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DC Field | Value | Language |
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dc.contributor.author | TRIPATHI, VIVEK | - |
dc.date.accessioned | 2010-11-09T06:25:55Z | - |
dc.date.available | 2010-11-09T06:25:55Z | - |
dc.date.issued | 2009-07-06 | - |
dc.identifier.uri | http://dspace.dtu.ac.in:8080/jspui/handle/123456789/249 | - |
dc.description | ME THESIS | en_US |
dc.description.abstract | Text mining, also known as text data mining or knowledge discovery from textual databases refers to the process of extracting interesting and non-trivial patterns or knowledge from unstructured text documents from a fixed domain. Text Mining tasks include text categorization, text clustering, concept/entity extraction, document summarization, and entity relation modeling. In this work, the focus is given to concept/entity extraction only. The major challenging issue in extracting concept/entity from texts is natural language words are always ambiguous. Up to now, not much research in text mining especially in concept/entity extraction has focused on the ambiguity problem. Ambiguity problem occur when a sentence could be interpreted in more than one meaning here we addresses ambiguity issues in natural language texts, and presents a new technique for resolving ambiguity problem in extracting concept/entity from texts. The approach is developed by utilizing natural language processin... | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartofseries | TD-535;45 | - |
dc.subject | Text Mining | en_US |
dc.subject | Lexical Ambiguity | en_US |
dc.title | AN APPROACH TO REMOVE LEXICAL AMBIGUITY IN TEXT MINING | en_US |
Appears in Collections: | M.E./M.Tech. Computer Technology & Applications |
Files in This Item:
File | Description | Size | Format | |
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major+report.doc | 878 kB | Microsoft Word | View/Open |
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