Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 6 Determining the word types Determining the word types is what part of speech (POS) tagging is all about. A POS tagger parses a full sentence with the goal to arrange it into a dependence tree, where each node corresponds to a word and the parent-child relationship determines which word it depends on. With this tree, it can then make more informed decisions; for example, whether the word "book" is a noun ("This is a good book.") or a verb ("Could you please book the flight?"). You might have already guessed that NLTK will also play a role also in this area. And indeed, it comes readily packaged with all sorts of parsers and taggers. The POS tagger we will use, nltk.pos_tag(), is actually a full-blown classifier trained using manually annotated sentences from the Penn Treebank Project (http://www.cis. upenn.edu/~treebank). It takes as input a list of word tokens and outputs a list of tuples, each element of which contains the part of the original sentence and its part of speech tag: >>> import nltk >>> nltk.pos_tag(nltk.word_tokenize("This is a good book.")) [('This', 'DT'), ('is', 'VBZ'), ('a', 'DT'), ('good', 'JJ'), ('book', 'NN'), ('.', '.')] >>> nltk.pos_tag(nltk.word_tokenize("Could you please book the flight?")) [('Could', 'MD'), ('you', 'PRP'), ('please', 'VB'), ('book', 'NN'), ('the', 'DT'), ('flight', 'NN'), ('?', '.')] The POS tag abbreviations are taken from the Penn Treebank Project (adapted from http://americannationalcorpus.org/OANC/penn.html): POS tag Description Example CC coordinating conjunction or CD cardinal number 2 second DT determiner the EX existential there there are FW foreign word kindergarten IN preposition/subordinating conjunction on, of, like JJ adjective cool JJR adjective, comparative cooler JJS adjective, superlative coolest LS list marker 1) MD modal could, will [ 139 ]
Classification II – Sentiment Analysis POS tag Description Example NN noun, singular or mass book NNS noun plural books NNP proper noun, singular Sean NNPS proper noun, plural Vikings PDT predeterminer both the boys POS possessive ending friend's PRP personal pronoun I, he, it PRP$ possessive pronoun my, his RB adverb however, usually, naturally, here, good RBR adverb, comparative better RBS adverb, superlative best RP particle give up TO to to go, to him UH interjection uhhuhhuhh VB verb, base form take VBD verb, past tense took VBG verb, gerund/present participle taking VBN verb, past participle taken VBP verb, singular, present, non-3D take VBZ verb, third person singular, present takes WDT wh-determiner which WP wh-pronoun who, what WP$ possessive wh-pronoun whose WRB wh-abverb where, when With these tags it is pretty easy to filter the desired tags from the output of pos_ tag(). We simply have to count all the words whose tags start with NN for nouns, VB for verbs, JJ for adjectives, and RB for adverbs. [ 140 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Learning How to Classify with Real-
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Chapter 2 We are using Matplotlib;
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Chapter 2 The last few lines select
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Chapter 2 error = 0.0 for ei in ran
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Chapter 2 We can play around with t
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Chapter 2 Features and feature engi
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Chapter 2 In the preceding screensh
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Chapter 2 Binary and multiclass cla
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Clustering - Finding Related Posts
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Chapter 3 How to do it More robust
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Chapter 3 This means that the first
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Chapter 3 ... post = posts[i] ... i
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Chapter 3 If you have a clear pictu
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Chapter 3 Extending the vectorizer
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Chapter 3 0.0 >>> print(tfidf("b",
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Chapter 3 Flat clustering divides t
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Because the cluster centers are mov
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Chapter 3 'D:\\data\\379\\raw\\comp
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As we have learned previously, we w
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Chapter 3 Position Similarity Excer
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Chapter 3 But before you go there,
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Topic Modeling For those who are in
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Topic Modeling Sparsity means that
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Topic Modeling Although daunting at
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Topic Modeling … for tj,v in t:
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Topic Modeling Finally, we build th
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Topic Modeling Alternatively, we ca
Page 103 and 104: Topic Modeling Topic modeling was f
Page 105 and 106: Classification - Detecting Poor Ans
Page 132 and 133: Classification II - Sentiment Analy
Page 134 and 135: Chapter 6 Getting to know the Bayes
Page 136 and 137: Using Naive Bayes to classify Given
Page 138 and 139: Chapter 6 This denotation "" leads
Page 140 and 141: Chapter 6 Similarly, we do this for
Page 142 and 143: Chapter 6 A quick look at the previ
Page 144 and 145: Chapter 6 To keep our experimentati
Page 146 and 147: Chapter 6 Y = np.zeros(Y.shape[0])
Page 148 and 149: Chapter 6 ° ° Experiment with whe
Page 150 and 151: Chapter 6 We have to be patient whe
Page 152 and 153: Chapter 6 First, we define a range
Page 156 and 157: Chapter 6 Successfully cheating usi
Page 158 and 159: Chapter 6 Our first estimator Now w
Page 160 and 161: Chapter 6 for d in documents: allca
Page 162 and 163: Regression - Recommendations You ha
Page 164 and 165: Chapter 7 The preceding graph shows
Page 166 and 167: Chapter 7 Root mean squared error a
Page 168 and 169: Penalized regression The important
Page 170 and 171: Chapter 7 P greater than N scenario
Page 172 and 173: Chapter 7 So, we can see that the d
Page 174 and 175: Chapter 7 Fortunately, scikit-learn
Page 176 and 177: [ 161 ] Chapter 7 The loading of th
Page 178: Chapter 7 Summary In this chapter,
Page 181 and 182: Regression - Recommendations Improv
Page 185 and 186: Weights Regression - Recommendation
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Page 198 and 199: Chapter 9 Matplotlib provides the c
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Page 202 and 203: Chapter 9 def create_fft(fn): sampl
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Chapter 9 ax.set_yticks(range(len(g
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Chapter 9 On the left-hand side gra
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[ 193 ] Chapter 9 Improving classif
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We get the following promising resu
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Chapter 9 Summary In this chapter,
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Computer Vision - Pattern Recogniti
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Dimensionality Reduction Sketching
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Dimensionality Reduction However, t
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Dimensionality Reduction To underst
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Dimensionality Reduction In order t
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Dimensionality Reduction Asking the
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Dimensionality Reduction n_ feature
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Dimensionality Reduction Sketching
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Dimensionality Reduction Limitation
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Dimensionality Reduction Now, MDS t
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Dimensionality Reduction Of course,
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Big(ger) Data • Your algorithms c
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Big(ger) Data sleep(4) return 2*x @
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Big(ger) Data Looking under the hoo
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Big(ger) Data def write_result(ofna
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Big(ger) Data Amazon Web Services i
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Big(ger) Data In EC2 parlance, a ru
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Big(ger) Data In this system, pip i
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Big(ger) Data Keys, keys, and more
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Big(ger) Data We can use the same j
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Where to Learn More about Machine L
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• Machined Learnings at http://ww
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Index A AcceptedAnswerId attribute
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F false negative 41 false positive
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inary matrix of recommendations, us
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sklearn.naive_bayes package 127 skl
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NumPy Beginner's Guide - Second Edi
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