Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting and resolving spatial ambiguity in text using named entity
  extraction and self learning fuzzy logic techniques   Information extraction identifies useful and relevant text in a document and
converts unstructured text into a form that can be loaded into a database
table. Named entity extraction is a main task in the process of information
extraction and is a classification problem in which words are assigned to one
or more semantic classes or to a default non-entity class. A word which can
belong to one or more classes and which has a level of uncertainty in it can be
best handled by a self learning Fuzzy Logic Technique. This paper proposes a
method for detecting the presence of spatial uncertainty in the text and
dealing with spatial ambiguity using named entity extraction techniques coupled
with self learning fuzzy logic techniques

18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph