Introduction: Machine Learning in Digital Libraries
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1 Introduction: Machine Learning in Digital Libraries Compiled by Cornelia Caragea & Sujatha Das Credits for slides: Hofmann, Mihalcea, Mobasher, Mooney, Schutze August 18, 2014
2 Course Title: Case studies in applying Machine Learning for Document Analysis and Retrieval Tasks in Scientific Digital Libraries
3 Quick Survey 1 Background CS/non-CS Industry vs. graduate student Coding experience Prior course on IR? Prior course in ML? 2 Expectations from RuSSIR 3 Expectation from this course
4 Machine Learning: Basics
5 What is Learning"? We learn" many things: Motor skills: walk, ride a bicycle, drive, play tennis or golf, play the piano. Visual concepts: man-made objects, faces, natural objects. Language: Speech recognition, read and write natural languages Spatial knowledge: Navigate between spatial locations, physical layout of a room. Symbolic knowledge: algebra, arithmetic, calculus. Social rules: how to interact with people, animals, machines...
6 Abstract definition of Learning" Definition due to Herbert Simon (1980): Learning" denotes changes in a system that are adaptive in that they enable the system to perform the same task or similar tasks drawn from the same population better over time.
7 Well-posed learning problem Definition due to Tom Mitchell (1998): A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
8 Spam Filtering Suppose your program watches which s you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting? 1 Classifying s as spam or not spam. 2 Watching you label s as spam or not spam. 3 The number (or fraction) of s correctly classified as spam/not spam. 4 None of the above - this is not a machine learning problem.
9 Fields of application Biology: Brain, Development, Evolution, Genetics, Neuroscience. Information Theory: Coding Theory, Entropy. Linguistics: Grammars, Language acquisition Mathematics: Calculus, Linear Algebra, Optimization. Psychology: Analogy, Concept Learning, Curiosity, Discovery, Memory, Reinforcement Philosophy: Causality, Induction, Theory Formation Statistics: Probability Distributions, Estimation, Hypothesis Testing.
10 Some applications of ML in practice If you invent a breakthrough in artificial intelligence, so machines can learn, that is worth 10 Microsofts", Bill Gates quoted in NY Times, Monday March 3, Information extraction from the web: Google, Microsoft, Yahoo Spam filtering Speech/handwriting recognition Object detection/recognition Weather prediction Stock market analysis Search engines (e.g, Google) Ad placement on websites Adaptive website design Credit-card fraud detection Webpage clustering (e.g., Google News) Social Network Analysis Machine Translation (e.g., Google Translate) Recommendation systems (e.g., Netflix, Amazon) Predicting a protein s functions Automatic vehicle navigation Performance tuning of computer systems Predicting good compilation flags for programs... and many more
11 Three fundamental problems in ML Classification: Learning to predict discrete labels associated with given observations. Binary classification: article related to politics or sports Multiclass classification: digit recognition on postal addresses Regression: Learning to predict continuous outputs associated with given observations Example: Predict the sales for a particular coffee-mix product Unsupervised learning: Learning to group objects into categories, without any training labels. Examples: clustering search results into topics
12 Supervised framework Learning Algorithm Output h H s.t. h(x i ) y i h(x test ) = y Hypothesis class H h : X Y x test new example D l = {x i, y i } i=1,n x i X, y i Y iid examples Learning = Search in Hypothesis Class
13 Linearly-separable classifiers Spam vs. not spam Tumor (malignant, benign)
14 Learning from relevant, labeled examples Distinguish a picture of me from a picture of someone else? Provide examples pictures of me and pictures of other people and let a classifier learn to distinguish the two. Determine whether a sentence is grammatical or not? Provide examples of grammatical and ungrammatical sentences and let a classifier learn to distinguish the two. Distinguish cancerous cells from normal cells? Provide examples of cancerous and normal cells and let a classifier learn to distinguish the two.
15 Labeled data ( play prediction)
16 Regression
17 Regression in Medicine [Efron et al., Least Angle Regression, Annals of Statistics, 2004]
18 Unsupervised learning Supervised learning Unsupervised learning
19 Clustering news" articles
20 References Pattern Recognition and Machine Learning, Christopher Bishop. Machine Learning, Tom Mitchell. The Elements of Statistical Learning: Data Mining, Inference and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman.
21 Information Retrieval Systems: Basics
22 What is Information Retrieval (IR) The processing, indexing and retrieval of textual documents. 1 retrieving relevant documents to a query. 2 retrieving from large sets of documents efficiently.
