This document provides an overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It discusses NLTK's modules for common NLP tasks like tokenization, part-of-speech tagging, parsing, and classification. It also describes how NLTK can be used to analyze text corpora, frequency distributions, collocations and concordances. Key functions of NLTK include tokenizing text, accessing annotated corpora, analyzing word frequencies, part-of-speech tagging, and shallow parsing.

1. Text Analytics With
NLTK
Girish Khanzode

2. Contents
• Tokenization
• Corpuses
• Frequency Distribution
• Stylistics
• SentenceTokenization
• WordNet
• Stemming
• Lemmatization
• Part of SpeechTagging
• Tagging Methods
• UnigramTagging
• N-gramTagging
• Chunking – Shallow Parsing
• Entity Recognition
• SupervisedClassification
• DocumentClassification
• Hidden Markov Models - HMM
• References

3. NLTK
• A set of Python modules to carry out many common natural language
tasks.
• Basic classes to represent data for NLP
• Infrastructure to build NLP programs in Python
• Python interface to over 50 corpora and lexical resources
• Focus on Machine Learning with specific domain knowledge
• Free and Open Source

4. NLTK
• Numpy and Scipy under the hood
• Fast and Formal
• Standard interfaces for tokenization, part-of-speech tagging, syntactic parsing
and text classification
• Windows:
>>> import nltk
>>> nltk.download('all')
• Linux
$ pip install --upgrade nltk

5. NLTK -Top-Level Organization
• Organized as a flat hierarchy of packages and modules
• Each module provides the tools necessary to address a specific task
• Modules has two types of classes
– Data-oriented classes
• Used to represent information relevant to natural language processing.
– Task-oriented classes
• Encapsulate the resources and methods needed to perform a specific task.

6. Modules
• Token - classes for representing and processing individual elements of
text, such as words and sentences
• Probability - classes for representing and processing probabilistic
information
• Tree - classes for representing and processing hierarchical information
over text
• Cfg - classes for representing and processing context free grammars

7. Modules
• Tagger - tagging each word with a part-of-speech, a sense, etc
• Parser - building trees over text (includes chart, chunk and probabilistic
parsers)
• Classifier - classify text into categories (includes feature,
featureSelection, maxent, naivebayes)
• Draw - visualize NLP structures and processes
• Corpus - access (tagged) corpus data

8. Tokenization
• Simplest way to represent a text is with a single string
• Difficult to process text in this format
• Convenient to work with a list of tokens
• Task of converting a text from a single string to a list of tokens is known as
tokenization
• The most basic natural language processing technique
• Example -WordTokenization
Input : “Hey there, How are you all?”
Output : “Hey”, “there,”, “How”, “are”, “you”, “all?”

9. Tokens andTypes
• The term word can be used in two different ways
– To refer to an individual occurrence of a word
– To refer to an abstract vocabulary item
• For example, the sentence “my dog likes his dog” contains five occurrences of
words, but four vocabulary items

10. Tokens andTypes
• To avoid confusion use more precise terminology
– Word token - an occurrence of a word
– WordType - a vocabulary item
• Tokens constructed from their types using theToken constructor
• Token member functions - type and loc

11. Tokens andTypes
>>> from nltk.token import *
>>> my_word_type = 'dog‘
'dog’
>>> my_word_token =Token(my_word_type) ‘dog'@[?]

12. Text Locations
• Text location @ [s:e] specifies a region of a text
– s is the start index
– e is the end index
• Specifies the text beginning at s, and including everything up to (but not
including) the text at e
• Consistent with Python slice

13. Text Locations
• Think of indices as appearing between elements
– I saw a man
– 0 1 2 3 4
• Shorthand notation when location width = 1
• Indices based on different units
– character
– word
– sentence

14. Text Locations
• Locations tagged with sources
– files, other text locations – the first word of the first sentence in the file
• Location member functions
– start
– end
– unit
– source

15. Text Corpus
• Large collection of text
• Concentrate on a topic or open domain
• May be raw text or annotated / categorized

16. Corpuses
• Gutenberg - selection of e-books from Project Gutenberg
• Webtext - forum discussions, reviews, movie script
• nps_chat - anonymized chats
• Brown - 1 million word corpus, categorized by genre
• Reuters - news corpus
• Inaugural - inaugural addresses of presidents
• Udhr - multilingual corpus

17. Accessing Corpora
• Corpora on disk - text files
• NLTK provides Python modules / functions / classes that allow for
accessing the corpora in a convenient way
• It is quite an effort to write functions that read in a corpus especially when
it comes with annotations
• The task of reading in a corpus is needed in many NLP projects

18. Accessing Corpora
• # tell Python we want to use the Gutenberg corpus
• from nltk.corpus import gutenberg
• # which files are in this corpus?
• print(gutenberg.fileids())
• >>> ['austen-emma.txt', 'austen-persuasion.txt', 'austen-sense.txt', 'bible-
kjv.txt', ...]

