Recent Advances in Memory-Based Part-of-Speech Tagging

Memory based learning algorithms are lazy learners Examples of a task are stored in memory and processing is largely postponed to the time when new instances of the task need to be solved This is then done by extrapolating directly from those remem bered instances which are most similar to the present ones Using memory based learning for Part of Speech tagging has a number of advantages over traditional statistical POS taggers i there is no need for an additional smoothing component for sparse data ii even low frequent or exceptional patterns can contribute to generalization iii the use of a weighted similarity metric allows for an easy integration of di erent information sources and iv both development time and processing speed are very fast in the or der of hours and thousands of words sec respectively In recent work we have applied the Memory Based tagger MBT to a number of di erent languages and corpora En glish Dutch Czech Swedish and Spanish Furthermore we have performed a controlled experimental comparison of MBT with several other POS tagging algorithms