1. 2009.04.29 - SLIDE 1IS 240 – Spring 2009 Prof. Ray Larson University of California, Berkeley School of Information Principles of Information Retrieval Lecture 22: NLP for IR

2. 2009.04.29 - SLIDE 2IS 240 – Spring 2009 Today Review –Cheshire III Design – GRID-based DLs NLP for IR Text Summarization Credit for some of the slides in this lecture goes to Marti Hearst and Eric Brewer

3. 2009.04.29 - SLIDE 3IS 240 – Spring 2009 Grid middleware Chemical Engineering Applications Application Toolkits Grid Services Grid Fabric Climate Data Grid Remote Computing Remote Visualization Collaboratories High energy physics Cosmology Astrophysics Combustion.…. Portals Remote sensors..… Protocols, authentication, policy, instrumentation, Resource management, discovery, events, etc. Storage, networks, computers, display devices, etc. and their associated local services Grid Architecture -- (Dr. Eric Yen, Academia Sinica, Taiwan.)

4. 2009.04.29 - SLIDE 4IS 240 – Spring 2009 Chemical Engineering Applications Application Toolkits Grid Services Grid Fabric Grid middleware Climate Data Grid Remote Computing Remote Visualization Collaboratories High energy physics Cosmology Astrophysics Combustion Humanities computing Digital Libraries … Portals Remote sensors Text Mining Metadata management Search & Retrieval … Protocols, authentication, policy, instrumentation, Resource management, discovery, events, etc. Storage, networks, computers, display devices, etc. and their associated local services Grid Architecture (ECAI/AS Grid Digital Library Workshop) Bio-Medical

5. 2009.04.29 - SLIDE 5IS 240 – Spring 2009 Grid IR Issues Want to preserve the same retrieval performance (precision/recall) while hopefully increasing efficiency (I.e. speed) Very large-scale distribution of resources is a challenge for sub-second retrieval Different from most other typical Grid processes, IR is potentially less computing intensive and more data intensive In many ways Grid IR replicates the process (and problems) of metasearch or distributed search

6. 2009.04.29 - SLIDE 6IS 240 – Spring 2009 Context Environmental Requirements: –Very Large scale information systems Terabyte scale (Data Grid) Computationally expensive processes (Comp. Grid) Digital Preservation Analysis of data, not just retrieval (Data/Text Mining) Ease of Extensibility, Customizability (Python) Open Source Integrate not Re-implement "Web 2.0" – interactivity and dynamic interfaces

7. 2009.04.29 - SLIDE 7IS 240 – Spring 2009 Context Data Grid Layer Data Grid SRB iRODS Digital Library Layer Application Layer Web Browser Multivalent Dedicated Client User Interface Apache+ Mod_Python+ Cheshire3 Protocol Handler Process Management Kepler Cheshire3 Query Results Query Results ExportParse Document Parsers Multivalent,... Natural Language Processing Information Extraction Text Mining Tools Tsujii Labs,... Classification Clustering Data Mining Tools Orange, Weka,... Query Results Search / Retrieve Index / Store Information System Cheshire3 User Interface MySRB PAWN Process Management Kepler iRODS rules Term Management Termine WordNet... Store

8. 2009.04.29 - SLIDE 8IS 240 – Spring 2009 Cheshire3 Object Model UserStore User ConfigStore Object Database Query Record Transformer Records Protocol Handler Normaliser IndexStore Terms Server Document Group Ingest Process Documents Index RecordStore Parser Document Query ResultSet DocumentStore Document PreParser Extracter

9. 2009.04.29 - SLIDE 9IS 240 – Spring 2009 Object Configuration One XML 'record' per non-data object Very simple base schema, with extensions as needed Identifiers for objects unique within a context (e.g., unique at individual database level, but not necessarily between all databases) Allows workflows to reference by identifier but act appropriately within different contexts. Allows multiple administrators to define objects without reference to each other

10. 2009.04.29 - SLIDE 10IS 240 – Spring 2009 Grid Focus on ingest, not discovery (yet) Instantiate architecture on every node Assign one node as master, rest as slaves. Master then divides the processing as appropriate. Calls between slaves possible Calls as small, simple as possible: (objectIdentifier, functionName, *arguments) Typically: ('workflow-id', 'process', 'document-id')

11. 2009.04.29 - SLIDE 11IS 240 – Spring 2009 Grid Architecture Master Task Slave Task 1 Slave Task N Data Grid GPFS Temporary Storage (workflow, process, document) fetch document document extracted data

12. 2009.04.29 - SLIDE 12IS 240 – Spring 2009 Grid Architecture - Phase 2 Master Task Slave Task 1 Slave Task N Data Grid GPFS Temporary Storage (index, load) store index fetch extracted data

13. 2009.04.29 - SLIDE 13IS 240 – Spring 2009 Workflow Objects Written as XML within the configuration record. Rewrites and compiles to Python code on object instantiation Current instructions: –object –assign –fork –for-each –break/continue –try/except/raise –return –log (= send text to default logger object) Yes, no if!

