MBT: A Memory-Based Part of Speech Tagger-Generator
<|reference_start|>MBT: A Memory-Based Part of Speech Tagger-Generator: We introduce a memory-based approach to part of speech tagging. Memory-based learning is a form of supervised learning based on similarity-based reasoning. The part of speech tag of a word in a particular context is extrapolated from the most similar cases held in memory. Supervised learning approaches are useful when a tagged corpus is available as an example of the desired output of the tagger. Based on such a corpus, the tagger-generator automatically builds a tagger which is able to tag new text the same way, diminishing development time for the construction of a tagger considerably. Memory-based tagging shares this advantage with other statistical or machine learning approaches. Additional advantages specific to a memory-based approach include (i) the relatively small tagged corpus size sufficient for training, (ii) incremental learning, (iii) explanation capabilities, (iv) flexible integration of information in case representations, (v) its non-parametric nature, (vi) reasonably good results on unknown words without morphological analysis, and (vii) fast learning and tagging. In this paper we show that a large-scale application of the memory-based approach is feasible: we obtain a tagging accuracy that is on a par with that of known statistical approaches, and with attractive space and time complexity properties when using {\em IGTree}, a tree-based formalism for indexing and searching huge case bases.} The use of IGTree has as additional advantage that optimal context size for disambiguation is dynamically computed.<|reference_end|>
@article{daelemans1996mbt:,
    title={MBT: A Memory-Based Part of Speech Tagger-Generator},
    author={Walter Daelemans (U. Tilburg, U. Antwerp), Jakub Zavrel (U. Tilburg)
  Peter Berck (U. Antwerp), Steven Gillis (U. Antwerp)},
    journal={Proceedings WVLC, Copenhagen},
    year={1996},
    archivePrefix={arXiv},
    eprint={cmp-lg/9607012},
    primaryClass={cmp-lg cs.CL}
}