3: Productivity in .NET Access 2d Data Matrix barcode in .NET 3: Productivity

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3: Productivity using barcode maker for vs .net control to generate, create data matrix barcode image in vs .net applications. PDF-417 spreading throug Visual Studio .NET ECC200 h the lexicon. Usually only the target item is (successfully) retrieved, which means that the activation of the target must have been strongest.

Now assume that a low frequency complex word enters the speech processing system of the hearer. Given that low frequency items have a low resting activation, access to the whole word representation of this word (if there is a whole word representation available at all) will be rather slow, so that the decomposition route will win the race. If there is no whole word representation available, for example in the case of newly coined words, decomposition is the only way to process the word.

If, however, the complex word is extremely frequent, it will have a high resting activation, will be retrieved very fast and can win the race, even if decomposition is also in principle possible. Let us look at some complex words and their frequencies for illustration. The first problem we face is to determine how frequently speakers use a certain word.

This methodological problem can be solved with the help of large electronic text collections, so-called corpora . Such corpora are huge collections of spoken and written texts which can be used for studies of vocabulary, syntax, semantics, etc., or for making dictionaries.

In our case, we will make use of the British National Corpus (BNC). This is a very large representative collection of texts and conversations from all kinds of sources, which amounts to about one hundred million words, c. 90 million of which are taken from written sources, c.

10 million of which represent spoken language. For reasons of clarity we have to distinguish between the number of different words (the so-called types) and the overall number of words in a corpus (the so-called tokens). The 100 million words of the BNC are tokens, which represent about 940,000 types.

We can look up the frequency of words in the BNC by checking the word frequency list provided by the corpus compilers. The two most frequent words in English, for example, are the definite article the (which occurs about 6.1 million times in the BNC), followed by the verb BE, which (counting all its different forms am, are, be, been, being, is, was, were) has a frequency of c.

4.2 million, meaning that it occurs 4.2 million times in the corpus.

For illustrating the frequencies of derived words in a large corpus let us look at the frequencies of some of the words with the suffix -able as they occur in the BNC. In (2), I give the (alphabetically) first twenty -able derivatives from the word list for the written part of the BNC corpus. Note that the inclusion of the form affable in this list of -.

3: Productivity able derivatives Data Matrix for .NET may be controversial (see chapter 4, section 2, or exercise 4.1.

for a discussion of the methodological problems involved in extracting lists of complex words from a corpus). (2) Frequencies of -able derivatives in the BNC (written corpus) -able derivative abominable absorbable abstractable abusable acceptable accountable accruable achievable acid-extractable actable frequency 84 1 2 1 3416 611 1 176 1 1 -able derivative actionable actualizable adaptable addressable adjustable admirable admissable adorable advisable affable frequency 87 1 230 12 369 468 2 66 516 111. There are huge d ifferences observable between the different -able derivatives. While acceptable has a frequency of 3416 occurrences, absorbable, abusable, accruable, acidextractable, actable and actualizable occur only once among the 90 million words of that sub-corpus. For the reasons outlined above, high frequency words such as acceptable are highly likely to have a whole word representation in the mental lexicon although they are perfectly regular.

To summarize, it was shown that frequency of occurrence plays an important role in the storage, access, and retrieval of both simplex and complex words. Infrequent complex words have a strong tendency to be decomposed. By contrast, highly frequent forms, be they completely regular or not, tend to be stored as whole words in the lexicon.

On the basis of these psycholinguistic arguments, the notion of a nonredundant lexicon should be rejected. But what has all this to do with productivity This will become obvious in the next section, where we will see that (and why) productive processes are characterized by a high proportion of low-frequency words..

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