A machine learning approach to building domain specific search
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The document discusses the application of machine learning in developing domain-specific search engines, which focus on specific segments of online content to provide more relevant search results. It outlines the anatomy of a search engine, emphasizing the processes of web crawling, indexing, and searching, while also comparing vertical search engines to general web search engines. Additionally, the document touches on concepts of vertical search, information extraction, and the evolution of machine learning as an important discipline within artificial intelligence.
A machine learning approach to building domain specific search
- 1. A Machine Learning Approach to Building Domain-Specific Search EnginesPresented By:Niharjyoti SarangiRoll:06/2328th Semester, B.Tech, ITVSSUT, Burla
- 2. Machine Learning Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases.
- 3. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data.
- 4. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.Vertical SearchA vertical search engine, as distinct from a general Web search engine, focuses on a specific segment of online content. The vertical content area may be based on topicality, media type, or genre of content.
- 5. General Web search engines :- Attempt to index large portions of the World Wide Web using a Web crawler.
- 6. Vertical search engines :- Typically use a focused crawler that attempts to index only Web pages that are relevant to a pre-defined topic or set of topics.Domain-Specific SearchDomain-specific search solutions focus on one area of knowledge, creating customized search experiences, that because of the domain's limited corpus and clear relationships between concepts, provide extremely relevant results for searchers.
- 7. Potential Benefits over general search engines:-Greater precision due to limited scopeLeverage domain knowledge including taxonomies and ontologiesSupport specific unique user tasks
- 8. Anatomy of a Search EngineCrawling the webIndexing the webSearching the indicesMajor Data structuresBig FilesRepositoriesDocument IndexLexiconHit ListsForward Index
- 9. Web CrawlingA Web crawler is a computer program that browses the World Wide Web in a methodical, automated manner or in an orderly fashion.
- 10. Other terms for Web crawlers are ants, automatic indexers, bots, and worms or Web spider, Web robot, or—especially in the FOAF community—Web scutter.
- 11. A Web crawler is one type of bot, or software agent. In general, it starts with a list of URLs to visit, called the seeds. As the crawler visits these URLs, it identifies all the hyperlinks in the page and adds them to the list of URLs to visit, called the crawl frontier. URLs from the frontier are recursively visited according to a set of policies.Web Crawling (contd.)
- 12. foodscience.com-Job2JobTitle: Ice Cream GuruEmployer: foodscience.comJobCategory: Travel/HospitalityJobFunction: Food ServicesJobLocation: Upper MidwestContact Phone: 800-488-2611DateExtracted: January 8, 2001Source: www.foodscience.com/jobs_midwest.htmlOtherCompanyJobs: foodscience.com-Job1Information Extraction
- 13. Information Extraction (contd.)As a task:As a task:Filling slots in a database from sub-segments of text.Filling slots in a database from sub-segments of text.October 14, 2002, 4:00 a.m. PTFor years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers."We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“Richard Stallman, founder of the Free Software Foundation, countered saying…October 14, 2002, 4:00 a.m. PTFor years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers."We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“Richard Stallman, founder of the Free Software Foundation, countered saying…NAME TITLE ORGANIZATIONNAME TITLE ORGANIZATION
- 14. Information Extraction (contd.)As a task:Filling slots in a database from sub-segments of text.October 14, 2002, 4:00 a.m. PTFor years, Microsoft CorporationCEOBill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers."We can be open source. We love the concept of shared source," said Bill Veghte, a MicrosoftVP. "That's a super-important shift for us in terms of code access.“Richard Stallman, founder of the Free Software Foundation, countered saying…IENAME TITLE ORGANIZATIONBill GatesCEOMicrosoftBill VeghteVPMicrosoftRichard StallmanfounderFree Soft..