19. Accessing Corpora - RawText
• # get the raw text of a corpus = one string
• >>> emmaText = gutenberg.raw("austen-emma.txt")
• # print the first 289 characters of the text
• >>> emmaText = gutenberg.raw("austen-emma.txt")
• >>> emmaText[:289]
• '[Emma by Jane Austen 1816]nnVOLUME InnCHAPTER InnnEmmaWoodhouse, handsome, clever,
and rich, with a comfortable homenand happy disposition, seemed to unite some of the best
blessingsnof existence; and had lived nearly twenty-one years in the worldnwith very little to distress or
vex her.‘

20. Accessing Corpora -Words
• # get the words of a corpus as a list
• emmaWords = gutenberg.words("austen-emma.txt")
• # print the first 30 words of the text
• >>> print(emmaWords[:30])
• ['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ']', 'VOLUME', 'I', 'CHAPTER', 'I', 'Emma',
'Woodhouse‘, 'handsome', ',', 'clever', ',', 'and', 'rich', ',', 'with', 'a', 'comfortable', 'home',
'and‘, 'happy', 'disposition', ',', 'seemed']

21. Accessing Corpora: Sentences
• # get the sentences of a corpus as a list of lists - one list of words per sentence
• >>> senseSents = gutenberg.sents("austen-sense.txt")
• # print out the first four sentences
• >>> print(senseSents[:4])
• [['[', 'Sense', 'and', 'Sensibility', 'by', 'Jane', 'Austen', '1811', ']'], ['CHAPTER', '1'], ['The',
'family', 'of', 'Dashwood', 'had', 'long‘, 'been', 'settled', 'in', 'Sussex', '.'], ['Their', 'estate',
'was', 'large', ',', 'and‘, 'their', 'residence', 'was', 'at', ...]]

22. Counting
• Use Inaugural Address text.
• >>> from nltk.book import text4
• Counting vocabulary: the length of a
text from start to fnish
• >>> len(text4)
• 145735
• How many distinct words?
• >>> len(set(text4)) #types
• 9754
• Richness of the text.
• >>> len(text4) / len(set(text4))
• 14.941049825712529
• >>> 100 * text4.count('democracy') /
len(text4)
• 0.03568120218204275

23. Positions of a Word inText
Lexical Dispersion Plot

24. List Elements Operations
• List comprehension
– >>> len(set([word.lower() for word in
text4 if len(word)>5]))
– 7339
– >>> [w.upper() for w in text4[0:5]]
– ['FELLOW', '-', 'CITIZENS', 'OF', 'THE']
• Loops and conditionals
• For word in text4[0:5]:
if len(word)<5 and word.endswith('e'):
print word, ' is short and ends with e‘
elif word.istitle():
print word, ' is a titlecase word‘
else:
print word, 'is just another word'

25. Brown Corpus
• First million-word electronic corpus of English
• Created at Brown University in 1961
• Text from 500 sources, categorized by genre
• >>> from nltk.corpus import brown
• >>> print(brown.categories())
• ['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor‘,
'learned', 'lore', 'mystery', 'news', 'religion‘, 'reviews', 'romance', 'science_fiction']

26. Brown Corpus – RetrieveWords by Category
• >>> from nltk.corpus import brown
• >>> news_words = brown.words(categories = "news")
• >>> print(news_words)
• ['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', 'Friday', 'an', 'investigation',
'of', "Atlanta's“, 'recent', 'primary', 'election', 'produced', ...]

27. Brown Corpus – RetrieveWords by Category
• >>> adv_words = brown.words(categories = "adventure")
• >>> print(adv_words)
• ['Dan', 'Morgan', 'told', 'himself', 'he', 'would‘, 'forget', 'Ann', 'Turner', '.', ...]
• >>> reli_words = brown.words(categories = "religion")
• >>> print(reli_words)
• ['As', 'a', 'result', ',', 'although', 'we', 'still', 'make', 'use', 'of', 'this', 'distinction', ',',...]

