By Daelemans W., Van den Bosch A.
Memory-based language processing--a laptop studying and challenge fixing procedure for language technology--is according to the concept the direct re-use of examples utilizing analogical reasoning is extra fitted to fixing language processing difficulties than the appliance of ideas extracted from these examples. This ebook discusses the idea and perform of memory-based language processing, displaying its comparative strengths over substitute equipment 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 details.
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At the same time, this also makes the IG weight robust to estimation problems in sparse data. Each parameter (weight) is estimated on the whole data set. A well-known problem with IG is that it tends to overestimate the relevance of features with large numbers of values. , Mann) as a feature in our German plural data set. Each value of this feature would be unique, and the feature will have a very high information gain, but it does not allow any generalization to new instances. 5). 2. 6). 6) The possibility of automatically determining the relevance of features implies that many different and possibly irrelevant features can be added to the feature set.
Instances in each of these intervals are then used in the IG computation as all having the same unordered, symbolic value per group. Note that this discretization is only temporary; it is not used in the computation of the distance metric. The IG weight of a feature is a probability-weighted average of the informativeness of the different values of the feature. This makes the values with low frequency but high informativity invisible. Such values disappear in the average. At the same time, this also makes the IG weight robust to estimation problems in sparse data.
To make our introduction more concrete, we will use plural formation of German nouns as a case study, based on Daelemans (2002). 0 or a later version has been installed on your system. 1. GERMAN PLURAL FORMATION 27 introduction to this problem, we provide a systematic introduction to the MBLP algorithms and their motivation, interleaved with tutorial material showing how to operate T I MBL on the plural formation data, and how to interpret its output. From this chapter onwards, T I MBL input and output is shown in outlined boxes, while practical discussions explaining the content of the boxes are printed around the boxes and are marked by a gray bar in the left margin.