1. 2009.05.06 - SLIDE 1IS 240 – Spring 2009 Prof. Ray Larson University of California, Berkeley School of Information Principles of Information Retrieval Lecture 26: CLIR

2. 2009.05.06 - SLIDE 2IS 240 – Spring 2009 Today Review –NLP for IR –Text Summarization Cross-Language Information Retrieval –Introduction –Cross-Language EVIs Thanks to Doug Oard (University of Maryland) Fredric Gey and Aitao Chen for some of the material in this lecture

3. 2009.05.06 - SLIDE 3IS 240 – Spring 2009 Today Review –NLP for IR –Text Summarization Cross-Language Information Retrieval –Introduction –Cross-Language EVIs Thanks to Doug Oard (University of Maryland) Fredric Gey and Aitao Chen for some of the material in this lecture

4. 2009.05.06 - SLIDE 4IS 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

5. 2009.05.06 - SLIDE 5IS 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

6. 2009.05.06 - SLIDE 6IS 240 – Spring 2009 Mark’s idle speculation What people think is going on always Keywords NLP From Mark Sanderson, University of Sheffield

7. 2009.05.06 - SLIDE 7IS 240 – Spring 2009 Mark’s idle speculation What’s usually actually going on Keywords NLP From Mark Sanderson, University of Sheffield

8. 2009.05.06 - SLIDE 8IS 240 – Spring 2009 Pasca and Harabagiu show value of sophisticated NLP Good IR is needed: paragraph retrieval based on SMART Large taxonomy of question types and expected answer types is crucial Statistical parser (modeled on Collins 1997) used to parse questions and relevant text for answers, and to build knowledge base Controlled query expansion loops (morphological, lexical synonyms, and semantic relations) are all important Answer ranking by simple ML method from Christopher Manning, Stanford Univ.

9. 2009.05.06 - SLIDE 9IS 240 – Spring 2009 Question Answering Example How hot does the inside of an active volcano get? get(TEMPERATURE, inside(volcano(active))) “lava fragments belched out of the mountain were as hot as 300 degrees Fahrenheit” fragments(lava, TEMPERATURE(degrees(300)), –belched(out, mountain)) –volcano ISA mountain –lava ISPARTOF volcano  lava inside volcano –fragments of lava HAVEPROPERTIESOF lava The needed semantic information is in WordNet definitions, and was successfully translated into a form that can be used for rough ‘proofs’ from Christopher Manning, Stanford Univ.

10. 2009.05.06 - SLIDE 10IS 240 – Spring 2009 Conclusion Complete human-level natural language understanding is still a distant goal But there are now practical and usable partial NLU systems applicable to many problems An important design decision is in finding an appropriate match between (parts of) the application domain and the available methods But, used with care, statistical NLP methods have opened up new possibilities for high performance text understanding systems. from Christopher Manning, Stanford Univ.

11. 2009.05.06 - SLIDE 11IS 240 – Spring 2009 Today Review –NLP for IR –Text Summarization Cross-Language Information Retrieval –Introduction –Cross-Language EVIs Thanks to Doug Oard (University of Maryland) Fredric Gey and Aitao Chen for some of the material in this lecture

12. 2009.05.06 - SLIDE 12IS 240 – Spring 2009 Introduction to CLIR Slides from Doug Oard…

13. 2009.05.06 - SLIDE 13IS 240 – Spring 2009 Cross-Language IR Given a query expressed in one language Find info that may be expressed in another –Electronic texts –Document images –Recorded speech [101] –Sign language Retrieval System English QueryFrench Documents

14. 2009.05.06 - SLIDE 14IS 240 – Spring 2009 Why Do Cross-Language IR? When users can read several languages –Eliminates multiple queries –Query in most fluent language Monolingual users can also benefit –If translations can be provided –If it suffices to know that a document exists –If text captions are used to search for images

15. 2009.05.06 - SLIDE 15IS 240 – Spring 2009 What We Know Dictionaries are very useful –Easily get to 50% of monolingual IR effectiveness –We can get to about 75% using: Part-of-speech tags Pseudo-relevance feedback Phrase indexing Multilingual training corpora are also useful –When the corpus is from the right domain

16. 2009.05.06 - SLIDE 16IS 240 – Spring 2009 Related Issues Multiscript text processing [12] –Character sets, writing system, direction,... Language identification [109] –Markup, detection Language-specific processing [103] –Stemming, morphological roots, compounds, … Document translation [51]

17. 2009.05.06 - SLIDE 17IS 240 – Spring 2009 Term-aligned Sentence-aligned Document-aligned Unaligned Parallel Comparable Knowledge-based Corpus-based Controlled Vocabulary Free Text Cross-Language Text Retrieval Query Translation Document Translation Text Translation Vector Translation Ontology-based Dictionary-based Thesaurus-based

18. 2009.05.06 - SLIDE 18IS 240 – Spring 2009 Free Text Developments 1970, 1973 Salton –Hand coded bilingual dictionaries 1990 Latent Semantic Indexing [53] –French/English using Hansard training corpus 1994 European multilingual IR project [84] –Medium-scale recall/precision evaluation 1996 SIGIR Cross-lingual IR workshop –And many annual conferences and workshops since!

