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\title{Two-Word and Three -Word Disambiguation Rules for Telugu Language Sentences: A Practical Approach}
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             \author[1]{J.  Sreedhar}

             \affil[1]{  JNTUK, Kakinada}

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\date{\small \em Received: 12 December 2013 Accepted: 2 January 2014 Published: 15 January 2014}

\maketitle


\begin{abstract}
        


This paper describes Two-word and Three-word Disambiguation Rules for Telugu language sentences, which are written in WX-notation. Generally in real life good number of words, which are having many meanings. If a word has many meanings, then we can call it as a word ambiguity. To resolve a word ambiguity, Natural Language Processing (NLP) system having lot of Word Sense Disambiguation (WSD)[1] methods. Among many methods, here we are proposing rule based method for Word Sense Disambiguation.

\end{abstract}


\keywords{natural language processing, word sense disambiguation, rules, parts-of-speech.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
atural Language Processing(NLP) is a theoretically motivated multiple methods and techniques from which are selected for the accomplishment of particular type of language in analyzing and representing a human communicable at one or more level of linguistic analysis in the purpose of achieving human like languages processing for a range of tasks or applications.\par
Word Sense Disambiguation (WSD) \hyperref[b1]{[2]} is the process of differentiating among the senses of words. The process of selecting most appropriate meaning of the word based on the context in which they occur. Computational identification of meaning for words in context is called Word Sense Disambiguation.\par
WSD \hyperref[b2]{[3]} process to remove the ambiguity of word in a given context is an important for NLP applications such as Information Retrieval, Machine  
\section[{Approach for Two Word Disambiguation Two Word Disambiguation Rules}]{Approach for Two Word Disambiguation Two Word Disambiguation Rules}\par
Morphological analysis \hyperref[b10]{[10]}, \hyperref[b5]{[13]} of a word gives detailed information about a word. Morphologically \hyperref[b8]{[11]} every word carries information with reference to its lexemic form, morpho syntactic \hyperref[b13]{[12]} category, and inflection. The detailed information may include among many other features, such as root/stem i.e. the lexemic shape listed in the dictionary the lexical category like noun/verb/adjective/adverb/pronoun /number /indeclinable as the case may be.\par
The following are some of the POS tag \hyperref[b3]{[4]},  {\ref [5] [6]} disambiguation rules \hyperref[b11]{[7]}, \hyperref[b12]{[8]}, \hyperref[b9]{[9]} used in the task: W1 :: W2 => W1 :: W2\par
Where W1 and W2 a sequence of words in that order.  Where n is noun, v is verb, pn is pronoun, adj is adjective and adv is adverb.\par
Here from rule 2 when a word carries tags (n,pn) and followed by another word carrying the tag n then the tag pn retained eliminating the n from (n,pn). From rule 10 a word carrying the tag such as (n,pn) followed by avy then most the times pn will be retained and v will be eliminated. Depending on the context linguist will decide which tag will be retained and which one has to be eliminated. These are mostly contextually based syntactic rules. If two word sequences is unable to resolved unique tags then three words, four words sequence rules may be used for disambiguation. 
\section[{III.}]{III.}\par
Theoritical Explanation with Example for Two Word Ambiguity Here in the above sentence the word carries tags (n,adj) and followed by another word carrying the tag n then the tag adj retained eliminating the n from (n,adj).so from the above sentence adj is eliminated and n is retained. 
\section[{c) After Applying Disambiguation Rule}]{c) After Applying Disambiguation Rule}\par
Adaxi a Nacivewaku alavAtu padipoyiMxi . n n n v punc Where punc is punctuation. 
\section[{d) Analysis of Two Word Disambiguation}]{d) Analysis of Two Word Disambiguation}\par
Here the below figures 1 and 2 explores the analysis of the Accuracy. Where X-axis indicates the number of test sessions and Y-axis indicates the Accuracy. As the result, we found that the proposal method can disambiguate nearly 98\%.  :: w2 :: w3 => w1 :: w2 :: w3 n,v,pn :: n :: pn,v => v :: n :: pn In the above sentence the first word carries tags (n,v,pn) and followed by second word carrying the tag n and followed by third word carrying the tags (pn,v) then the tag v retained from the first word and pn retained from the third word eliminating the (n,pn) from (n,v,pn) and eliminating v from (pn,v). iv. Analysis Of Three Word Disambiguation Here the above figures 3 and 4 explores the analysis of the Accuracy. Where X-axis indicates the number of test sessions and Y-axis indicates the Accuracy. As the result, we found that the proposal method can disambiguate nearly 96\%.\par
We are very thankful to all the authors in a reference list, to make this research article in a better shape and right direction.  
\section[{Conclusion and Future Research Direction}]{Conclusion and Future Research Direction}\par
This research article explores the impact of twoword disambiguation and three-word disambiguation.\par
,\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}N}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{figure7}}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.017708333333333333\textwidth}P{0.07083333333333333\textwidth}P{0.3541666666666667\textwidth}P{0.017708333333333333\textwidth}P{0.19479166666666664\textwidth}P{0.19479166666666664\textwidth}}
8\tabcellsep 926\tabcellsep n :: v,n :: v,pn\tabcellsep =>\tabcellsep n :: n :: v\tabcellsep n :: n :: v\\
9\tabcellsep 11634\tabcellsep \multicolumn{2}{l}{n,v : avy :: v,pn,adj =>}\tabcellsep n :: avy :v\tabcellsep n :: avy :v\end{longtable} \par
 
\caption{\label{tab_2}Table 2 :}\end{figure}
 			\footnote{© 2014 Global Journals Inc. (US) Disambiguation and Empirical approach for Three-Word WSD} 		 		\backmatter   			 \par
Here based on the context, linguist will decide which tag will be retained and which one has to be eliminated. We observed that if two-word and three-word sequences is unable to resolve unique tags, then four-word, five-word sequence rules may be useful for disambiguation. 			  			  				\begin{bibitemlist}{1}
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\end{document}