14. 2009.04.29 - SLIDE 14IS 240 – Spring 2009 Workflow example workflow.SimpleWorkflow Unparsable Record ”Loaded Record:” + input.id

15. 2009.04.29 - SLIDE 15IS 240 – Spring 2009 Text Mining Integration of Natural Language Processing tools Including: –Part of Speech taggers (noun, verb, adjective,...) –Phrase Extraction –Deep Parsing (subject, verb, object, preposition,...) –Linguistic Stemming (is/be fairy/fairy vs is/is fairy/fairi) Planned: Information Extraction tools

16. 2009.04.29 - SLIDE 16IS 240 – Spring 2009 Data Mining Integration of toolkits difficult unless they support sparse vectors as input - text is high dimensional, but has lots of zeroes Focus on automatic classification for predefined categories rather than clustering Algorithms integrated/implemented: –Perceptron, Neural Network (pure python) –Naïve Bayes (pure python) –SVM (libsvm integrated with python wrapper) –Classification Association Rule Mining (Java)

17. 2009.04.29 - SLIDE 17IS 240 – Spring 2009 Data Mining Modelled as multi-stage PreParser object (training phase, prediction phase) Plus need for AccumulatingDocumentFactory to merge document vectors together into single output for training some algorithms (e.g., SVM) Prediction phase attaches metadata (predicted class) to document object, which can be stored in DocumentStore Document vectors generated per index per document, so integrated NLP document normalization for free

18. 2009.04.29 - SLIDE 18IS 240 – Spring 2009 Data Mining + Text Mining Testing integrated environment with 500,000 medline abstracts, using various NLP tools, classification algorithms, and evaluation strategies. Computational grid for distributing expensive NLP analysis Results show better accuracy with fewer attributes:

19. 2009.04.29 - SLIDE 19IS 240 – Spring 2009 Applications (1) Automated Collection Strength Analysis Primary aim: Test if data mining techniques could be used to develop a coverage map of items available in the London libraries. The strengths within the library collections were automatically determined through enrichment and analysis of bibliographic level metadata records. This involved very large scale processing of records to: –Deduplicate millions of records –Enrich deduplicated records against database of 45 million –Automatically reclassify enriched records using machine learning processes (Naïve Bayes)

20. 2009.04.29 - SLIDE 20IS 240 – Spring 2009 Applications (1) Data mining enhances collection mapping strategies by making a larger proportion of the data usable, by discovering hidden relationships between textual subjects and hierarchically based classification systems. The graph shows the comparison of numbers of books classified in the domain of Psychology originally and after enhancement using data mining

21. 2009.04.29 - SLIDE 21IS 240 – Spring 2009 Applications (2) Assessing the Grade Level of NSDL Education Material The National Science Digital Library has assembled a collection of URLs that point to educational material for scientific disciplines for all grade levels. These are harvested into the SRB data grid. Working with SDSC we assessed the grade-level relevance by examining the vocabulary used in the material present at each registered URL. We determined the vocabulary-based grade-level with the Flesch-Kincaid grade level assessment. The domain of each website was then determined using data mining techniques (TF-IDF derived fast domain classifier). This processing was done on the Teragrid cluster at SDSC.

22. 2009.04.29 - SLIDE 22IS 240 – Spring 2009 Applications (2) The formula for the Flesch Reading Ease Score: FRES = 206.835 –1.015 ((total words)/(total sentences)) – 84.6 ((total syllables)/(total words)) The Flesch-Kincaid Grade Level Formula: FKGLF = 0.39 * ((total words)/(total sentences)) + 11.8 * ((total syllables)/(total words)) –15.59 The Domain was determined by: –Domains used were based upon the AAAS Benchmarks –Taking in samples from each of the domain areas being examined and produces scored and ranked lists of vocabularies for each domain. –Each token in a document is passed through a lookup function against this table and tallies are calculated for the entire document. –These tallies are then used to rank the order of likelihood of the document being about each topic and a statistical pass of the results returns only those topics that are above in certain threshold.