19. 2009.05.06 - SLIDE 19IS 240 – Spring 2009 How Controlled Vocabulary Works Thesaurus design [102] –Design a knowledge structure for domain –Assign a unique “descriptor” to each concept Include “scope notes” and “lead-in vocabulary” Document indexing –Read the document, assign appropriate descriptors Retrieval –Select desired descriptors, use exact match retrieval

20. 2009.05.06 - SLIDE 20IS 240 – Spring 2009 Multilingual Thesauri Adapt the knowledge structure –Cultural differences influence indexing choices Use language-independent descriptors –Matched to a unique term in each language Three construction techniques [46] –Build it from scratch –Translate an existing thesaurus –Merge monolingual thesauri

21. 2009.05.06 - SLIDE 21IS 240 – Spring 2009 Advantages over Free Text High-quality concept-based indexing –Descriptors need not appear in the document Knowledge-guided searching –Good thesauri capture expert domain knowledge Excellent cross-language effectiveness –Up to 100% of monolingual effectiveness Understandable retrieval results Efficient implementation

22. 2009.05.06 - SLIDE 22IS 240 – Spring 2009 Limitations Costly to create –Design knowledge structure, index each document Costly to maintain –Document indexing, vocabulary and concept change Hard to use –Vocabulary choice, knowledge structure navigation Limited scope –Domain must be chosen at design time

23. 2009.05.06 - SLIDE 23IS 240 – Spring 2009 Query vs. Document Translation Query translation –Very efficient for short queries Not as big an advantage for relevance feedback –Hard to resolve ambiguous query terms Document translation –May be needed by the selection interface And supports adaptive filtering well –Slow, but only need to do it once per document Poor scale-up to large numbers of languages

24. 2009.05.06 - SLIDE 24IS 240 – Spring 2009 Document Translation Example Approach –Select a single query language –Translate every document into that language –Perform monolingual retrieval Long documents provide enough context –And many translation errors do not hurt retrieval Much of the generation effort is wasted –And choosing a single translation can hurt

25. 2009.05.06 - SLIDE 25IS 240 – Spring 2009 Query Translation Example Select controlled vocabulary search terms Retrieve documents in desired language Form monolingual query from the documents Perform a monolingual free text search Information Need Thesaurus Controlled Vocabulary Multilingual Text Retrieval System Alta Vista French Query Terms English Abstracts English Web Pages

26. 2009.05.06 - SLIDE 26IS 240 – Spring 2009 Machine Readable Dictionaries Based on printed bilingual dictionaries –Becoming widely available Used to produce bilingual term lists –Cross-language term mappings are accessible Sometimes listed in order of most common usage –Some knowledge structure is also present Hard to extract and represent automatically The challenge is to pick the right translation

27. 2009.05.06 - SLIDE 27IS 240 – Spring 2009 Unconstrained Query Translation Replace each word with every translation –Typically 5-10 translations per word About 50% of monolingual effectiveness –Main problem is ambiguity –Example: Fly (English) 8 word senses (e.g., to fly a flag) 13 Spanish translations (enarbolar, ondear, …) 38 English retranslations (hoist, brandish, lift…)

28. 2009.05.06 - SLIDE 28IS 240 – Spring 2009 Phrase Indexing Improves retrieval effectiveness two ways –Phrases are less ambiguous than single words –Idiomatic phrases translate as a single concept Three ways to identify phrases –Semantic (e.g., appears in a dictionary) –Syntactic (e.g., parse as a noun phrase) –Cooccurrence (words found together often) Semantic phrase results are impressive

29. 2009.05.06 - SLIDE 29IS 240 – Spring 2009 Types of Bilingual Corpora Parallel corpora: translation-equivalent pairs –Document pairs –Sentence pairs –Term pairs Comparable corpora –Content-equivalent document pairs Unaligned corpora –Content from the same domain

