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 […]
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
GLIMPSE: A Tool to Search Through Entire File Systems Udi Manber and Sun Wu TR 93-34 October 1993 DEPARTMENT OF COMPUTER SCIENCE To appear in the 1994 Winter USENIX Technical Conference GLIMPSE: A Tool to Search Through Entire File Systems 1 Udi Manber Department of Computer Science University of Arizona Tucson, AZ 85721 udi@cs.arizona.edu Sun
Fast Text Searching With Errors Sun Wu and Udi Manber TR 91-11 DEPARTMENT OF COMPUTER SCIENCE FAST TEXT SEARCHING WITH ERRORS Sun Wu and Udi Manber1 Department of Computer Science University of Arizona Tucson, AZ 85721 June 1991 ABSTRACT Searching for a pattern in a text file is a very common operation in many applications
Introduction to Artificial Intelligence with Python Language Natural Language Processing Natural Language Processing • • • • • • • • • automatic summarization information extraction language identification machine translation named entity recognition speech recognition text classification word sense disambiguation … Syntax “Just before nine o’clock Sherlock Holmes stepped briskly into the room.” “Just before
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CSCI E-80 Introduction to Artificial Intelligence with Python Harvard Extension School Fall 2020 0. Search 1. Knowledge 2. Uncertainty 3. Optimization 4. Learning 5. Neural Networks 6. Language Announcements Lectures Office Hours Projects Sections Staff Syllabus Ed Discussion Quick Start Guide Lecture 6 Language So far in the course, we needed to shape tasks and data
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Data Mining and Machine Learning Lecture 4 TF-IDF Similarity, the Index and an Example Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives Review IDF, TF-IDF weighting and TF-IDF similarity Practical considerations The word-document index Example calculation Assessing the retrieval Slide 2 Data Mining and Machine Learning Summary of
Data Mining and Machine Learning Lecture 2 Statistical Analysis of Texts Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives Understand different approaches to text-based IR – Rationalism vs Empiricism “Bundles of words” approaches Introduction to zipf.c Statistical analysis of word occurrence in text Zipf’s Law Examples Slide
Data Mining and Machine Learning Topic Analysis Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives Statistical modelling of topics Identifying topics in a document – Latent Dirichlet Allocation (LDA) Topic Spotting – Salience and Usefulness – Example: The AT&T “How May I Help You?” system Slide 2 Data Mining and
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Data Mining and Machine Learning Lecture 3 Stopping, Stemming & TF-IDF Similarity Peter Jančovič Slide 1 Data Mining and Machine Learning Objectives Understand definition and use of Stop Lists Understand motivation and methods of Stemming Understand how to calculate the TF-IDF Similarity between two documents Slide 2 Data Mining and Machine Learning
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