By Walter Daelemans
Memory-based language processing--a computing device studying and challenge fixing approach for language technology--is in accordance with the concept the direct re-use of examples utilizing analogical reasoning is extra fitted to fixing language processing difficulties than the applying of ideas extracted from these examples. This publication discusses the speculation and perform of memory-based language processing, displaying its comparative strengths over replacement tools of language modelling. Language is complicated, with few generalizations, many sub-regularities and exceptions, and the good thing about memory-based language processing is that it doesn't summary clear of this worthy low-frequency info.
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Additional info for Memory-Based Language Processing (Studies in Natural Language Processing)
The emphasis on hands-on use of software in a book such as this deserves some justiﬁcation. Although our aims are mainly theoretical in showing that MBLP has the right bias for solving NLP tasks on the basis of argumentation and experiment, we believe that the strengths and limitations of any algorithm can only be understood in sufﬁcient depth by experimenting with this speciﬁc algorithm. To make our introduction more concrete, we will use plural formation of German nouns as a case study, based on Daelemans (2002).
In this case to ignore (I) features one to three. With these settings, however, generalization accuracy diminishes considerably, and there are a lot more duplicate instances now. , on the basis of linguistic intuition) directly to T I MBL via a weights ﬁle. weights is present with the following contents: # Fea. 1 2 3 4 5 6 7 Weight 1 1 1 4 8 16 10 MEMORY AND SIMILARITY 38 This represents the (linguistic) intuition that the ﬁnal syllable is much more important than the last but one syllable, and that within the last syllable the nucleus and especially the coda (which together constitute the rhyme) are relevant, with gender being slightly more important than nucleus and clearly more important than coda.
4 Conclusion Models, both hand-crafted or learned, for an NLP task should be general: they should explain how to perform the task beyond a set of training examples or observations, and how to handle new, previously unseen cases successfully. e. table lookup, is dismissed off-hand by many as missing this creative mechanism. In this chapter we have shown that this view has competing opinions. It makes sense to separate the dimensions of representation and learning. First of all, a rule-based approach does not have to be hand-crafted; rules can be learned.
Memory-Based Language Processing (Studies in Natural Language Processing) by Walter Daelemans