Information Retrieval and Web Search

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Information Retrieval and Web Search

• Cross Language Information Retrieval
• Instructor Rada Mihalcea
• Class web page http//lit.csci.unt.edu/classes/C
  SCE5200
• Some of the slides are from a course taught by
  Doug Oard at U. Maryland

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The General Problem

• Find documents written in any language
• Using queries expressed in a single language

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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

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Source Michael Lesk, How Much Information is
there in the World?
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Supply Side Internet Hosts
Guess What will be the most widely used
language on the Web in 2010?
Source Network Wizards Jan 99 Internet Domain
Survey
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Demand Side Number of Speakers
Source http//www.g11n.com/faq.html
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Search Technology
Chinese Feature Assignment
Monolingual Chinese Matching
1 0.72 2 0.48
Language Identification
Chinese Feature Assignment
Chinese Query
English Feature Assignment
Cross- Language Matching
3 0.91 4 0.57 5 0.36
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Language Identification

• Can be specified using metadata
• Included in HTTP and HTML
• Can be determined using word-scale features
• Which dictionary gets the most hits?
• Can be determined using subword features
• Letter n-grams, for example

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Design Decisions

• What to index?
• Free text or controlled vocabulary
• What to translate?
• Queries or documents
• Where to get translation knowledge?

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Query Vector Translation
Chinese Query Features
Query (Vector) Translation
Monolingual English Matching
3 0.91 4 0.57 5 0.36
English Document Features
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Document Vector Translation
Chinese Query Features
English Document Features
Monolingual Chinese Matching
3 0.91 4 0.57 5 0.36
Document (Vector) Translation
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Matching Interlingual Representations
Chinese Query Features
Query Folding In
English Document Features
Interlingual Matching
3 0.91 4 0.57 5 0.36
Document Folding In
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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

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Cross-Language Text Retrieval
Query Translation
Document Translation
Text Translation Vector Translation
Controlled Vocabulary Free Text
Knowledge-based
Corpus-based
Ontology-based Dictionary-based
Term-aligned Sentence-aligned
Document-aligned Unaligned
Thesaurus-based
Parallel Comparable
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Translation Knowledge

• A lexicon
• e.g., extract term list from a bilingual
  dictionary
• Corpora
• Parallel or comparable, linked or unlinked
• Algorithmic
• e.g., transliteration rules, cognate matching
• The user

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Types of Lexicons

• Ontology
• Representation of concepts and relationships
• Thesaurus
• Ontology specialized for retrieval
• Bilingual lexicon
• Ontology specialized for machine translation
• Bilingual dictionary
• Ontology specialized for human translation

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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
• Build it from scratch
• Translate an existing thesaurus
• Merge monolingual thesauri

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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

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Unconstrained Query Translation

• Replace each word with every translation
• Typically 5-10 translations per word
• About 50 of monolingual effectiveness
• Ambiguity is a serious problem
• Example Fly (English)
• 8 word senses (e.g., to fly a
  flag)
• 13 Spanish translations (enarbolar, ondear, )
• 38 English retranslations (hoist, brandish, lift)

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Exploiting Part-of-Speech Tags

• Constrain translations by part of speech
• Noun, verb, adjective,
• Effective taggers are available
• Works well when queries are full sentences
• Short queries provide little basis for tagging
• Constrained matching can hurt monolingual IR
• Nouns in queries often match verbs in documents
• This is why stemming usually improves performance

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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

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Types of Bilingual Corpora

• Parallel corpora translation-equivalent pairs
• Document pairs
• Sentence pairs
• Term pairs
• Comparable corpora
• Content-equivalent document pairs
• E.g. newspaper articles in different languages,
  on the same day (for the same event)
• Unaligned corpora
• Content from the same domain

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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

Top ranked French Documents
French Query Terms
English Web Pages
English Translations
French Text Retrieval System
Parallel Corpus
Alta Vista
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Learning From Document Pairs

• Count how often each term occurs in each pair
• Treat each pair as a single document

English Terms
Spanish Terms
E1 E2 E3 E4 E5 S1 S2
S3 S4
Doc 1
4
2
2
1
Doc 2
8
4
4
2
Doc 3
2
2
1
2
Doc 4
2
1
2
1
Doc 5
4
1
2
1
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Similarity-Based Dictionaries

• Automatically developed from aligned documents
• Terms E1 and E3 are used in similar ways
• Terms E1 S1 (or E3 S4) are even more similar
• 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
• Stories were automatically linked based on date
  and subject

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Generalized Vector Space Model

• Term space of each language is different
• But the document space for a corpus is the same
• Describe new documents based on the corpus
• Vector of cosine similarity to each corpus
  document
• Easily generated from a vector of term weights
• Multiply by the term-document matrix
• Compute cosine similarity in document space
• Excellent results when the domain is the same

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Latent Semantic Indexing

• Designed for better monolingual effectiveness
• Works well across languages too
• 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

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Sentence-Aligned Parallel Corpora

• Easily constructed from aligned documents
• Match pattern of relative sentence lengths
• Not yet used directly for effective retrieval
• But all experiments have included domain shift
• Good first step for term alignment
• Sentences define a natural context

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Cooccurrence-Based Translation

• 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 usage reinforces good translations

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Exploiting Unaligned Corpora

• Documents about the same set of subjects
• No known relationship between document pairs
• Easily available in many applications
• Two approaches
• Use a dictionary for rough translation
• But refine it using the unaligned bilingual
  corpus
• Use a dictionary to find alignments in the corpus
• Then extract translation knowledge from the
  alignments

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CLIR Evaluation Resources

• Electronic texts
• Text Retrieval Conference (E, F, G, I)
• Topic Detection and Tracking (E, C)
• Document images
• No evaluation programs yet
• Recorded speech
• Topic Detection and Tracking (E, C)
• Sign language
• No evaluation programs yet
• CLEF Evaluation
• http//clef.iei.pi.cnr.it2002/

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