Group items tagged

(i) Never use a metaphor, simile, or other figure of speech which you are used to seeing in print. (ii) Never us a long word where a short one will do. (iii) If it is possible to cut a word out, always cut it out. (iv) Never use the passive where you can use the active. (v) Never use a foreign phrase, a scientific word, or a jargon word if you can think of an everyday English equivalent. (vi) Break any of these rules sooner than say anything outright barbarous.

By convention, m:n means elements m…n-1

A consequence of this last change is that the list only has four elements, and accessing a later value generates an error

We can join the words of a list to make a single string, or split a string into a list, as follows:

'Monty Python'.split()

frequency distribution

frequency of each vocabulary item

find the 50 most frequent words

hese very long words are often hapaxes (i.e. unique) and perhaps it would be better to find frequently occurring long words.

Here are all words from the chat corpus that are longer than 7 characters, that occur more than 7 times:   >>> fdist5 = FreqDist(text5) >>> sorted([w for w in set(text5) if len(w) > 7 and fdist5[w] > 7]) ['#14-19teens', '#talkcity_adults', '((((((((((', '........', 'Question', 'actually', 'anything', 'computer', 'cute.-ass', 'everyone', 'football', 'innocent', 'listening', 'remember', 'seriously', 'something', 'together', 'tomorrow', 'watching'] >>>

The collocations() function does this for us

find bigrams that occur more often than we would expect based on the frequency of individual words.

fdist = FreqDist(samples) create a frequency distribution containing the given samples fdist.inc(sample) increment the count for this sample fdist['monstrous'] count of the number of times a given sample occurred fdist.freq('monstrous') frequency of a given sample fdist.N() total number of samples fdist.keys() the samples sorted in order of decreasing frequency for sample in fdist: iterate over the samples, in order of decreasing frequency fdist.max() sample with the greatest count fdist.tabulate() tabulate the frequency distribution fdist.plot() graphical plot of the frequency distribution fdist.plot(cumulative=True) cumulative plot of the frequency distribution fdist1 < fdist2 test if samples in fdist1 occur less frequently than in fdist2

it goes through each word in text1, assigning each one in turn to the variable w and performing the specified operation on the variable.

The above notation is called a "list comprehension"

[f(w) for ...] or [w.f() for ...],

Now that we are not double-counting words like This and this

by filtering out any non-alphabetic items:   >>> len(set([word.lower() for word in text1 if word.isalpha()]))

A collocation is a sequence of words which occur together unusually often. Thus red wine is a collocation, while the wine is not. A characteristic of collocations is that they are resistant to substitution with words that have similar senses — maroon wine sounds definitely odd.