23. 2009.04.29 - SLIDE 23IS 240 – Spring 2009 Today Natural Language Processing and IR –Based on Papers in Reader and on David Lewis & Karen Sparck Jones “Natural Language Processing for Information Retrieval” Communications of the ACM, 39(1) Jan. 1996 Text summarization: Lecture from Ed Hovy (USC)

24. 2009.04.29 - SLIDE 24IS 240 – Spring 2009 Natural Language Processing and IR The main approach in applying NLP to IR has been to attempt to address –Phrase usage vs individual terms –Search expansion using related terms/concepts –Attempts to automatically exploit or assign controlled vocabularies

25. 2009.04.29 - SLIDE 25IS 240 – Spring 2009 NLP and IR Much early research showed that (at least in the restricted test databases tested) –Indexing documents by individual terms corresponding to words and word stems produces retrieval results at least as good as when indexes use controlled vocabularies (whether applied manually or automatically) –Constructing phrases or “pre-coordinated” terms provides only marginal and inconsistent improvements

26. 2009.04.29 - SLIDE 26IS 240 – Spring 2009 NLP and IR Not clear why intuitively plausible improvements to document representation have had little effect on retrieval results when compared to statistical methods –E.g. Use of syntactic role relations between terms has shown no improvement in performance over “bag of words” approaches

27. 2009.04.29 - SLIDE 27IS 240 – Spring 2009 General Framework of NLP Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

28. 2009.04.29 - SLIDE 28IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation John runs. Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

29. 2009.04.29 - SLIDE 29IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation John runs. John run+s. P-N V 3-pre N plu Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

30. 2009.04.29 - SLIDE 30IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation John runs. John run+s. P-N V 3-pre N plu S NP P-N John VP V run Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

31. 2009.04.29 - SLIDE 31IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation John runs. John run+s. P-N V 3-pre N plu S NP P-N John VP V run Pred: RUN Agent:John Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

32. 2009.04.29 - SLIDE 32IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation John runs. John run+s. P-N V 3-pre N plu S NP P-N John VP V run Pred: RUN Agent:John John is a student. He runs. Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

33. 2009.04.29 - SLIDE 33IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation Domain Analysis Appelt:1999 Tokenization Part of Speech Tagging Term recognition (Ananiadou) Inflection/Derivation Compounding Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

34. 2009.04.29 - SLIDE 34IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation Difficulties of NLP (1) Robustness: Incomplete Knowledge Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

35. 2009.04.29 - SLIDE 35IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation Difficulties of NLP (1) Robustness: Incomplete Knowledge Incomplete Lexicons Open class words Terms Term recognition Named Entities Company names Locations Numerical expressions Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

36. 2009.04.29 - SLIDE 36IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation Difficulties of NLP (1) Robustness: Incomplete Knowledge Incomplete Grammar Syntactic Coverage Domain Specific Constructions Ungrammatical Constructions Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

37. 2009.04.29 - SLIDE 37IS 240 – Spring 2009 Syntactic Analysis General Framework of NLP Morphological and Lexical Processing Semantic Analysis Context processing Interpretation Difficulties of NLP (1) Robustness: Incomplete Knowledge Incomplete Domain Knowledge Interpretation Rules Predefined Aspects of Information Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

38. 2009.04.29 - SLIDE 38IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation Difficulties of NLP (1) Robustness: Incomplete Knowledge (2) Ambiguities: Combinatorial Explosion Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

39. 2009.04.29 - SLIDE 39IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation Difficulties of NLP (1) Robustness: Incomplete Knowledge (2) Ambiguities: Combinatorial Explosion Most words in English are ambiguous in terms of their parts of speech. runs: v/3pre, n/plu clubs: v/3pre, n/plu and two meanings Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

40. 2009.04.29 - SLIDE 40IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation Difficulties of NLP (1) Robustness: Incomplete Knowledge (2) Ambiguities: Combinatorial Explosion Structural Ambiguities Predicate-argument Ambiguities Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

41. 2009.04.29 - SLIDE 41IS 240 – Spring 2009 Structural Ambiguities (1)Attachment Ambiguities John bought a car with large seats. John bought a car with $3000. (2) Scope Ambiguities young women and men in the room (3)Analytical Ambiguities Visiting relatives can be boring. The manager of Yaxing Benz, a Sino-German joint venture The manager of Yaxing Benz, Mr. John Smith John bought a car with Mary. $3000 can buy a nice car. Semantic Ambiguities(1) Semantic Ambiguities(2) Every man loves a woman. Co-reference Ambiguities Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

42. 2009.04.29 - SLIDE 42IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation Difficulties of NLP (1) Robustness: Incomplete Knowledge (2) Ambiguities: Combinatorial Explosion Structural Ambiguities Predicate-argument Ambiguities Combinatorial Explosion Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