28. Frequency Distribution
• Records how often each item occurs in a list of words
• Frequency distribution over words
• Basically a dictionary with some extra functionality
• init creates a frequency distribution from a list of words

29. Frequency Distribution
• >>>news_words = brown.words(categories = "news")
• >>>fdist = nltk.FreqDist(news_words)
• >>>print("shoe:", fdist["shoe"])
• >>>print("the: ", fdist["the"])

30. Frequency Distribution
• # show the 10 most frequent words & frequencies
• >>>fdist.tabulate(10)
• the , . Of and to a in for The
• 5580 5188 4030 2849 2146 2116 1993 1893 943 806

31. Plot Frequency Distribution
• Create a plot of the 10 most frequent words
• >>>fdist.plot(10)

32. Stylistics
• Systematic differences between genres
• Brown corpus with its categories is a convenient resource
• Is there a difference in how the modal verbs (can, could, may, might,
must, will) are used in the genres?
• Let us look at the frequency distribution

33. Stylistics
• from nltk import FreqDist
• # Define modals of interest
• >>>modals = ["may", "could", "will"]
• # Define genres of interest
• >>>genres = ["adventure", "news",
"government", "romance"]
• # count how often they occur in the genres
of interest
• >>>for g in genres:
• >>>words = brown.words(categories = g)
• >>>fdist = FreqDist([w.lower() for w in
words
• >>> if w.lower() in modals])
• >>>print g, fdist

34. Conditional Frequency Distributions
• >>>from nltk import ConditionalFreqDist
• >>>cfdist = ConditionalFreqDist()
• >>>for g in genres:
words = brown.words(categories = g)
for w in words
if w.lower() in modals:
cfdist[g].inc(w.lower())
• >>> cfdist.tabulate()
could may will
Adventure 154 7 51
Government 38 179 244
News 87 93 389
Romance 195 11 49
• >>>cfdist.plot(title="Modals in various Genres")

35. Conditional Frequency Distributions

36. Processing RawText
• Assume you have a text file on your disk...
• # Read the text
• >>> path = "holmes.txt“
• >>> f = open(path)
• >>> rawText = f.read()
• >>> f.close()
• >>> print(rawText[:165])
• THE ADVENTURES OF SHERLOCK HOLMES
• By
• SIR ARTHUR CONAN DOYLE
I. A Scandal in Bohemia
II.The Red-headed League

37. SentenceTokenization
• # Split the text up into sentences
• >>> sents = nltk.sent_tokenize(raw)
• >>> print(sents[20:22])
• ['I had seen little of Holmes lately.', 'My marriage had drifted usrnaway from
each other.‘, ...]

38. WordTokenization
• >>># Tokenize the sentences using nltk
• >>>tokens = []
• >>>for sent in sents:
tokens += nltk.word_tokenize(sent)
• >>>print(tokens[300:350])
• [’such’, ’as’, ’his’, ’.’, ’And’, ’yet’, ’there’, ’was’, ’but’, ’one’, ’woman’, ’to’, ’him’, ’,’, ’and’, ’that’,
’woman’, ’was’, ’the’, ’late’, ’Irene’, ’Adler’, ’,’, ’of’, ’dubious’, ’and’, ’questionable’, ’memory’,
...]

39. Creating aText Object
• Using a list of tokens, we can create an nltk.Text object for a document.
• Collocations = terms that occur together unusually often
• Concordance view = shows the contexts in which a token occurs

40. Creating aText Object
• >>># Create a text object
• >>>text = nltk.Text(tokens)
• >>># Do stuff with the text object
• >>>print(text.collocations())
• Sherlock Holmes; said Holmes; St. Simon; Baker Street; Lord St.; St. Clair; Mr.
Holmes; HosmerAngel; Irene Adler; Miss Hunter; young lady; Briony Lodge; Stoke
Moran; Neville St.; Miss Stoner; ScotlandYard; could see; Mr. Holmes.; Boscombe
Pool; Mr. Rucastle

41. ConcordanceView
• >>>print(text.concordance("Irene"))
• >>>Building index...
• >>>Displaying 17 of 17 matches:
• to love for IreneAdler . All emotions , and that one
• was the late IreneAdler , of dubious and questionable
• dventuress , IreneAdler .The name is no doubt familia
• nd . " " And IreneAdler ? " "Threatens to send them t
• se , of Miss IreneAdler . " " Quite so ; but the seque
• And what of IreneAdler ? " I asked . " Oh , she has t
• tying up of IreneAdler , spinster , to Godfrey Norton
• ction . Miss Irene , or Madame , rather , returns from
• ...

