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copied!<p><strong>Disclaimer:</strong> This is not an easy to do task, and thus my suggested solutions will be high level and include references to academic papers that aim to solve a part of your problem:</p> <hr> <p>You can try a <strong>semantic relatedness</strong> approach:</p> <ul> <li>Find relatedness between every two pairs of words, filter out the word that is least related to all others.</li> <li>Semantic relatedness can be done using <strong><a href="http://cs.technion.ac.il/news/2011/438/docs/dudu_yanay.pdf" rel="nofollow">semantic sort</a></strong> in a supervised learning, for example.</li> </ul> <p>Another alternative is to model a <strong>semantic representation</strong> of each word.</p> <ul> <li>Each word will be represented by a vector representing its meaning. This vector can be obtained for example using the wikipedia articles that mention this word. <br>More information on this approach can be found in Markovitch et al <a href="http://www.cs.technion.ac.il/~shaulm/papers/pdf/Gabrilovich-Markovitch-JAIR2009.pdf" rel="nofollow">Wikipedia-based Semantic Interpretation for Natural Language Processing</a></li> <li>After you represent your data as vectors, it is a question of finding the word which is least similar to the others. It can be done using <a href="http://en.wikipedia.org/wiki/Supervised_learning" rel="nofollow">supervised learning</a>, or other alternative is choosing the point which is most distant from the median of all vectors.</li> </ul> <p>One more possible solution is using <a href="http://wordnet.princeton.edu/" rel="nofollow"><strong>WordNet</strong></a></p> <hr> <p>Note that all methods are heuristics that I would try, and are expected to fail for some cases, but I believe will work pretty well for most of the cases.</p>

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