43. 2009.04.29 - SLIDE 43IS 240 – Spring 2009 Note: Ambiguities vs Robustness More comprehensive knowledge: More Robust big dictionaries comprehensive grammar More comprehensive knowledge: More ambiguities Adaptability: Tuning, Learning Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

44. 2009.04.29 - SLIDE 44IS 240 – Spring 2009 Framework of IE IE as compromise NLP Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

45. 2009.04.29 - SLIDE 45IS 240 – Spring 2009 Syntactic Analysis General Framework of NLP Morphological and Lexical Processing Semantic Analysis Context processing Interpretation Difficulties of NLP (1) Robustness: Incomplete Knowledge Incomplete Domain Knowledge Interpretation Rules Predefined Aspects of Information Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

46. 2009.04.29 - SLIDE 46IS 240 – Spring 2009 Syntactic Analysis General Framework of NLP Morphological and Lexical Processing Semantic Analysis Context processing Interpretation Difficulties of NLP (1) Robustness: Incomplete Knowledge Incomplete Domain Knowledge Interpretation Rules Predefined Aspects of Information Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

47. 2009.04.29 - SLIDE 47IS 240 – Spring 2009 Techniques in IE (1) Domain Specific Partial Knowledge: Knowledge relevant to information to be extracted (2) Ambiguities: Ignoring irrelevant ambiguities Simpler NLP techniques (4) Adaptation Techniques: Machine Learning, Trainable systems (3) Robustness: Coping with Incomplete dictionaries (open class words) Ignoring irrelevant parts of sentences Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

48. 2009.04.29 - SLIDE 48IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Anaysis Context processing Interpretation Open class words: Named entity recognition (ex) Locations Persons Companies Organizations Position names Domain specific rules:, Inc. Mr.. Machine Learning: HMM, Decision Trees Rules + Machine Learning Part of Speech Tagger FSA rules Statistic taggers 95 % F-Value 90 Domain Dependent Local Context Statistical Bias Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

49. 2009.04.29 - SLIDE 49IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Anaysis Context processing Interpretation FASTUS 1.Complex Words: Recognition of multi-words and proper names 2.Basic Phrases: Simple noun groups, verb groups and particles 3.Complex phrases: Complex noun groups and verb groups 4.Domain Events: Patterns for events of interest to the application Basic templates are to be built. 5. Merging Structures: Templates from different parts of the texts are merged if they provide information about the same entity or event. Based on finite states automata (FSA) Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

50. 2009.04.29 - SLIDE 50IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Anaysis Context processing Interpretation FASTUS 1.Complex Words: Recognition of multi-words and proper names 2.Basic Phrases: Simple noun groups, verb groups and particles 3.Complex phrases: Complex noun groups and verb groups 4.Domain Events: Patterns for events of interest to the application Basic templates are to be built. 5. Merging Structures: Templates from different parts of the texts are merged if they provide information about the same entity or event. Based on finite states automata (FSA) Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

51. 2009.04.29 - SLIDE 51IS 240 – Spring 2009 General Framework of NLP Morphological and Lexical Processing Syntactic Analysis Semantic Analysis Context processing Interpretation FASTUS 1.Complex Words: Recognition of multi-words and proper names 2.Basic Phrases: Simple noun groups, verb groups and particles 3.Complex phrases: Complex noun groups and verb groups 4.Domain Events: Patterns for events of interest to the application Basic templates are to be built. 5. Merging Structures: Templates from different parts of the texts are merged if they provide information about the same entity or event. Based on finite states automata (FSA) Slides from Prof. J. Tsujii, Univ of Tokyo and Univ of Manchester

52. 2009.04.29 - SLIDE 52IS 240 – Spring 2009 Using NLP Strzalkowski (in Reader) TextNLPrepres Dbase search TAGGER NLP: PARSERTERMS

53. 2009.04.29 - SLIDE 53IS 240 – Spring 2009 Using NLP INPUT SENTENCE The former Soviet President has been a local hero ever since a Russian tank invaded Wisconsin. TAGGED SENTENCE The/dt former/jj Soviet/jj President/nn has/vbz been/vbn a/dt local/jj hero/nn ever/rb since/in a/dt Russian/jj tank/nn invaded/vbd Wisconsin/np./per

54. 2009.04.29 - SLIDE 54IS 240 – Spring 2009 Using NLP TAGGED & STEMMED SENTENCE the/dt former/jj soviet/jj president/nn have/vbz be/vbn a/dt local/jj hero/nn ever/rb since/in a/dt russian/jj tank/nn invade/vbd wisconsin/np./per

