A Brief Survey on Cross-language Information Retrieval (CLIR) - Text Retrieval Perspective
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Transcript of A Brief Survey on Cross-language Information Retrieval (CLIR) - Text Retrieval Perspective
A Brief Survey on
Cross-language Information Retrieval (CLIR)- Text Retrieval Perspective
by
Ying Alvarado (24401693)
CSE 8337Lecturer : Dr. Margaret Dunham
April 26, 2007
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Outline Introduction
Concept Why important
Approach CLIR problems Resource Approaches Example Techniques
A CLIR application system CLIR effectiveness CLIR future tasks CLIR communities References
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Cross Language IR Definition: Users enter their query in one
language and the system retrieves relevant documents in other languages.
For example, a user may pose their query in English but retrieve relevant documents written in French.
Example CLIR applications Cross-Language retrieval from texts Cross-Language retrieval from audio and images
[1] Wikipedia, http://en.wikipedia.org/wiki/Cross-language_information_retrieval[2] Paul Clough, Bridging the language gap: making digital collections available to a multilingual society, presentation, 2005
In this presentation, we focus on text IR only!
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•Monolingual IR: Documents and user requests in the same language
Documents(L1 )
IR systemRequest (L1)
Results(L1)
Monolingual vs. Bilingual vs. Multilingual
[2] Paul Clough, Bridging the language gap: making digital collections available to a multilingual society, presentation, 2005
•Cross-language IR: Documents and user requests are in different languages (bilingual IR)
Documents(L2 )
Cross-language IR (CLIR) systemRequest (L1) Results(L2)
Source languageTarget language
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Documents(L3)
Multilingual IR (MLIR) systemRequest (L?) Results (L2, L3 or L4)
Documents(L2 )
Documents(L4 )
e.g. the Web
•Multilingual IR: Documents in collection in different languages, search requests in any language
Monolingual vs. Bilingual vs. Multilingual (con.)
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Why CLIR?
[3] Internet World Stats, http://www.internetworldstats.com/stats7.htm
TOP TEN LANGUAGESIN THE INTERNET
% of allInternet Users
Internet Usersby Language
InternetPenetration
by Language
Internet Growthfor Language( 2000 - 2007 )
2007 EstimateWorld Populationfor the Language
English 29.5 % 328,666,386 28.7 % 139.6 % 1,143,218,916
Chinese 14.3 % 159,001,513 11.8 % 392.2 % 1,351,737,925
Spanish 8.0 % 88,920,232 20.2 % 260.3 % 439,284,783
Japanese 7.7 % 86,300,000 67.1 % 83.3 % 128,646,345
German 5.3 % 58,711,687 61.1 % 113.2 % 96,025,053
French 5.0 % 55,521,294 14.3 % 355.2 % 387,820,873
Portuguese 3.6 % 40,216,760 17.2 % 430.8 % 234,099,347
Korean 3.1 % 34,120,000 45.6 % 79.2 % 74,811,368
Italian 2.8 % 30,763,940 51.7 % 133.1 % 59,546,696
Arabic 2.6 % 28,540,700 8.4 % 931.8 % 340,548,157
TOP TEN LANGUAGES 81.7 % 910,762,512 21.4 % 181.4 % 4,255,739,462
Rest of World Languages 18.3 % 203,511,914 8.8 % 444.5 % 2,318,926,955
WORLD TOTAL 100.0 % 1,114,274,426 16.9 % 208.7 % 6,574,666,417
Top Ten Languages Used in the Web( Number of Internet Users by Language )
Mar. 10, 2007
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Why CLIR? (con.)
[4] D.W. Oard, A Survey of Multilingual Text Retrieval. Computer Science Technical Report Series; Vol. CS-TR-3615. 1996
A collection may contains documents in many different languages, e.g. the Web. It would be impractical to form a query in each language.
The documents may be expressed in more than one languages. For example,
Technical documents in which English jargon appears intermixed with narrative text in another language.
Academic works which cite the titles of documents in different languages.
The user is not sufficiently fluent to express a query in a language, but is able to make use of the documents that are identified.