- 15. Information Extraction (contd.)As a familyof techniques:Information Extraction = segmentation + classification + clustering + associationOctober 14, 2002, 4:00 a.m. PTFor years, Microsoft CorporationCEOBill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers."We can be open source. We love the concept of shared source," said Bill Veghte, a MicrosoftVP. "That's a super-important shift for us in terms of code access.“Richard Stallman, founder of the Free Software Foundation, countered saying…Microsoft CorporationCEOBill GatesMicrosoftGatesMicrosoftBill VeghteMicrosoftVPRichard StallmanfounderFree Software Foundation
- 16. Information Extraction (contd.)As a familyof techniques:Information Extraction = segmentation + classification + association + clusteringOctober 14, 2002, 4:00 a.m. PTFor years, Microsoft CorporationCEOBill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers."We can be open source. We love the concept of shared source," said Bill Veghte, a MicrosoftVP. "That's a super-important shift for us in terms of code access.“Richard Stallman, founder of the Free Software Foundation, countered saying…Microsoft CorporationCEOBill GatesMicrosoftGatesMicrosoftBill VeghteMicrosoftVPRichard StallmanfounderFree Software Foundation
- 17. Information Extraction (contd.)As a familyof techniques:Information Extraction = segmentation + classification+ association + clusteringOctober 14, 2002, 4:00 a.m. PTFor years, Microsoft CorporationCEOBill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers."We can be open source. We love the concept of shared source," said Bill Veghte, a MicrosoftVP. "That's a super-important shift for us in terms of code access.“Richard Stallman, founder of the Free Software Foundation, countered saying…Microsoft CorporationCEOBill GatesMicrosoftGatesMicrosoftBill VeghteMicrosoftVPRichard StallmanfounderFree Software Foundation
- 18. NAME TITLE ORGANIZATIONBill GatesCEOMicrosoftBill VeghteVPMicrosoftFree Soft..Richard StallmanfounderInformation Extraction (contd.)As a familyof techniques:Information Extraction = segmentation + classification+ association+ clusteringOctober 14, 2002, 4:00 a.m. PTFor years, Microsoft CorporationCEOBill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation.Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers."We can be open source. We love the concept of shared source," said Bill Veghte, a MicrosoftVP. "That's a super-important shift for us in terms of code access.“Richard Stallman, founder of the Free Software Foundation, countered saying…Microsoft CorporationCEOBill GatesMicrosoftGatesMicrosoftBill VeghteMicrosoftVPRichard StallmanfounderFree Software Foundation****
- 19. Context of ExtractionCreate ontologySpiderFilter by relevanceIESegmentClassifyAssociateClusterDatabaseLoad DBQuery,SearchDocumentcollectionTrain extraction modelsData mineLabel training data
- 20. IE TechniquesClassify Pre-segmentedCandidatesLexiconsSliding WindowAbraham Lincoln was born in Kentucky.Abraham Lincoln was born in Kentucky.Abraham Lincoln was born in Kentucky.member?ClassifierClassifierAlabamaAlaska…WisconsinWyomingwhich class?which class?Try alternatewindow sizes:Context Free GrammarsFinite State MachinesBoundary ModelsAbraham Lincoln was born in Kentucky.Abraham Lincoln was born in Kentucky.Abraham Lincoln was born in Kentucky.Most likely state sequence?NNPVPNPVNNPMost likely parse?ClassifierPPwhich class?VPNPVPBEGINENDBEGINENDS…and beyondAny of these models can be used to capture words, formatting or both.
- 21. Sliding Window GRAND CHALLENGES FOR MACHINE LEARNING Jaime Carbonell School of Computer Science Carnegie Mellon University 3:30 pm 7500 Wean HallMachine learning has evolved from obscurity in the 1970s into a vibrant and popular discipline in artificial intelligence during the 1980s and 1990s. As a result of its success and growth, machine learning is evolving into a collection of related disciplines: inductive concept acquisition, analytic learning in problem solving (e.g. analogy, explanation-based learning), learning theory (e.g. PAC learning), genetic algorithms, connectionist learning, hybrid systems, and so on.CMU UseNet Seminar Announcement
- 22. Sliding Window GRAND CHALLENGES FOR MACHINE LEARNING Jaime Carbonell School of Computer Science Carnegie Mellon University 3:30 pm 7500 Wean HallMachine learning has evolved from obscurity in the 1970s into a vibrant and popular discipline in artificial intelligence during the 1980s and 1990s. As a result of its success and growth, machine learning is evolving into a collection of related disciplines: inductive concept acquisition, analytic learning in problem solving (e.g. analogy, explanation-based learning), learning theory (e.g. PAC learning), genetic algorithms, connectionist learning, hybrid systems, and so on.CMU UseNet Seminar Announcement
- 23. Sliding Window GRAND CHALLENGES FOR MACHINE LEARNING Jaime Carbonell School of Computer Science Carnegie Mellon University 3:30 pm 7500 Wean HallMachine learning has evolved from obscurity in the 1970s into a vibrant and popular discipline in artificial intelligence during the 1980s and 1990s. As a result of its success and growth, machine learning is evolving into a collection of related disciplines: inductive concept acquisition, analytic learning in problem solving (e.g. analogy, explanation-based learning), learning theory (e.g. PAC learning), genetic algorithms, connectionist learning, hybrid systems, and so on.CMU UseNet Seminar Announcement
