1 / 58

Programming for Linguists

Programming for Linguists. An Introduction to Python 13/12/2012. Dictionaries. Like a list, but more general In a list the index has to be an integer, e.g. words[4] In a dictionary the index can be almost any type A dictionary is like a mapping between 2 sets: keys and values.

scot
Download Presentation

Programming for Linguists

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Programming for Linguists An Introduction to Python13/12/2012

  2. Dictionaries • Like a list, but more general • In a list the index has to be an integer, e.g. words[4] • In a dictionary the index can be almost any type • A dictionary is like a mapping between 2 sets: keys and values

  3. To create an empty list:list = [ ] • To create an empty dictionary:dictionary = { } e.g. a dictionary containing English and Spanish words:>>>eng2sp = { }>>>eng2sp['one'] = 'uno’>>>print eng2sp{'one': 'uno'}

  4. In this case both the keys and the values are of the string type • Like with lists, you can create dictionaries yourselves, e.g.eng2sp = {'one': 'uno', 'two': 'dos', 'three': 'tres'}print eng2sp • Note: in general, the order of items in a dictionary is unpredictable

  5. You can use the keys to look up the corresponding values, e.g.>>>print eng2sp['two'] • The key ‘two’ always maps to the value ‘dos’ so the order of the items does not matter • If the key is not in the dictionary you get an error message, e.g.>>>print eng2sp[‘ten’]KeyError: ‘ten’

  6. The len( ) function returns the number of key-value pairslen(eng2sp) • The in operator tellsyouwhethersomethingappearsas a key in the dictionary>>>‘one’ in eng2spTrue • BUT>>>‘uno’ in eng2spFalse

  7. To see whethersomethingappears as a value in a dictionary, youcanuse the values( ) function, which returns the values as a list, and thenuse the in operator, e.g.>>>‘uno’ in eng2sp.values( )True • Lists can be values, but never keys!

  8. Default dictionary Trythis: words = [‘een’, ‘twee’, ‘drie’]frequencyDict = { }for w in words:frequencyDict[w] += 1

  9. Possible solution: for w in words:if w in frequencyDict:frequencyDict[w] += 1else:frequencyDict[w] = 1

  10. The easy solution: >>>fromcollections import defaultdict>>>frequencyDict = defaultdict(int)>>>for w in words:frequencyDict[w] += 1 • youcanuse int, float, str,… in the defaultdict

  11. A Dictionary as a Set of Counters • Suppose you want to count the number of times each letter occurs in a string, you could: • create 26 variables, traverse the string and, for each letter, add 1 to the corresponding counter • create a dictionary with letters as keys and counters as the corresponding values

  12. def frequencies(sent): freq_dict = defaultdict(int)for let in sent: freq_dict[let] += 1 return freq_dict dictA = frequencies(“abracadabra”) list_keys = dictA.keys( ) list_values = dictA.values( ) z_value = dictA[‘z’]

  13. The first line of the function creates an empty default dictionary • The for loop traverses the string • Each time through the loop, we create a new key item with the initial value 1 • If the letter is already in the dictionary we add 1 to its corresponding value

  14. Write a function that counts the word frequencies in a sentence instead of the letter frequencies using a dictionary

  15. def words(sent):word_freq = defaultdict(int)wordlist = sent.split( )for word in wordlist:word_freq[word] += 1return word_freq words(“this is is a a test sentence”)

  16. Dictionary Lookup • Given a dictionary “word_freq” and a key “is”, finding the corresponding value: word_freq[“is”] • This operation is called a lookup • What if you know the value and want to look up the corresponding key?