The user is monolingual and wants to query in their native language. Because he
can judge relevance even if results not translated have access to document translation
[2] Paul Clough, Bridging the language gap: making digital collections available to a multilingual society, presentation, 2005
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Handling non-ASCII character setsUntranslatable search keys (OOV): e.g. compound words, proper names, special termsMulti-word concepts, e.g. phrases and idiomsAmbiguity, e.g. Homonymy and polysemyWord Inflections, e.g. plurals and gender
CLIR problems
[5] Ari Pirkola, et al. Dictionary-Based Cross-Language Information Retrieval_ Problems, Methods, and Research Findings. Information Retrieval, Vol. 4. 2001
[2] Paul Clough, Bridging the language gap: making digital collections available to a multilingual society, presentation, 2005
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Ontology Representation of concepts and relationships
Thesaurus it more commonly means a listing of words with similar,
related, or opposite meanings It does not include the definition of words
Bilingual dictionary a list of words together with additional word-specific
information. Bilingual controlled vocabulary
carefully selected list of words and phrases, which are used to tag units of information (document or work) so that they may be more easily retrieved by a search
Corpora The document collection itself
Resources for Translation
[6] Jimmy Lin, Cross-Language and Multimedia Information Retrieval. Slides for LBSC 796/INFM 718R. 2006
[4] D.W. Oard, A Survey of Multilingual Text Retrieval. Computer Science Technical Report Series; Vol. CS-TR-3615. 1996
[1] Wikipedia. Related pages.[7] Metamodel.com. What are the differences between a vocabulary, a taxonomy, a thesaurus, an ontology, and a meta-model? http://www.metamodel.com/article.php?story=20030115211223271. 2004
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An example of controlled vocabulary
[14] Boxes and Arrows, http://www.boxesandarrows.com/view/what_is_a_controlled_vocabulary
The hierarchical relationships
Women’s Pants: BT Pants NT Casual Pants NT Dress Pants NT Sports Pants
The equivalence relationship
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What to translate? Document translation
Text translation E.g., translate entire document collection into English → search collection in English
Vector translation Query translation
E.g., translate English query into Chinese query → search Chinese document collection
[6] Jimmy Lin, Cross-Language and Multimedia Information Retrieval. Slides for LBSC 796/INFM 718R. 2006
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Tradeoffs Document Translation
Documents can be translate and stored offline Dependent on high quality automatic machine translation
(MT) system Does not easily deal with changing document sets
Query Translation Often easier Disambiguation of query terms may be difficult with short
queries
[6] Jimmy Lin, Cross-Language and Multimedia Information Retrieval. Slides for LBSC 796/INFM 718R. 2006
[4] D.W. Oard, A Survey of Multilingual Text Retrieval. Computer Science Technical Report Series; Vol. CS-TR-3615. 1996
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Approaches to query translation Knowledge-based: Several aspects of domain knowledge is manually encoded in
to a lexicon. Ontology-based (concept driven) Thesaurus-based Dictionary-based
Expensive to construct lexicons; Lag behind the common use of terminology.
Corpus-based: directly exploit statistical information about term usage in a corpora; automatically construct lexicon.
Parallel corpora: document pairs, sentence pairs, term pairs Comparable corpora: document pairs, similar content Unaligned corpora: documents from the same domain, not translations of one another,
not linked in any other way
[8] Miguel E. Ruiz, CLIR. Slides for school seminars. 2001[9] Rada Mihalcea, Information Retrieval and Web Search. Class slides. 2007
[4] D.W. Oard, A Survey of Multilingual Text Retrieval. Computer Science Technical Report Series; Vol. CS-TR-3615. 1996
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Applying monolingual IR techniques Query expansion Relevance feedback Stemming Latent semantic analysis Parsing Part of speech tagging……
[4] D.W. Oard, A Survey of Multilingual Text Retrieval. Computer Science Technical Report Series; Vol. CS-TR-3615. 1996
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Multilingual Thesauri Three construction techniques
Build it from scratch Translate an existing thesaurus Merge monolingual thesauri
For example EuroWordNet 7 languages Built from existing lexical resources Has the same structure as Princeton
WordNet
[8] Miguel E. Ruiz, CLIR. Slides for school seminars. 2001[9] Rada Mihalcea, Information Retrieval and Web Search. Class slides. 2007
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Pseudo-Relevance Feedback Also call Blind feedback Assume that the top n documents in the result set
actually are relevant. Enter query terms in French Find top French documents in parallel corpus Construct a query from English translations Perform a monolingual free text search
Top ranked FrenchDocuments French
Text Retrieval System
AltaVista
FrenchQueryTerms
EnglishTranslations
English Web PagesParallel
Corpus
[9] Rada Mihalcea, Information Retrieval and Web Search. Class slides. 2007
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Different level alignment in parallel corpora Document alignment
Already exists Collected from existing corpora
Examine document external features Examine document internal features
Sentence alignment Easily constructed from aligned documents Match pattern of relative sentence lengths Good first step for term alignment
Term alignment Using co-occurrence-based translation
[9] Rada Mihalcea, Information Retrieval and Web Search. Class slides. 2007
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Example of term alignment
CSE8337 是一门关于信息存储和检索的课程。
CSE8337 is a class about information storage and retrieval.
