data science

程序代写代做代考 flex algorithm data science Syntax and Parsing 4: Statistical Parsing

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Syntax and Parsing 4: Statistical Parsing Variation and Ambiguity This time: The problem of ambiguity Lexical ambiguity Structural ambiguity Local vs. global ambiguity Probabilistic Context-Free Grammar (PCFG) Parse probability and string probability Disambiguation Training Two challenges for NLP are variation and ambiguity: Variation: Practically unlimited number of ways of saying the same thing Need huge […]

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程序代写代做代考 data structure algorithm data science Syntax and Parsing 3: Parsing with CFG

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Syntax and Parsing 3: 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 Autumn 2015 1 / 28 Parsing with CFG Consider again the following

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程序代写代做代考 Hidden Markov Mode chain algorithm data science Sequence Labelling 2: Hidden Markov Models

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Sequence Labelling 2: Hidden Markov Models This time: The PoS Tagging Problem Modelling the problem Hidden Markov Models Hidden Markov Model tagging Computing sequence probabilities Finding the most likely path Efficient computation: The Viterbi algorithm Training HMMs Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 29 The PoS Tagging Problem Input: a sequence of

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程序代写代做代考 Hidden Markov Mode Java database data science Information Extraction 1: Chunking & Named Entities

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Information Extraction 1: Chunking & Named Entities Information Extraction This time: What is Information Extraction? Tasks in IE Chunking What is chunking? Chunking vs. parsing IOB labelling Chunking as sequence labelling Suppose that we want to keep an up-to-date record of who currently holds the key executive positions at major companies This sort of information

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程序代写代做代考 data science Semantics 2: Distributional Lexical Similarity

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Semantics 2: Distributional Lexical Similarity The Distributional Hypothesis This time: The Distributional Hypothesis Distributional Models of Meaning Context Features Bag-of-Words vs. Grammatical Relations Comparing Word Meanings Vector-Space Model for Words Impact of Feature Choice Feature Weighting Words that appear in similar contexts tend to have similar meanings — Harris, 1954 A word is characterised by

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程序代写代做代考 Hidden Markov Mode information retrieval algorithm data science Sequence Labelling 1: Part-of-Speech Tagging

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Sequence Labelling 1: Part-of-Speech Tagging This time: Parts of Speech What are they useful for? Open and closed PoS classes PoS Tagsets The Penn Treebank Tagset PoS Tagging Sources of information for tagging A simple unigram tagger Evaluating taggers Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 27 Parts of Speech Words can be

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程序代写代做代考 algorithm data mining finance data science ## STAT GU4243/GR5243 Fall 2016 Applied Data Science

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## STAT GU4243/GR5243 Fall 2016 Applied Data Science ### Project 4 Association mining of music and text ### – from the [million song data](http://labrosa.ee.columbia.edu/millionsong/) project In this project we will explore the association between music features and lyrics words from a subset of songs in the [million song data](http://labrosa.ee.columbia.edu/millionsong/). [Association rule minging](https://en.wikipedia.org/wiki/Association_rule_learning) has a wide

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程序代写代做代考 Hidden Markov Mode information retrieval python data science Introduction to NLE

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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

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程序代写代做代考 Excel data science Document Classification 2: Using Wordlists

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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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程序代写代做代考 data science Document Classification 3: The Naïve Bayes Classifier

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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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