42. Annotated Corpora
• Example -The/at Fulton/np-tl County/nn-tl Grand/jj-tl Jury/nn-tl said/vbd
Friday/nr an/at investigation/nn ...
• Some corpora come with annotations - POS tags, parse trees,...
• NLTK provides convenient access to these corpora (get the text + annotations)
• DependencyTree Bank (e.g. Penn): collection of (dependency-) parsed sentences
(manually annotated), can be used for training a statistical parser or parser
evaluation

43. WordNet
• Structured, semantically oriented English dictionary
• Synonyms, antonyms, hyponims, hypernims, depth of a synset, trees, entailments,
etc.
• >>> from nltk.corpus import wordnet as wn
• >>> wn.synsets('motorcar')
• [Synset('car.n.01')]
• >>> wn.synset('car.n.01').lemma_names
• ['car', 'auto', 'automobile', 'machine', 'motorcar']

44. WordNet
• >>> wn.synset('car.n.01').definition
• 'a motor vehicle with four wheels; usually propelled by an internal combustion engine'
• >>> for synset in wn.synsets('car')[1:3]:
• ... print synset.lemma_names
• ['car', 'railcar', 'railway_car', 'railroad_car'] ['car', 'gondola']
• >>> wn.synset('walk.v.01').entailments()
• #Walking involves stepping
• [Synset('step.v.01')]

45. Getting InputText - HTML
• >>> from urllib import urlopen
• >>> url = "http://www.bbc.co.uk/news/science-environment-21471908"
• >>> html = urlopen(url).read()
• html[:60]
• >>> raw = nltk.clean_html(html)
• '<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML+RDFa 1.0//EN" "http'
• >>> tokens = nltk.word_tokenize(raw)
• >>> tokens[:15]
• ['BBC', 'News', '-', 'Exoplanet', 'Kepler', '37b', 'is', 'tiniest‘, 'yet', '-', 'smaller', 'than', 'Mercury', 'Accessibility',
'links‘]

46. Getting InputText - User
• >>> s = raw_input("Enter some text: ")
• Use your own files on disk
• >>> f = open('C:DataFilesUK_natl_2010_en_Lab.txt')
• >>> raw = f.read()
• >>> print raw[:100]
• #Foreword by Gordon Brown
• This General Election is fought as our troops are bravely fighting to def

47. Import Files as Corpus
• >>> from nltk.corpus import PlaintextCorpusReader
• >>> corpus_root = "C:/Data/Files/"
• >>> wordlists = PlaintextCorpusReader(corpus_root, '.*.txt')
• >>> wordlists.fileids()[:3]
• ['UK_natl_1987_en_Con.txt', 'UK_natl_1987_en_Lab.txt',
• 'UK_natl_1987_en_LibSDP.txt']
• >>> wordlists.words('UK_natl_2010_en_Lab.txt')
• ['#', 'Foreword', 'by', 'Gordon', 'Brown', '.', 'This', ...]

48. Stemming
• Strip off affixes
• >>>porter = nltk.PorterStemmer()
• >>>[porter.stem(t) for t in tokens]
• Porter stemmer lying - lie, women - women
• >>>lancaster = nltk.LancasterStemmer()
• >>>[lancaster.stem(t) for t in tokens]
• Lancaster stemmer lying - lying, women - wom

49. Lemmatization
• Removes affixes if in dictionary
• >>>wnl = nltk.WordNetLemmatizer()
• >>>[wnl.lemmatize(t) for t in tokens]
• lying - lying, women - woman

50. Write Output to File
• Save separated sentences text to a new file
• >>>output_file = open('C:DataFilesoutput.txt', 'w')
• >>>words = set(sents)
• >>>for word in sorted(words):
• >>> output_file.write(word + "n")
• To write non-text data, first convert it to string - str()
• Avoid filenames that contain space characters or that are identical except for
case distinctions

51. Part of SpeechTagging
• POSTagging - Process of classifying words into their parts of speech &
labelling them accordingly
– Words grouped into classes, such as nouns, verbs, adjectives, and adverbs
• Parts of speech are also known as word classes or lexical categories
• The collection of tags used for a particular task is known as a tagset

52. Part of SpeechTagging
• NLTK tags text automatically
– Predicting the behaviour of previously unseen words
– Analyzing word usage in corpora
– Text-to-speech systems
– Powerful searches
– Classification