30. 2009.05.06 - SLIDE 30IS 240 – Spring 2009 Generating Parallel Corpora Parallel corpora are naturally domain-tuned –Finding one for the right domain may be hard Alternative is to build one –Start with a monolingual corpus –Automatic machine translation for second language Worthwhile when IR technique is faster than MT –If translation errors don’t hurt the IR technique Good results with Latent Semantic Indexing

31. 2009.05.06 - SLIDE 31IS 240 – Spring 2009 Top ranked French Documents French Text Retrieval System Alta Vista French Query Terms English Translations English Web Pages Parallel Corpus Pseudo-Relevance Feedback Enter query terms in French Find top French documents in parallel corpus Construct a query from English translations Perform a monolingual free text search

32. 2009.05.06 - SLIDE 32IS 240 – Spring 2009 Similarity-Based Dictionaries Automatically developed from aligned documents –Reflects language use in a specific domain For each term, find most similar in other language –Retain only the top few (5 or so) Performs as well as dictionary-based techniques –Evaluated on a comparable corpus of news stories [98] Stories were automatically linked based on date and subject

33. 2009.05.06 - SLIDE 33IS 240 – Spring 2009 Latent Semantic Indexing Designed for better monolingual effectiveness –Works well across languages too [27] Cross-language is just a type of term choice variation Produces short dense document vectors –Better than long sparse ones for adaptive filtering Training data needs grow with dimensionality –Not as good for retrieval efficiency Always 300 multiplications, even for short queries

34. 2009.05.06 - SLIDE 34IS 240 – Spring 2009 Cooccurrence-Based Dictionaries Align terms using cooccurrence statistics –How often do a term pair occur in sentence pairs? Weighted by relative position in the sentences –Retain term pairs that occur unusually often Useful for query translation –Excellent results when the domain is the same Also practical for document translation –Term use variations to reinforce good translations

35. 2009.05.06 - SLIDE 35IS 240 – Spring 2009 Language Identification Can be specified using metadata –Included in HTTP and HTML Determined using word-scale features –Which dictionary gets the most hits? Determined using subword features –Letter n-grams in electronic and printed text –Phoneme n-grams in speech

36. 2009.05.06 - SLIDE 36IS 240 – Spring 2009 Research Directions User needs assessment Evaluation Corpus construction Word sense disambiguation System integration Probabilistic models Adaptive filtering

37. 2009.05.06 - SLIDE 37IS 240 – Spring 2009 Evaluation Most critical need is for side by side tests –TREC-did this for French/German/Italian Domain shift metric –Domain shift hurts corpus-based techniques –Need a way to measure severity of the shift Test collections for adaptive filtering –From cross-language recall/precision evaluation

38. 2009.05.06 - SLIDE 38IS 240 – Spring 2009 Corpus Construction Corpus-based techniques have great potential Parallel corpora are rare and expensive –Find it, reverse engineer the links, clean it up Unlinked corpora are of limited value –Context linking research could change that [77] Comparable corpora offer middle ground –Need to develop automatic linking techniques –Also need a metric for degree of comparability

39. 2009.05.06 - SLIDE 39IS 240 – Spring 2009 1 Find and Interpret Information Vital to National Security The Tamil National leader,Mr. V.Pirapaharan delivered a speech on 13 May 1998, the anniversary of the launch of Sri Lanka's biggest and longest assault on the Tamil homelands, describing how the LTTE defended against Sri Lanka's latest military ambitions. Here’s what he said: 62Million people in South India and Sri Lanka can read this Find and retrieve information in unfamiliar languages Translate it into English Extract and correlate its content against other materials TIDES

40. 2009.05.06 - SLIDE 40IS 240 – Spring 2009 3 The Challenges Today is a significant day in the history of our national liberation struggle, it marks the end of a year during which we have resisted and fought against the biggest ever offensive operation launched by the Sri Lankan armed forces code named "Jayasikuru”... Translation Topic Detection Summarization Extraction The objective of the Sinhala chauvinists was to utilize maximum man power and fire power to destroy the military capability of the LTTE and to bring an end to the Tamil freedom movement. Before the launching of the operation "Jayasikuru" the Sri Lankan political and military high command miscalculated the military strength and determination of the LTTE. Liberation Tigers ofTamil Eelam(LTTE) Sri Lanka Velupillai Pirapaharan Rebellion OrgLeaderHQLosses SinhalaKumaratunga3000 LTTEPirapaharanWanni1300 (manual) (experimental) (special-purpose) (key sentences) Tamildocument Tamildocument analysis