55. 2009.04.29 - SLIDE 55IS 240 – Spring 2009 Using NLP PARSED SENTENCE [assert [[perf [have]][[verb[BE]] [subject [np[n PRESIDENT][t_pos THE] [adj[FORMER]][adj[SOVIET]]]] [adv EVER] [sub_ord[SINCE [[verb[INVADE]] [subject [np [n TANK][t_pos A] [adj [RUSSIAN]]]] [object [np [name [WISCONSIN]]]]]]]]]

56. 2009.04.29 - SLIDE 56IS 240 – Spring 2009 Using NLP EXTRACTED TERMS & WEIGHTS President 2.623519 soviet 5.416102 President+soviet 11.556747 president+former 14.594883 Hero 7.896426 hero+local 14.314775 Invade 8.435012 tank 6.848128 Tank+invade 17.402237 tank+russian 16.030809 Russian 7.383342 wisconsin 7.785689

57. 2009.04.29 - SLIDE 57IS 240 – Spring 2009 Same Sentence, different sys INPUT SENTENCE The former Soviet President has been a local hero ever since a Russian tank invaded Wisconsin. TAGGED SENTENCE (using uptagger from Tsujii) The/DT former/JJ Soviet/NNP President/NNP has/VBZ been/VBN a/DT local/JJ hero/NN ever/RB since/IN a/DT Russian/JJ tank/NN invaded/VBD Wisconsin/NNP./.

58. 2009.04.29 - SLIDE 58IS 240 – Spring 2009 Same Sentence, different sys CHUNKED Sentence (chunkparser – Tsujii) (TOP (S (NP (DT The) (JJ former) (NNP Soviet) (NNP President) ) (VP (VBZ has) (VP (VBN been) (NP (DT a) (JJ local) (NN hero) ) (ADVP (RB ever) ) (SBAR (IN since) (S (NP (DT a) (JJ Russian) (NN tank) ) (VP (VBD invaded) (NP (NNP Wisconsin) ) ) ) ) ) ) (..) )

59. 2009.04.29 - SLIDE 59IS 240 – Spring 2009 Same Sentence, different sys Enju Parser ROOTROOTROOTROOT-1ROOTbeenbeVBNVB5 beenbeVBNVB5ARG1PresidentpresidentNNPNNP3 beenbeVBNVB5ARG2heroheroNNNN8 aaDTDT6ARG1heroheroNNNN8 aaDTDT11ARG1tanktankNNNN13 locallocalJJJJ7ARG1heroheroNNNN8 ThetheDTDT0ARG1PresidentpresidentNNPNNP3 formerformerJJJJ1ARG1PresidentpresidentNNPNNP3 RussianrussianJJJJ12ARG1tanktankNNNN13 SovietsovietNNPNNP2MODPresidentpresidentNNPNNP3 invadedinvadeVBDVB14ARG1tanktankNNNN13 invadedinvadeVBDVB14ARG2WisconsinwisconsinNNPNNP15 hashaveVBZVB4ARG1PresidentpresidentNNPNNP3 hashaveVBZVB4ARG2beenbeVBNVB5 sincesinceININ10MODbeenbeVBNVB5 sincesinceININ10ARG1invadedinvadeVBDVB14 evereverRBRB9ARG1sincesinceININ10

60. 2009.04.29 - SLIDE 60IS 240 – Spring 2009 NLP & IR Indexing –Use of NLP methods to identify phrases Test weighting schemes for phrases –Use of more sophisticated morphological analysis Searching –Use of two-stage retrieval Statistical retrieval Followed by more sophisticated NLP filtering

61. 2009.04.29 - SLIDE 61IS 240 – Spring 2009 NPL & IR Lewis and Sparck Jones suggest research in three areas –Examination of the words, phrases and sentences that make up a document description and express the combinatory, syntagmatic relations between single terms –The classificatory structure over document collection as a whole, indicating the paradigmatic relations between terms and permitting controlled vocabulary indexing and searching –Using NLP-based methods for searching and matching

62. 2009.04.29 - SLIDE 62IS 240 – Spring 2009 NLP & IR Issues Is natural language indexing using more NLP knowledge needed? Or, should controlled vocabularies be used Can NLP in its current state provide the improvements needed How to test

63. 2009.04.29 - SLIDE 63IS 240 – Spring 2009 NLP & IR New “Question Answering” track at TREC has been exploring these areas –Usually statistical methods are used to retrieve candidate documents –NLP techniques are used to extract the likely answers from the text of the documents