lecture06.pptx LECTURE 6 Vector Representaton and Models for Word Embeddings Arkaitz Zubiaga, 24 th January, 2018 2 Vector space models for language representaton. Word embeddings. SVD: Singular Value Decompositon. Iteraton based models. CBOW and skip-gram models. Word2Vec and Glove. LECTURE 6: CONTENTS VECTOR SPACE MODELS 4 Goal: compute […]
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Assignment4 Document Analysis Assignment 4: Node Label Prediction¶ Your Information¶ Please fill in the following information: Name: [Your name] Uni id: [Your uid] Overview¶ The task in Assignment 4 is to train a document classifier which predicts the label of documents, while taking into account the network structure between documents. You are given a dataset
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lecture09.pptx LECTURE 9 Sequence Classifcaaon and Part-Of-Speech Tagging Arkaitz Zubiaga, 5 th February, 2018 2 Sequence Classifcaaon Sequence Classifers: Hidden Markov Models (HMM). Maximum Entropy Markov Models (MEMM). Condiaonal Random Fields (CRF). Using Sequence Classifers for Part-of-Speech (POS) Tagging. LECTURE 9: CONTENTS 3 Someames, classifcaatin tif items in
Seminar presentation CS918 NLP Seminar Mathematical primer for neural networks Elena Kochkina University of Warwick Outline • Linear algebra • Differentiation • Cost function • Optimisation via Gradient Descent Refreshing linear algebra A = ↵11 ↵12 ↵21 ↵22 � I = 2 66 4 1 0 .. 0 0 1 .. 0 .. ..
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Adaptive Supertagging [Clark & Curran, 2007] Start with an initial prob. cuto↵ � He reads the book NP (S [pss]\NP)/NP NP/N N N (S\NP)/NP NP/NP (S\NP)/NP N/N S\NP N/N NP/NP (S [pt]\NP)/NP (S [dcl ]\NP)/NP Adaptive Supertagging [Clark & Curran, 2007] Prune a category, if its probability is below � times the prob. of the
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lecture10.pptx LECTURE 10 Grammars and Parsing Arkaitz Zubiaga, 7 th February, 2018 2 What is parsing? What are consttuencies and dependency structures? Probabilistc parsing: Context Free Grammars (CFG). Lexicalised parsing. Dependency parsing. LECTURE 10: CONTENTS 3 Parsing: process of recognising a sentence and assigning syntactc structure to it.
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PowerPoint Presentation LECTURE 9 Sequence Classifcatin and Part-Of-Speech Tagging Arkaitz Zubiaga, 5th February, 2018 2 Sequence Classifcatin Sequence Classifers: Hidden Markiv Midels (HMM). Maximum Entripy Markiv Midels (MEMM). Cinditinal Randim Fields (CRF). Using Sequence Classifers fir Part-if-Speech (POS) Tagging. LECTURE 9: CONTENTS 3 Simetmes, classifcaaton of items in
Option One Title Here ANLY-601 Advanced Pattern Recognition Spring 2018 L14 – Principal Components Feature Extraction for Signal Representation Principal Components • Principal component analysis is a classical statistical technique that eliminates correlation among variables and is can be used to reduce data dimensionality for – Visualization – Data compression (transform coding) – Dimension reduction
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L20 – Neural Networks k-means clustering (recap) • Idea: try to estimate k cluster centers by minimizing “distortion” • Define distortion as: • rnk is 1 for the closest cluster mean to xn. • Each point xn is the minimum distance from its closet center. • How do we learn the cluster means? • Need
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Data Analyst – Take Home Assignment I. SQL The goal of this section is to prepare data from an SQL table for analysis. The table is called ‘edgar_contracts’ and the schema is as follows: [ filing_id INT (primary key) , content CHAR , submission_date DATE , filing_company VARCHAR(40) , num_contracts SMALLINT ] The ‘content’ field
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