- 24. Sliding Window GRAND CHALLENGES FOR MACHINE LEARNING Jaime Carbonell School of Computer Science Carnegie Mellon University 3:30 pm 7500 Wean HallMachine learning has evolved from obscurity in the 1970s into a vibrant and popular discipline in artificial intelligence during the 1980s and 1990s. As a result of its success and growth, machine learning is evolving into a collection of related disciplines: inductive concept acquisition, analytic learning in problem solving (e.g. analogy, explanation-based learning), learning theory (e.g. PAC learning), genetic algorithms, connectionist learning, hybrid systems, and so on.CMU UseNet Seminar Announcement
- 25. P(“Wean Hall Rm 5409” = LOCATION) =Prior probabilityof start positionPrior probabilityof lengthProbabilityprefix wordsProbabilitycontents wordsProbabilitysuffix wordsTry all start positions and reasonable lengthsEstimate these probabilities by (smoothed) counts from labeled training data.If P(“Wean Hall Rm 5409” = LOCATION)is above some threshold, extract it. Naïve Bayes Model00 : pm Place : Wean Hall Rm 5409 Speaker : Sebastian Thrun…w t-mw t-1w tw t+nw t+n+1w t+n+mprefixcontentssuffix
- 26. Hidden Markov ModelHMMs are the standard sequence modeling tool in genomics, music, speech, NLP, …Graphical modelFinite state modelSSStransitionst-1tt+1......observations...Generates:State sequenceObservation sequenceOOOtt+1-t1o1 o2 o3 o4 o5 o6 o7 o8Parameters: for all states S={s1,s2,…} Start state probabilities: P(st ) Transition probabilities: P(st|st-1 ) Observation (emission) probabilities: P(ot|st )Training: Maximize probability of training observations (w/ prior)Usually a multinomial over atomic, fixed alphabet
- 27. IE with HMMGiven a sequence of observations:Yesterday Lawrence Saul spoke this example sentence.and a trained HMM:Find the most likely state sequence: (Viterbi)YesterdayLawrence Saulspoke this example sentence.Any words said to be generated by the designated “person name”state extract as a person name:Person name: Lawrence Saul
- 28. Limitations of HMMHMM/CRF models have a linearstructure.Web documents have a hierarchicalstructure.
- 29. Tree Based ModelsExtracting from one web siteUse site-specificformatting information: e.g., “the JobTitle is a bold-faced paragraph in column 2”For large well-structured sites, like parsing a formal languageExtracting from many web sites:Need general solutions to entity extraction, grouping into records, etc.Primarily use content informationMust deal with a wide range of ways that users present data.Analogous to parsing natural languageProblems are complementary:Site-dependent learning can collect training data for a site-independent learner
- 30. Stalker: Hierarchical decomposition of two web sites
- 31. WrapsterCommon representations for web pages include:a rendered imagea DOMtree(tree of HTML markup & text)gives some of the power of hierarchical decompositiona sequence of tokensa bag of words, a sequence of characters, a node in a directed graph, . . .Questions: How can we engineer a system to generalize quickly?How can we explorerepresentational choices easily?
- 32. Wrapsterhtmlhttp://wasBang.org/aboutus.htmlWasBang.com contact info:Currently we have offices in two locations:Pittsburgh, PA
- 33. Provo, UTheadbody…pp“WasBang.com .. info:”ul“Currently..”liliaa“Pittsburgh, PA”“Provo, UT”
- 34. Wrapster Builders Compose `tagpaths’ and `brackets’
- 35. E.g., “extract strings between ‘(‘ and ‘)’ inside a list item inside an unordered list”
- 36. Compose `tagpaths’ and language-based extractors
- 37. E.g., “extract city names inside the first paragraph”
- 38. Extract items based on position inside a rendered table, or properties of the rendered text
- 39. E.g., “extract items inside any column headed by text containing the words ‘Job’ and ‘Title’”
- 40. E.g. “extract items in boldfaced italics”Table Based BuildersHow to represent “links to pages about singers”?Builders can be based on a geometric view of a page.
- 42. References[Bikel et al 1997] Bikel, D.; Miller, S.; Schwartz, R.; and Weischedel, R. Nymble: a high-performance learning name-finder. In Proceedings of ANLP’97, p194-201.[Califf & Mooney 1999], Califf, M.E.; Mooney, R.: Relational Learning of Pattern-Match Rules for Information Extraction, in Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99).[Cohen, Hurst, Jensen, 2002] Cohen, W.; Hurst, M.; Jensen, L.: A flexible learning system for wrapping tables and lists in HTML documents. Proceedings of The Eleventh International World Wide Web Conference (WWW-2002)[Cohen, Kautz, McAllester 2000] Cohen, W; Kautz, H.; McAllester, D.: Hardening soft information sources. Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000).[Cohen, 1998] Cohen, W.: Integration of Heterogeneous Databases Without Common Domains Using Queries Based on Textual Similarity, in Proceedings of ACM SIGMOD-98.[Cohen, 2000a] Cohen, W.: Data Integration using Similarity Joins and a Word-based Information Representation Language, ACM Transactions on Information Systems, 18(3).[Cohen, 2000b] Cohen, W. Automatically Extracting Features for Concept Learning from the Web, Machine Learning: Proceedings of the Seventeeth International Conference (ML-2000).