Error Mining with Suspicion Trees: Seeing the Forest for the Trees

In recent years, error mining approaches have been proposed to identify the most likely sources of errors in symbolic parsers and generators. However the techniques used generate a flat list of suspicious forms ranked by decreasing order of suspicion. We introduce a novel algorithm that structures the output of error mining into a tree (called, suspicion tree) highlighting the relationships between suspicious forms. We illustrate the impact of our approach by applying it to detect and analyse the most likely sources of failure in surface realisation; and we show how the suspicion tree built by our algorithm helps presenting the errors identified by error mining in a linguistically meaningful way thus providing better support for error analysis. The right frontier of the tree highlights the relative importance of the main error cases while the subtrees of a node indicate how a given error case divides into smaller more specific cases. Title and Abstract in Hindi s\d h v "o\ к sAT / EV хnn, v "o\ кF хoj j\gl m \ hAl к vqo m , þtFкA(mк pd &yAхк tTA pd -u(pAdк кF glEtyo\ к s\BAEvt ™oto\ кF phcAn к Ely ки / EV хnn tкnFк \ þ-tAEvt кF gyF h{\। hAlA\Eк þ-tAEvt tкnFк \ s\d hA(mк udAhrZo\ кo eк sADArZ s cF m \ unк GVt s\d h к ‡m m \ þ-t t кrt h{\। hm yhA eк nyF tкnFк þ-t t кr rh h{\ jo / EV хnn к pErZAmo\ кo eк v " (s\d h v " ) к !p m \ gEWt кr s\d hA(mк udAhrZo\ к bFc к s\b\Do\ кo EdхlAtA h{। hm apn tкnFк кF upyoEgtA pd -u(pAdк кF s\BAEvt asPltAao\ к ™oto\ кo Y Yn tTA Evš qZ кrк þ-t t кrt h{\। hm yh EdхAt h{\ Eк к{s hmAr tкnFк ’ArA EnEm t s\d h v " / EV хnn ’ArA phcAnFt glEtyo\ кo BAqAшA-/ к d E tкoZ s кAPF aT p Z trFк s þ-t t кr / EV Evš qZ m \ кAPF mddgAr sAEbt hotA h{। s\d h v " кA dAEhnA sFmA\t m Hy / EVp Z udAhrZo\ кo unк t lnA(mк mhA к sAT þ-t t кrtA h{ jbEк s\d h v " кF шAхAy \ EdхAtF\ h{\ Eк к{s eк / EVp Z udAhrZ CoV -CoV / EVp Z EvEш£ udAhrZo\ m \ b VtA h{।