53. Tagging Methods
• Default tagger
• Regular expression tagger
• Unigram tagger
• N-gram taggers

54. Tagging Methods
• Can be combined using a technique known as backoff
– when a more specialized model (such as a bigram tagger) cannot assign a tag
in a given context, we backoff to a more general model (such as a unigram
tagger)
• Taggers can be trained and evaluated using tagged corpora

55. Tagging Examples
• Some corpora already tagged
• >>> nltk.corpus.brown.tagged_words()
• [('The', 'AT'), ('Fulton', 'NP-TL'), ...]
• A simple example
• >>> nltk.pos_tag(text)
• [('And', 'CC'), ('now', 'RB'), ('for', 'IN'), ('something', 'NN'), ('completely', 'RB'), ('different', 'JJ')]
– CC is coordinating conjunction; RB is adverb; IN is preposition; NN is noun; JJ is adjective
– Lots of others - foreign term, verb tenses, “wh” determiner etc

56. Tagging Examples
• An example with homonyms
• >>> text = nltk.word_tokenize("They refuse to permit us to obtain the
refuse permit")
• >>> nltk.pos_tag(text)
• [('They', 'PRP'), ('refuse', 'VBP'), ('to', 'TO'), ('permit‘, 'VB'), ('us', 'PRP'),
('to', 'TO'), ('obtain', 'VB'), ('the‘, 'DT'), ('refuse', 'NN'), ('permit', 'NN')]

57. UnigramTagging
• Unigram tagging - nltk.UnigramTagger()
– Assign the tag that is most likely for that particular token
– Train it specifying tagged sentence data as a parameter when we initialize the
tagger
– Separate training and testing data

58. N-gramTagging
• Context is the current word together with the part-of-speech
• Tags of the n-1 preceding tokens
• Evaluate performance
• Contexts that were not present in the training data – accuracy vs. Coverage
• Combine taggers

59. Information Extraction
• Search large bodies of unrestricted
text for specific types of entities and
relations
• Move these in well-organized
databases
• Use these databases to find answers
for specific questions

60. Information Extraction - Steps
• Segmenting, tokenizing, and part-of-speech tagging the text
• Search resulting data for specific types of entity
• Examine entities that are mentioned near one another in the text to
determine if specific relationships hold between those entities

61. Chunking – Shallow Parsing
• Analyzes a sentence to identify the constituents noun groups, verbs, verb groups etc
• However, it does not specify their internal structure, nor their role in the main sentence
• The smaller boxes show word-level tokenization and part-of-speech tagging, while large
boxes show higher-level chunking
• Each of these larger boxes is called a chunk
• Like tokenization, which omits whitespace, chunking usually selects a subset of the tokens
• Like tokenization, the pieces produced by a chunker do not overlap in the source text

62. Chunking – Shallow Parsing

63. Entity Recognition
• Entity recognition performed using chunkers
– Segment multi-token sequences and label them with the appropriate entity type
– ORGANIZATION, PERSON, LOCATION, DATE,TIME, MONEY, and GPE (geo-political
entity)
• Constructing chunkers
– Use rule-based systems like RegexpParser class from NLTK
– Using machine learning techniques like ConsecutiveNPChunker
– POS tags are very important in this context.

64. Relation Extraction
• Rule-based systems - look for specific patterns in the text that connect
entities and the intervening words
• Machine-learning systems - attempt to learn patterns automatically from
a training corpus

65. ProcessingText
• Choose a particular class label for a given input
• Identify particular features of language data that are salient for classifying it
• Construct models of language that can be used to perform language processing
tasks automatically
• Learn about text/language from these models
• Machine learning techniques
– Decision trees
– Naive Bayes' classifiers
– Maximum entropy classifiers

66. Applications
• Determining the topic of an article or a book
• Deciding if an email is spam or not
• Determining who wrote a text
• Determining the meaning of a word in a particular context
• Open-class classification - set of labels is not defined in advance
• Multi-class classification - each instance may be assigned multiple labels
• Sequence classification - a list of inputs are jointly classified

67. Supervised Classification

68. Example – Identify Gender by Name
• Relevant feature: last letter
• Create a feature set (a dictionary) that maps feature’s names to their values
– >>>def gender_features(word):
– >>>return {'last_letter': word[-1]}
• Import names, shuffle them
– >>>from nltk.corpus import names
– >>>import random
– >>>names = ([(name, 'male') for name in names.words('male.txt')] + [(name, 'female') for
name in names.words('female.txt')])
– >>>random.shuffle(names)