23 Key terms Query: a representation of what the user is looking for - can be a list of words or a phrase. Document: webpage/pdf/image...what user wants to retrieve Collection or corpus: a set of documents Index: a set of data structures that make querying efficient Term: word or concept that appears in a document or a query
24 Typical IR system architecture
25 What is a Digital Library? An electronic library for a focused collection of digital objects Objects can include text, visual material, audio material...(electronic media formats) A type of information retrieval system. Examples: CiteSeer x, PubMed, ACM DL, LawNet...
26 Web Search (Google) vs. Digital Libraries All the Web vs. domain-specific collections/special types of documents Everybody vs. users with special needs Documents vs. Documents, Authors, Connections... For a DL (or a typical IR system) Must assemble a document corpus (spidering the Web or from trusted sources) Document collections need to be constantly updated Different types of search, ranking, and visualization tasks
27 ACM Digital Library
28 PubMed
29 LawNet
30 Components in an IR system Crawl/Acquire* documents that need to be indexed in the system. Index/Search retrieves documents that contain a given query token from the inverted index. Rank scores all retrieved documents according to a relevance metric. Visualize manages interaction with the user:
31 Typical IR Search Given: A corpus A user query in the form of a textual string Find: A ranked set of documents that are relevant to the query
32 Relevance and Ranking Relevance is a subjective judgment and may include: Being on the proper subject. Being timely (recent information). Being authoritative (from a trusted source). Satisfying the goals of the user and his/her intended use of the information (information need) Main relevance criterion: an IR system should fulfill a user s information need Relevance is hard to measure Measures such as Precision, Recall, Mean Reciprocal Rank, NDCG on benchmark collections (example, from TREC)
33 Information Retrieval The processing, indexing and retrieval of documents. 1 retrieving relevant documents to a query. 2 retrieving from large sets of documents efficiently. Matching documents and queries Handling vocabulary mismatch ( PRC" vs China") Handling ambiguity ( bat", jaguar")
34 Implementation and User Experience concerns Fast search (efficient data structures such as inverted indices) What queries are possible? How many results? Query suggestions? Show similar searches? Cluster results, other visualizations?
35 References Introduction to Information Retrieval", C.D. Manning, P. Raghavan, H. Schütze Mining the Web: Discovering Knowledge from Hypertext", Soumen Chakrabarti Search Engines: Information Retrieval in Practice", Bruce Croft, Donald Metzler and Trevor Strohman
36 ML in a practical IR system (CiteSeer)
37 What is CiteSeer/CiteSeer x? Scientific digital library for Computer and Information Science Indexes (free) PostScript and PDF research articles on the Web Automated techniques for acquiring and harvesting research articles Several functionalities: citation indexing, metadata extraction, author disambiguation, citation statistics and trends
38 CiteSeer: Document Search and Metadata
39 CiteSeer: Citations and Trends
40 Author Disambiguation
41 Reference Recommendation
42 Upcoming Lectures/Hands-on How are we applying Machine Learning techniques for these tasks in CiteSeer? Day 1: Introduction + Heritrix (crawling) exercise Day 2: Classification + Weka exercise Day 3: Pagerank/graph-based analysis + Gephi demo Day 4: Topic Modeling + Mallet (LDA) exercise Day 5: Information Extraction + OpenCalais demo Requirements for exercises: Familiarity with Java and Linux environments
43 Crawling the Web
44 The Web (Corpus) by the Numbers 43 million web servers 167 Terabytes of data About 20% text/html 100 Terabytes in deep Web 440 Terabytes in s [Lyman & Varian: How much Information? 2003]
45 Spiders (Robots/Bots/Crawlers) Spidering represents the main difference between traditional IR and IR these days. Start with a comprehensive set of root URL s from which to start the search. Follow all links on these pages recursively to find additional pages. Index/Process all novel found pages in an inverted index as they are encountered. May allow users to directly submit pages to be indexed (and crawled from).
46 Search Strategies Breadth-first Search
47 Search Strategies Breadth-first Search
48 Search Strategies Breadth-first Search
49 Search Strategies Depth-first Search
50 Search Strategies Depth-first Search
51 Search Strategies Depth-first Search
52 Some challenges/concerns Must detect when revisiting a page that has already been spidered (web is a graph not a tree, link canonicalization). Must efficiently index visited pages to allow rapid recognition test. Restricting the crawl (robots.txt, content/anchor-text decide-rules) How often should we crawl? Directed/Focused Spidering
53 Hands-on with Heritrix
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