other,5-2-N03-2015,ak automaton </term> . For our purposes , a <term> hub </term> is a <term> node </term> in a <term> graph
other,3-3-N03-2015,ak </term> greater than one . We create a <term> word-trie </term> , transform it into a <term> minimal
other,11-2-N03-2015,ak hub </term> is a <term> node </term> in a <term> graph </term> with <term> in-degree </term> greater
other,9-3-N03-2015,ak word-trie </term> , transform it into a <term> minimal DFA </term> , then identify <term> hubs </term> .
other,8-2-N03-2015,ak our purposes , a <term> hub </term> is a <term> node </term> in a <term> graph </term> with <term> in-degree
tech,14-1-N03-2015,ak identifying <term> hubs </term> in an <term> automaton </term> . For our purposes , a <term> hub </term>
other,18-2-N03-2015,ak in-degree </term> greater than one and <term> out-degree </term> greater than one . We create a <term>
other,8-4-N03-2015,ak boundary between <term> root </term> and <term> suffix </term> , achieving similar performance to
other,6-4-N03-2015,ak hubs </term> mark the boundary between <term> root </term> and <term> suffix </term> , achieving
other,14-3-N03-2015,ak <term> minimal DFA </term> , then identify <term> hubs </term> . Those <term> hubs </term> mark the
other,11-1-N03-2015,ak <term> morphology </term> by identifying <term> hubs </term> in an <term> automaton </term> . For
other,8-1-N03-2015,ak unsupervised technique </term> for learning <term> morphology </term> by identifying <term> hubs </term> in
tech,4-1-N03-2015,ak model </term> . We describe a simple <term> unsupervised technique </term> for learning <term> morphology </term>
other,1-4-N03-2015,ak then identify <term> hubs </term> . Those <term> hubs </term> mark the boundary between <term> root
other,13-2-N03-2015,ak node </term> in a <term> graph </term> with <term> in-degree </term> greater than one and <term> out-degree
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