69. Example – Identify Gender by Name
• Divide list of features into training set and test set
– >>>featuresets = [(gender_features(n), g) for (n,g) in names]
– >>>from nltk.classify import apply_features
– >>>#Use apply if you're working with large corpora
– >>>train_set = apply_features(gender_features, names[500:])
– >>>test_set = apply_features(gender_features, names[:500])
• Use training set to train a naive Bayes classier
– >>>classifier = nltk.NaiveBayesClassifier.train(train_set)

70. Example – Identify Gender by Name
• Test the classier on unseen data
– >>> classifier.classify(gender_features('Neo'))
– >>>'male'
– >>> classifier.classify(gender_features('Trinity'))
– >>>'female‘
• >>> print nltk.classify.accuracy(classifier, test_set)
– >>>0.744

71. Example – Identify Gender by Name
• Examine the classier to see which feature is most effective at distinguishing
between classes
• >>> classifier.show_most_informative_features(5)
• Most Informative Features
• last_letter = 'a' female : male = 35.7 : 1.0
• last_letter = 'k' male : female = 31.7 : 1.0
• last_letter = 'f' male : female = 16.6 : 1.0
• last_letter = 'p' male : female = 11.9 : 1.0
• last_letter = 'v' male : female = 10.5 : 1.0

72. Example - Document Classification
• Use corpora where documents have been labelled with categories
– Build classifiers that will automatically tag new documents with appropriate
category labels
• Use the movie review corpus, which categorizes reviews as positive or
negative to construct a list of documents
• Define a feature extractor for documents - feature for each of the most
frequent 2000 words in the corpus
• Define a feature extractor that checks if words are present in a document
• Train a classier to label new movie reviews

73. Document Classification
• Compute accuracy on the test set
– >>> print nltk.classify.accuracy(classifier, test_set)
– >>> 0.79
• Evaluation issues: size of the test set depends on number of labels, their balance and the diversity of the test.
• Show most informative features
• >>> classifier.show_most_informative_features(5)
– Most Informative Features
– contains(outstanding) =True pos : neg = 11.2 : 1.0
– contains(mulan) =True pos : neg = 8.9 : 1.0
– contains(wonderfully) =True pos : neg = 8.5 : 1.0
– contains(seagal) =True neg : pos = 8.3 : 1.0
– contains(damon) =True pos : neg = 6.0 : 1.0

74. Context
• Contextual features often provide powerful clues for
classification
• Context-dependent feature extractor - pass in a complete
(untagged) sentence, along with the index of the target word
• Joint classier models - choose an appropriate labelling for a
collection of related inputs

75. Sequence Classification
• Jointly choose part-of-speech tags for all the words in a given
sentence
• Consecutive classification - find the most likely class label for
the first input, then to use that answer to help find the best
label for the next input, repeat
• Feature extraction function needs to take a history argument
- list of tags predicted so far

76. Hidden Markov Models - HMM
• Use inputs and the history of predicted tags
• Generate a probability distribution over tags
• Combine probabilities to calculate scores for sequences
• Choose tag sequence with the highest probability

77. More Advanced Models
• Maximum Entropy Markov Models
• Linear-ChainConditional Random Field Models

78. References
1. Indurkhya, Nitin and Fred Damerau (eds, 2010) Handbook of Natural Language Processing (Second
Edition)Chapman & Hall/CRC. 2010. (Indurkhya & Damerau, 2010) (Dale, Moisl, & Somers, 2000)
2. Jurafsky, Daniel and James Martin (2008) Speech and Language Processing (Second Edition). Prentice
Hall. (Jurafsky & Martin, 2008)
3. Mitkov, Ruslan (ed, 2003)The Oxford Handbook of Computational Linguistics.Oxford University Press.
(second edition expected in 2010). (Mitkov, 2002)
4. Bird, Steven; Klein, Ewan; Loper, Edward (2009). Natural Language Processing with Python. O'Reilly
Media Inc
5. Perkins, Jacob (2010). PythonText Processing with NLTK 2.0 Cookbook. Packt Publishing
6. Bird, Steven; Klein, Ewan; Loper, Edward; Baldridge, Jason (2008) Proceedings of theThirdWorkshop
on Issues inTeaching Computational Linguistics,ACL

79. ThankYou
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