@article{MAKHILLAJIT20141325804,
    title = {A New Text Mining Approach in Search Technology},
    journal = {Asian Journal of Information Technology},
    volume = {13},
    number = {2},
    pages = {93-98},
    year = {2014},
    issn = {1682-3915},
    doi = {ajit.2014.93.98},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2014.93.98},
    author = {B.,P.N. Santosh,A. and},
    keywords = {TM,AI,PR,DM,NLP,PRA,SE},
    abstract = {Text-Mining (TM) refers generally to the practice of extracting 
  attractive and non-trivial information and facts from unstructured text. TM 
  includes several Computer Science (CS) regulations with a strong direction towards 
  Artificial Intelligence (AI) in general including but not limited to Pattern 
  Recognition (PR), Neural Networks (NN), Natural Language Processing (NLP), Information 
  Retrieval (IR) and Machine Learning (ML). A significant variation with search 
  is that search requires a user to identify what he or she is looking for while 
  TM attempts to realize information in a model that is not known earlier. TM 
  is mainly motivating in domains where users have to invent new information. 
  This is the case for, e.g., in criminal enquiries and legal findings. Such examinations 
  require 100% evoke, i.e., users can not meet the expense of missing relevant 
  data. In distinction, a user searching the internet for background information 
  using a benchmark Search Engine (SE) simply requires any data as long as it 
  is reliable. Increasing evoke almost positively will decrease accuracy involving 
  that users have to browse huge collections of documents that that may or may 
  not be relevant. Standard procedures use language expertise to increase accuracy 
  but when text collections are not in one language are not domain specific and 
  or contain variable size and type documents either these schemes fail or are 
  so complicated that the user does not understand what is happening and loses 
  control. A different technique is to combine standard significance ranking with 
  Adaptive Filtering (AF) and Interactive Visualization (IV) that is based on 
  characteristics that have been mined earlier.}
    }