  17. Sorting a Dictionary According to its Values • First you need to import itemgetter:from operator import itemgetter • To go over each item in a dictionary you can use .iteritems( ) • To sort the dictionary according to the values, you need to use key = itemgetter(1) • To sort it decreasingly: reverse = True

  18. >>>from operator import itemgetter>>>defgetValues(sent):w_fr = defaultdict(int)wordlist = sent.split( )for word in wordlist:w_fr[word] += 1byVals = sorted(w_fr.iteritems( ), key = itemgetter(1), reverse =True) return byVals>>>getValues(‘this is a aasentence’)

  19. Write a functionthat takes a sentence as an argument and returns allwordsthatoccuronlyonce in the sentence.

  20. defgetHapax(sent):words = sent.split( )freqs = defaultdict(int)for w in words:freqs[w] += 1 hapaxlist = [ ]for item in freqs:value = freqs[item]ifvalue == 1:hapaxlist.append(item) return hapaxlist

  21. GettingStartedwith NLTK • In IDLE: import nltknltk.download()

  22. SearchingTexts • Start your script withimporting all texts in NLTK: fromnltk.book import * • text1: Moby Dick by Herman Melville 1851 • text2: Sense and Sensibility by Jane Austen 1811 • text3: The Book of Genesis • text4: Inaugural Address Corpus • text5: Chat Corpus • text6: Monty Python and the Holy Grail • text7: Wall Street Journal • text8: Personals Corpus • text9: The Man Who Was Thursday by G . K . Chesterton 1908

  23. Any time you want to find out about these texts, just enter their names at the Python prompt:>>> text1<Text: Moby Dick by Herman Melville 1851> • A concordance view shows every occurrence of a given word, together with some context:e.g. “monstrous” in Moby Dicktext1.concordance(“monstrous”)

  24. Try looking up the context of “lol” in the chat corpus (text 5) • If you have a corpus that contains texts that are spread over time, you can look up how some words are used differently over time:e.g. the InauguralAddress Corpus (dates back to 1789): words like “nation”, “terror”, “God”…

  25. You can also examine what other words appear in a similar context, e.g. text1.similar(“monstrous”) • common_contexts( ) allows you to examine the contexts that are shared by two or more words, e.g.text1.common_contexts([“very”, “monstrous”])

  26. You can also determine the location of a word in the text • This positional information can be displayed using a dispersion plot • Each stripe represents an instance of a word, and each row represents the entire text, e.g. text4.dispersion_plot(["citizens", "democracy", "freedom", "duties", "America"])

  27. Counting Tokens • To count the number of tokens (words + punctuation marks), just use the len( ) function, e.g. len(text5) • To count the number of unique tokens, you have to make a set, e.g.set(text5)

  28. If you want them sorted alfabetically, try this:sorted(set(text5)) • Note: in Python all capitalized words precede lowercase words (you can use .lower( ) first to avoid this)

  29. Now you can calculate the lexical diversity of a text, e.g. the chat corpus (text5): 45010 tokens 6066 unique tokens or typesThe lexical diversity = nr of types/nr of tokens • Use the Python functions to calculate the lexical diversity of text 5

  30. len(set(text5))/float(len(text5))

  31. Frequency Distributions • To find n most frequent tokens: FreqDist( ), e.g.fdist = FreqDist(text1)fdist[“have”] 760all_tokens = fdist.keys( )all_tokens[:50] • The function .keys( ) combined with the FreqDist( ) also gives you a list of all the unique tokens in the text

  32. Frequency distributions can be informative, BUT the most frequent words usually are function words (the, of, and, …) • What proportion of the text is taken up with such words? Cumulative frequency plotfdist.plot(50, cumulative=True)

  33. If frequent tokens do not give enough information, what about infrequent tokens?Hapaxes= tokens which occur only oncefdist.hapaxes( ) • Without their context, you do not get much information either

  34. Fine-grained Selection of Tokens • Extract tokens of a certain minimum length:tokens = set(text1)long_tokens = [ ]for token in tokens: if len(token) >= 15:long_tokens.append(token) #OR shorter:long_tokens= list(token for token in tokens if len(token) >= 15)

  35. BUT: very long words are often hapaxes • You can also extract frequently occurring long words of a certain length:words = set(text1)fdist = FreqDist(text1)#short versionfreq_long_words= list(word for word in words if len(word) >= 7 and fdist[word] >= 7)

  36. Collocations and Bigrams • A collocation is a sequence of words that occur together unusually often, e.g. “red whine” is a collocation, “yellow whine” is not • Collocations are essentially just frequent bigrams (word pairs), but you can find bigrams that occur more often than is to be expected based on the frequency of the individual words:text8.collocations( )