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Co-occurrence-based translation Align terms using co-occurrence statistics
assumed that the correct translations of query terms tend to co-occur in target language documents
How often do a term pair occur in sentence pairs?
Weighted by relative position in the sentences
Retain term pairs that occur unusually often
[9] Rada Mihalcea, Information Retrieval and Web Search. Class slides. 2007
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Exploiting Unaligned Corpora
Example approach: category-based translation Extract a large number of terms from unaligned
coprora of the first and second languages Assign a category to each extracted term by
accessing monolingual thesauri of the first and second languages
Estimate category-to-category translation probabilities Estimate term-to-term translation probabilities using
said category-to-category translation probabilities
[15] David Hull, Terminology translation for unaligned comparable corpora using category based translation probabilities. United States Patent 6885985. Filing date: Dec 18, 2000. Issue date: Apr 26, 2005
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In Summary
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
[8] Miguel E. Ruiz, CLIR. Slides for school seminars. 2001
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An experimental system
[10] Jiang Chen, et al. Automatic construction of parallel English-Chinese corpus for cross-language information retrieval. Proceedings of the sixth conference on Applied natural language processin. 2000
Automatic construction of parallel English-Chinese corpus for CLIR A parallel text mining system- PTMiner Finds parallel text from web Parallel Text Mining Algorithm
1. Search for candidate sites - Using existing Web search engines, search for the candidate sites that may contain parallel pages; (by using text anchor)
2. File name fetching - For each candidate site, fetch the URLs of Web pages that are indexed by the search engines;
3. Host crawling - Starting from the URLs collected in the previous step, search through each candidate site separately for more URLs;
4. Pair scan - From the obtained URLs of each site, scan for possible parallel pairs; (by analyzing document external features)
5. Download and verifying - Download the parallel pages, determine file size, language and character set, text length, HTML structure, and filter out non-parallel pairs.
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The workflow of the mining process
Sample anchor texts: “english version” [“in english”, ……] Sample document external features: “file-ch.html” vs. “file-en.html” “…/chinese/…/file.html” vs. “…/english/…file.html” Sample document internal features: Character set, HTML structure
[10] Jiang Chen, et al. Automatic construction of parallel English-Chinese corpus for cross-language information retrieval. Proceedings of the sixth conference on Applied natural language processin. 2000
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An alignment example
[10] Jiang Chen, et al. Automatic construction of parallel English-Chinese corpus for cross-language information retrieval. Proceedings of the sixth conference on Applied natural language processin. 2000
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Part of the lexicons t: ture f: false
[10] Jiang Chen, et al. Automatic construction of parallel English-Chinese corpus for cross-language information retrieval. Proceedings of the sixth conference on Applied natural language processin. 2000
•Encoding scheme transformation (for Chinese)•Sentence level segmentation•Chinese word segmentation•English expression extraction•SILC: language and encoding identification system
Other techniques and tools used:
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Results 14820 pairs of texts (lexicon) C-E has a precision of 77% E-C has a precision of 81.5% CLIR results
Test corpus: TREC5 and TREC6 Chinese track
[10] Jiang Chen, et al. Automatic construction of parallel English-Chinese corpus for cross-language information retrieval. Proceedings of the sixth conference on Applied natural language processin. 2000
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Does CLIR work? Best systems at TREC-6 (1997):
