Introduction to NLE Natural Language Engineering Informatics Data Science Group Data Science Group (Informatics) Introduction to NLE Autumn 2015 1 / 34 About This Module An introduction to concepts, tools and techniques in computational processing of natural language You will learn about software technology that can be used to process textual data The focus will […]
Document Classification 1: Scenarios This time: Classification and NLP Document Classification Scenarios: Sentiment Analysis Topic Relevance Detection Document Filtering Technology for Document Classification Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 22 Classification Tasks Many NLP tasks can be view as document classification: document classifier class A class B class C Data Science Group
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Document Classification 2: Using Wordlists This time: Words as features Using vocabulary lists: Sentiment classification Relevance detection Document filtering Scoring documents Decisions criteria Avoding hand-crafted lists Words, sparseness and Zipf’s Law! Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 23 Words as Features Words provide evidence of being in a particular class: excellent —
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Document Classification 3: The Naïve Bayes Classifier This time Some elementary probability theory Random variables Probability distributions Bayes’ Law The Naïve Bayes classifier Parameter learning Multinomial Bayes Bernoulli Bayes The zero probability problem and smoothing Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 21 Learning a Document Classifier Learning to classify data: Classification based
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Syntax and Parsing 3: Parsing with CFG Parsing with CFG This time: Basic recognition/parsing strategies top-down strategy bottom up strategy Problems with simple strategies left recursion empty productions redundant reparsing Earley’s Algorithm: Chart Parsing edges and the chart the fundamental rule Data Science Group (Informatics) NLE/ANLP Basic Recognition/Parsing Strategies There are different strategies that may
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courseScraper-checkpoint In [1]: import urllib2 #specify the url wiki = “http://guide.berkeley.edu/courses/compsci/” page = urllib2.urlopen(wiki) from bs4 import BeautifulSoup soup = BeautifulSoup(page, “lxml”) In [34]: res = [] for t in soup.find_all(‘h3’, class_=”courseblocktitle”): alls = t.find_all() res.append(‘ ‘.join(x.string for x in alls).replace(u’\xa0’, ‘ ‘)) # alls = soup.find_all(‘h3’, class_=”courseblocktitle”)[0].find_all() # ‘ ‘.join(x.string for x in alls).replace(u’\xa0’, ‘ ‘)
Topic 2: Text Documents and Pre-Processing Feature Extraction Sentence segmentation Tokenisation Regular expressions Canonicalisation Stemming and lemmatisation Morphological Processes Inflection and derivation Morphological Analysers The Porter stemmer Finite State models Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 27 Document Pre-Processing document A document B document C feature extractor A features B features C
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Document Classification 4: Evaluation Evaluating Classifiers This time: Accuracy and error rate The confusion matrix Precision and recall Trading off precision and recall The Naïve Bayes decision rule The decision boundary The Receiver Operating Characteristics (ROC) curve Data Science Group (Informatics) NLE/ANLP Binary Classification Will focus on case of binary classification Autumn 2015 1 /
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Document Classification 4: Evaluation This time: Accuracy and error rate The confusion matrix Precision and recall Trading off precision and recall The Naïve Bayes decision rule The decision boundary The Receiver Operating Characteristics (ROC) curve Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 20 Evaluating Classifiers Suppose we want to measure how well a
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Document Classification 1: Scenarios This time: Classification and NLP Document Classification Scenarios: Sentiment Analysis Topic Relevance Detection Document Filtering Technology for Document Classification Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 22 Classification Tasks Many NLP tasks can be view as document classification: document classifier class A class B class C Data Science Group
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