41. 2009.05.06 - SLIDE 41IS 240 – Spring 2009 Cross-Language IR on the Web http://www.clis.umd.edu/dlrg/clir/ –Most workshop proceedings –Lots of papers and project descriptions –Links to working systems Including 2 web search engines –Useful linguistic resources –BibTeX for the attached bibliography

42. 2009.05.06 - SLIDE 42IS 240 – Spring 2009 Today Review –NLP for IR –Text Summarization Cross-Language Information Retrieval –Introduction –Cross-Language EVIs Credit for some of the material in this lecture goes to Doug Oard (University of Maryland) and to Fredric Gey and Aitao Chen

43. 2009.05.06 - SLIDE 43IS 240 – Spring 2009 The “Entry Vocabulary Index” Approach to Multilingual Search Ray R. Larson, Fredric Gey, Aitao Chen, Michael Buckland University of California, Berkeley School of Information Management and Systems and UC Data Harvesting Translingual Vocabulary Mappings for Multilingual Digital Libraries Note: This talk was presented at the 2002 JCDL (Joint Conference on Digital Libraries)

44. 2009.05.06 - SLIDE 44IS 240 – Spring 2009 Overview What are Entry Vocabulary Indexes? –EVI Research at Berkeley –Notion of an EVI –How are EVIs Built Berkeley Multilingual EVI –Technology components –Database –Examples of operation Ongoing research

45. 2009.05.06 - SLIDE 45IS 240 – Spring 2009 Entry Vocabulary Index Research Projects at Berkeley DARPA Information Management Program –“Search Support for Unfamiliar Metadata Vocabularies” Institute for Museum and Library Services –“Seamless Searching of Numeric and Textual Resources” DARPA TIDES program –“Translingual Information Management Using Domain Ontologies” NSF/NASA/DARPA: DLI-2 (IDL) –“ Discovery and Use of Textual, Numeric and Spatial Data”

46. 2009.05.06 - SLIDE 46IS 240 – Spring 2009 The IMLS project: To demonstrate improved access to written material and numerical data on the same topic when searching two very different databases: --- books, articles, and their bibliographic records; --- numerical data in socio-economic databases. PHASE I: A library gateway providing search support for searching both text and socio-economic numeric databases. The gateway would accept a query in the library users’ own terms and would suggest what terms in the specialized categorization used in the resource to be searched. PHASE II: Demonstration of a library gateway supporting searches between text and numeric databases. If you found some thing interesting in a socio-economic database, the gateway would help you to find documents on the same topic in a text database – and vice versa.

47. 2009.05.06 - SLIDE 47IS 240 – Spring 2009 TIDES Project Translingual Information Detection, Extraction and Summarization –Building EVIs to map across languages Using same notion with training data in different languages Using Library of Congress Subject Headings from the CDL MELVYL database

48. 2009.05.06 - SLIDE 48IS 240 – Spring 2009 What is an Entry Vocabulary Index? EVIs are a means of mapping from user’s vocabulary to the controlled vocabulary of a collection of documents…

49. 2009.05.06 - SLIDE 49IS 240 – Spring 2009 Start with a collection of documents.

50. 2009.05.06 - SLIDE 50IS 240 – Spring 2009 Classify and index with controlled vocabulary. Index Ideally, use a database already indexed

51. 2009.05.06 - SLIDE 51IS 240 – Spring 2009 Problem: Controlled Vocabularies can be difficult for people to use. “pass mtr veh spark ign eng” Index

52. 2009.05.06 - SLIDE 52IS 240 – Spring 2009 Solution: Entry Level Vocabulary Indexes. Index EVI pass mtr veh spark ign eng” = “Automobile”

53. 2009.05.06 - SLIDE 53IS 240 – Spring 2009 EVI example EVI 1 Index term: “pass mtr veh spark ign eng” User Query “Automobile” EVI 2 Index term: “automobiles” OR “internal combustible engines”

54. 2009.05.06 - SLIDE 54IS 240 – Spring 2009 But why stop there? Index EVI

55. 2009.05.06 - SLIDE 55IS 240 – Spring 2009 “Which EVI do I use?” Index EVI Index EVI Index EVI

56. 2009.05.06 - SLIDE 56IS 240 – Spring 2009 EVI to EVIs Index EVI Index EVI Index EVI EVI 2

57. 2009.05.06 - SLIDE 57IS 240 – Spring 2009 Find Plutonium In Arabic Chinese Greek Japanese Korean Russian Tamil Why not treat language the same way?

