources help users comprehend the scope of collections and focus on particular subsets of interest. For narrative docu-ments, questions of “what happened? where? and when?” are natural points of entry. Building on our earlier work at the Perseus Project with detecting terms, place names, and dates, we have exploited co-occurrences of dates and place names to detect and describe likely events in document col-lections. We compare statistical measures for determining the relative significance of various events. We have built in-terfaces that help users preview likely regions of interest for a given range of space and time by plotting the distribution and relevance of various collocations. Users can also control the amount of collocation information in each view. Once particular collocations are selected, the system can identify key phrases associated with each possible event to organize browsing of the documents themselves.

Clustering Unstructured Text Documents Using Fading Function

by Pallav Roxy, Durga Toshniwal
"... Abstract—Clustering unstructured text documents is an important issue in data mining community and has a number of applications such as document archive filtering, document organization and topic detection and subject tracing. In the real world, some of the already clustered documents may not be of ..."
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Abstract—Clustering unstructured text documents is an important issue in data mining community and has a number of applications such as document archive filtering, document organization and topic detection and subject tracing. In the real world, some of the already clustered documents may

Alignment of Noisy Unstructured Text Data

by Julien Bourdaillet, Jean-gabriel Ganascia - IN: PROC. OF THE IJCAI WORKSHOP ON ANALYTICS FOR NOISY UNSTRUCTURED TEXT DATA (AND 2007) OF THE 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI , 2007
"... This paper describes a textual aligner named MEDITE whose specificity is the detection of moves. It was developed to solve a problem from textual genetic criticism, a humanities discipline that compares different versions of authors ’ texts in order to highlight invariants and differences between th ..."
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This paper describes a textual aligner named MEDITE whose specificity is the detection of moves. It was developed to solve a problem from textual genetic criticism, a humanities discipline that compares different versions of authors ’ texts in order to highlight invariants and differences between

Analytics for Noisy Unstructured Text Data

by Free Books, Held At , 2006
"... with UNESCO. ..."
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with UNESCO.

Data Algorithms for Processing and Analysis of Unstructured Text Documents

by Artem Borodkin , Evgeny Lisin , Wadim Strielkowski , 2014
"... Abstract This paper deals with the functionality of a research program complex for processing and analysis of unstructured text data. It provides a thorough description of the implemented algorithms and experimental results obtained in various text samples. With the recent disclose of PRISM and sim ..."
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Abstract This paper deals with the functionality of a research program complex for processing and analysis of unstructured text data. It provides a thorough description of the implemented algorithms and experimental results obtained in various text samples. With the recent disclose of PRISM

TITLE: Extraction of Causal-Association Networks from Unstructured Text Data

by Brett Nicholas Bojduj, N. Bojduj , 2009
"... iii Extraction of Causal-Association Networks from Unstructured Text Data by ..."
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iii Extraction of Causal-Association Networks from Unstructured Text Data by
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