English-French: 49% of highest French monolingual English-German: 64% of highest German monolingual
Best systems at CLEF (2002): English-French: 83% of highest French monolingual English-German: 86% of highest German monolingual
Best systems at CLEF (2006): English-French: 93.82% of best French monolingual English-Portuguese: 90.91% of best Portuguese monolingual
[2]Paul Clough, Bridging the language gap: making digital collections available to a multilingual society, presentation, 2005[16] Giorgio M. Di Nunzio, CLEF 2006: Ad Hoc Track Overview. 2006
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Future tasks
[11] D.W. Oard, When You Come to a Fork in the Road, Take It: Multiple Futures for CLIR Research. SIGIR 2002 CLIR[12] Fredric Gey, et al, CROSS LANGUAGE INFORMATION RETRIEVAL: A RESEARCH ROADMAP. SIGIR 2002 CLIR
Extend study scope: Web pages, medical literature, USENET newsgroup
articles, records of legislative and legal proceedings… Lower cost, improve efficiency
Pay more attention on indexing-time optimizations to improve query-time efficiency
Consider user’s perspective Improve the utility of ranked lists
Define suitable criteria for the construction of a valid multilingual Web corpus
Get resources for resource-poor languages
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CLIR Communities TREC Cross Language Track currently focuses on the
Arabic language,
Cross-Language Evaluation Forum (CLEF) – a spinoff from TREC - covering many European languages,
NTCIR Asian Language Evaluation (covering Chinese, Japanese and Korean).
[12] Fredric Gey, et al, CROSS LANGUAGE INFORMATION RETRIEVAL: A RESEARCH ROADMAP. SIGIR 2002 CLIR
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In CLEF 2006, eight tracks were offered to evaluate the
performance of systems:
multilingual document retrieval on news collections (Ad-hoc)
cross-language structured scientific data (Domain-specific)
interactive cross-language retrieval multiple language question answering cross-language retrieval on image collections cross-language speech retrieval multilingual web retrieval cross-language geographic retrieval.
CLEF
[13] Carol Peters, Cross-Language Evaluation Forum - CLEF 2006. D-Lib Magazine October 2006
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References[1] Wikipedia, http://en.wikipedia.org/wiki/Cross-language_information_retrieval[2] Paul Clough, Bridging the language gap: making digital collections available to a multilingual society, presentation, 2005[3] Internet World Stats, http://www.internetworldstats.com/stats7.htm
[4] D.W. Oard, A Survey of Multilingual Text Retrieval. Computer Science Technical Report Series; Vol. CS-TR-3615. 1996
[6] Jimmy Lin, Cross-Language and Multimedia Information Retrieval. Slides for LBSC 796/INFM 718R. 2006
[8] Miguel E. Ruiz, CLIR. Slides for school seminars. 2001
[5] Ari Pirkola, et al. Dictionary_Based Cross-Language Information Retrieval_ Problems, Methods, and Research Findings. Information Retrieval, Vol. 4. 2001
[7] Metamodel.com. What are the differences between a vocabulary, a taxonomy, a thesaurus, an ontology, and a meta-model? http://www.metamodel.com/article.php?story=20030115211223271. 2004
[9] Rada Mihalcea, Information Retrieval and Web Search. Class slides. 2007
[10] Jiang Chen, et al. Automatic construction of parallel English-Chinese corpus for cross-language information retrieval. Proceedings of the sixth conference on Applied natural language processin. 2000[11] D.W. Oard, When You Come to a Fork in the Road, Take It: Multiple Futures for CLIR Research. SIGIR 2002 CLIR[12] Fredric Gey, et al, CROSS LANGUAGE INFORMATION RETRIEVAL: A RESEARCH ROADMAP. SIGIR 2002 CLIR[13] Carol Peters, Cross-Language Evaluation Forum - CLEF 2006. D-Lib Magazine October 2006
[14] Boxes and Arrows, http://www.boxesandarrows.com/view/what_is_a_controlled_vocabulary
[15] David Hull, Terminology translation for unaligned comparable corpora using category based translation probabilities. United States Patent 6885985. Filing date: Dec 18, 2000. Issue date: Apr 26, 2005[16] Giorgio M. Di Nunzio, CLEF 2006: Ad Hoc Track Overview. 2006
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Thank you!