By R. Skousen

1. Structuralist as opposed to Analogical Descriptions ONE vital objective of this publication is to check thoroughly dif ferent methods to describing language. the 1st of those methods, as a rule known as stnlctllralist, is the conventional procedure for describing habit. Its tools are present in many assorted fields - from organic taxonomy to literary feedback. A structuralist description should be widely characterised as a approach of type. the basic query structuralist description makes an attempt to reply to is how a normal contextual area could be partitioned. for every context within the partition, a rule is outlined. the guideline both specifies the habit of that context or (as in a taxonomy) assigns a reputation to that context. Structuralists have implicitly assumed that descriptions of habit aren't in basic terms be right, yet must also reduce the variety of ideas and allow in basic terms the best attainable contextual necessities. It seems that those intuitive notions can truly be derived from extra basic statements concerning the uncertainty of rule structures. generally, linguistic analyses were in accordance with the concept that a language is a procedure of ideas. Saussure, after all, is widely known as an early proponent of linguistic structuralism, as exemplified by way of his characterization of language as "a self-contained complete and precept of type" (Saussure 1966:9). but linguistic structuralism didn't originate with Saussure - nor did it finish with "American structuralism".

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We now determine the statistical properties of our definition of heterogeneity. It turns out that our decision rule of never increasing the number of disagreements is equivalent to the most powerful statistical test possible. To see this, let us reinterpret the behavior of a supracontext and its subcontexts in terms of an Ix] two-way contingency table: OUTCOMES 1 2 J SUB CONTEXTS 1 2 I SUPRACONTEXT n We can measure the divergence from identical behavior (or homogeneity) for this array in terms of Pearson's X 2 , a standard statistical measure of divergence: An Analogical Approach 35 The most powerful test for heterogeneity has a level of significance equal to one and occurs when Pearson's X2 = o.

Chapter Three THREE EXAMPLES FROM ENGLISH 1. Specifying the Data Set IN applying the analogical approach to actual language examples, we need to consider a number of questions on how to construct the data set. We must first select the variables that will specify the occurrences in the data set. We must also decide whether the data should be represented by token or by type. The problem of variable selection results in part from a computational limit on the number of variables. If a given context has n variables, the number of supracontexts that must be considered is 2n , an exponential function of 11.

36 ANALOGICAL MODELING OF LANGUAGE We now construct the analogical set for the given context. The analogical set contains all the pointers and their associated outcomes from each of the homogeneous supracontexts of the given context. HOMOGENEOUS SUPRACOI\'TEXT 31-12 3---2 NUMBER OF POINTERS OCCURRENCES WITH POINTERS ()- (), 310e~311r (212r C310e~311r~ 212r~032r 'U tU totals e r 2 0 2 0 2 1 2 4 4 9 Thus four out of the thirteen pointers in the analogical set point to the exceptional outcome e; the remaining nine pointers are associated with the regular outcome r.