Introduction to information system Model Evaluation Metrics Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science MASH • Maths • And • Stats • Help • MASH • mash@lincoln.ac.uk • In The Library mailto:mash@lincoln.ac.uk • What Is A Model Evaluation Metric? • Mean Absolute Error (MAE) • Root Mean Squared Error (RMSE) […]
Introduction to information system R Graphics lattice & ggplot2 Bowei Chen, Deema Hafeth and Jingmin Huang School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science 2016 – 2017 Workshop Today’s Objectives • Study the following slides: – Part I: lattice – Part II: ggplot2 • Do the exercises 1-11 • Do the additional exercises
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Microsoft Learning Experiences CMP3036M/CMP9063M Data Science 2016 – 2017 Semester B Week 02 Workshop In this workshop, you will be given the Azure Pass and start to use Azure and its ML Studio. It should be noted that Azure ML Studio has a free trial version, whose workspace is based on the South Central US
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CMP3036M Data Science Page 1 of 3 University of Lincoln School of Computer Science 2016 – 2017 Assessment Item 1 of 2 Briefing Document Title: CMP3036M Data Science Indicative Weighting: 50% Learning Outcomes On successful completion of this component a student will have demonstrated competence in the following areas: LO1 Critically apply fundamental concepts
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y # extract sentences from document get_sentences
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Introduction to information system Data Structures in R Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science 2016 – 2017 Workshop Vectors (1/3) Vectors are one-dimensional arrays that can hold numeric data, character data, or logical data. The combine function c() is used to form the vector. Note that the data in
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Semantics 1: Lexical Meaning & WordNet This time: Language and meaning Lexical Semantics Lexemes, Lemmas and Word Senses Lexical Relations Homonony/Polysemy Hyponomy/Hypernymy Synonymy/Antonymy Holonymy/Meronymy WordNet WordNet Synsets WordNet Hierarchies WordNet-Based Similarity Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 24 Language and Meaning 1 Lexical Semantics The meaning of individual words 2 Phrasal/Sentential Semantics
Document Similarity This time: Characterising document topic Stop words The role of word frequency TF-IDF term weighting From words to phrasal terms Measuring document similarity The vector space model Cosine similarity Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 26 Characteristics of a document Consider problem of characterising what a document is about (its
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Semantics 2: Distributional Lexical Similarity 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 Lin’s Similarity Measure Some Examples Data Science Group (Informatics) NLE/ANLP Autumn 2015 1 / 30 The Distributional Hypothesis Words that appear in
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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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