58. 2009.05.06 - SLIDE 58IS 240 – Spring 2009 Find Plutonium In Arabic Chinese Greek Japanese Korean Russian Tamil Statistical association Digital library resources

59. 2009.05.06 - SLIDE 59IS 240 – Spring 2009 Background on Online Library Catalogs Library catalogs have been automated at a furious pace worldwide since the late ’70s Library objects (books, maps, pictures) in 400+ languages Bibliographic descriptions contain one or more sentences from a particular language (transliterated) Objects have been classified by subject by librarians –Library of Congress Subject Heading (Islamic Fundamentalism) –Library of Congress Classification (BP60, BP63, KF27) –Dewey Decimal Classification (297.2, 306.6, 320.5) International standard (MARC) for catalog metadata Huge number of remotely searchable catalogs worldwide accessible using the international search/retrieve protocol Z39.50

60. 2009.05.06 - SLIDE 60IS 240 – Spring 2009 What can libraries and their catalogs provide? Millions of sentences in multiple languages Sentences with topical content identified from 150,000 Library of Congress Subject Headings Transfer point (interlingua) between English topics and words in other languages Can be used to create: –Bilingual dictionaries –Query expansion in cross-language information retrieval

61. 2009.05.06 - SLIDE 61IS 240 – Spring 2009 Search: SUBJECT “Islamic Fundamentalism” and LANGUAGE “Arabic” Yield: 119 Arabic language samples on topic “Islamic Fundamentalism”

62. 2009.05.06 - SLIDE 62IS 240 – Spring 2009 Our Training Set and Prototype University of California/CDL MELVYL catalog Private copy, 10 million+ records (5 million non- English) Records in over 100 languages Obtained in MARC database standard format Foreign language titles use Library of Congress transliteration (Romanization) standard Prototype search software maps from/to English and –Arabic, Chinese, French, German –Italian, Japanese, Russian, Spanish

63. 2009.05.06 - SLIDE 63IS 240 – Spring 2009 Technical Details Download a set of training data. Build associations between extracted terms & controlled vocabularies. Part of speech tagging Extract terms (words and noun phrases) from titles and abstracts. Building an Entry Vocabulary Module (EVI) For noun phrases Internet DB indexed with a controlled vocabulary.

64. 2009.05.06 - SLIDE 64IS 240 – Spring 2009 Association Measure C ¬C t a b ¬t c d Where t is the occurrence of a term and C is the occurrence of a classification in the training set

65. 2009.05.06 - SLIDE 65IS 240 – Spring 2009 Association Measure Maximum Likelihood ratio W(C,t) = 2[logL(p 1,a,a+b) + logL(p 2,c,c+d) - logL(p,a,a+b) – logL(p,c,c+d)] where logL(p,n,k) = klog(p) + (n – k)log(1- p) and p 1 = p 2 = p= a a+b c c+d a+c a+b+c+d Vis. Dunning

66. 2009.05.06 - SLIDE 66IS 240 – Spring 2009 Example: Library of Congress Subject Heading “Islamic Fundamentalism” yields most closely associated words in multiple languages

67. 2009.05.06 - SLIDE 67IS 240 – Spring 2009 Non-English words can be mapped to English subject headings

68. 2009.05.06 - SLIDE 68IS 240 – Spring 2009 Examples

69. 2009.05.06 - SLIDE 69IS 240 – Spring 2009 Catalog Languages vs. FBIS Languages (University of California online catalog. 10 million records) Approx. language distribution (Berkeley # sentences, FBIS est. # lines source) BerkeleyFBIS BerkeleyFBIS German840,03249,872 Danish41,51718,688 Spanish614,025388,772 Hebrew41,4683,500 French609,0892,871 Czech35,4323,647 Russian341,05015,415 Urdu30,206 Italian266,424254 Turkish30,015 Portuguese149,38924,930 Bulgarian27,850 Chinese127,636246,549 Norwegian26,47813,596 Japanese110,956 Korean25,97968,607 Arabic96,124(8263)* Rumanian25,874 Dutch90,170 Finnish25,0278,187 Latin88,818 Thai24,693 Polish81,698 Serbo-Croatian24,60136,139 Indonesian59,445 Greek23,926 Swedish53,85416,652 Bengali23,430 Hungarian46,3306,631 Catalan20,392 Hindi42,886 Tamil20,232 *English only, no source text 106 languages with > 500 records

70. 2009.05.06 - SLIDE 70IS 240 – Spring 2009 Future Research Add content from other online library catalogs –RLIN (>30M records, >900K Chinese, >250K Arabic) –COPAC [UK] (9M records, 40k Arabic) Transliteration and back-transliteration for scripted languages Phrase mapping (POS tagging for English, bigram-trigram identification for target languages using mutual information) Further evaluation (TREC, CLEF, NCTIR and local analysis)