  37. Some Functions for NLTK's Frequency Distributions fdist = FreqDist(samples) fdist[“word”]  frequency of “word” fdist.freq(“word”)  frequency of “word” fdist.N( )  total number of samples fdist.keys( )  the samples sorted in order of decreasing frequency for sample in fdist:  iterates over the samples in order of decreasing frequency

  38. fdist.max( )  sample with the greatest count fdist.plot( )  graphical plot of the frequency distribution fdist.plot(cumulative=True)  cumulative plot of the frequency distribution fdist1 < fdist2  tests if the samples in fdist1 occur less frequently than in fdist2

  39. Accessing Corpora • NLTK also contains entire corpora, e.g.: • Brown Corpus • NPS Chat • Gutenberg Corpus • …A complete list can be found on http://nltk.googlecode.com/svn/trunk/nltk_data/index.xml

  40. Each of these corpora contains dozens of individual texts • To see which files are e.g. in the Gutenberg corpus in NLTK:nltk.corpus.gutenberg.fileids() • Do not forget the dot notation nltk.corpus. This tells Python the location of the corpus

  41. You can use the dot notation to work with a corpus from NLTK or you can import a corpus at the beginning of your script:from nltk.corpus import gutenberg • After that you just have to use the name of the corpus and the dot notation before a functiongutenberg.fileids( )

  42. If you want to examine a particular text, e.g. Shakespeare’s Hamlet, you can use the .words( ) functionHamlet = gutenberg.words(“shakespeare-hamlet.txt”) • Note that “shakespeare-hamlet.txt” is the file name that is to be found using the previous .fileids( ) function • You can use some of the previously mentioned functions (corpus methods) on this text, e.g.fdist_hamlet = FreqDist(hamlet)

  43. Some Corpus Methods in NLTK • brown.raw( )  raw data from the corpus file(s) • brown.categories( )  fileids( ) grouped per predefinedcategories • brown.words( )  a list of words and punctuationtokens • brown.sents( )  words( ) groupedintosentences

  44. brown.tagged_words( )  a list of (word,tag) pairs • brown.tagged_sents( ) tagged_words( ) groupedintosentences • treebank.parsed_sents( )  a list of parse trees

  45. defstatistics(corpus):forfileidincorpus.fileids( ): nr_chars = len(corpus.raw(fileid)) nr_words = len(corpus.words(fileid)) nr_sents = len(corpus.sents(fileid)) nr_vocab = len(set([word.lower() for word in corpus.words(fileid)])) print fileid, “average word length: ”, nr_chars/nr_words, “average sentencelength: ”, nr_words/nr_sents, “lexicaldiversity: ”, nr_words/nr_vocab

  46. Some corpora contain several subcategories, e.g. the Brown Corpus contains “news”, “religion”,… • You can optionally specify these particular categories or files from a corpus, e.g.:from nltk.corpus import brown brown.categories( ) brown.words(categories='news') brown.words(fileids=['cg22']) brown.sents(categories=['news', 'editorial', 'reviews'])

  47. Some linguistic research: comparing genres in the Brown corpus in their usage of modal verbs fromnltk.corpus import browncfd = nltk.ConditionalFreqDist((genre, word) for genre in brown.categories( )for word in brown.words(categories =genre)) #Do not press enter to type in the for #statements!

  48. genres = ['news', 'religion', 'hobbies', 'science_fiction', 'romance', 'humor’] modal_verbs = ['can', 'could', 'may', 'might', 'must', 'will'] cfd.tabulate(conditions=genres, samples=modal_verbs)

  49. can could may might must will news 93 86 66 38 50 389 religion 82 59 78 12 54 71 hobbies 268 58 131 22 83 264 science_fiction16 49 4 12 8 16 romance 74 193 11 51 45 43 humor 16 30 8 8 9 13 • A conditional frequency distribution is a collection of frequency distributions, each one for a different "condition” • The condition is usually the category of the text (news, religion,…)

  50. Loading Your Own Text or Corpus • Make sure that the texts/files of your corpus are in plaintext format (convert them, do not just change the file extensions from e.g. .docx to .txt) • Make a map with the name of your corpus which contains all the text files

More Related