Diapositive 1

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A multi-word term extraction program for Arabic
language
LREC 28-30 May 2008 Marrakech
Siham Boulaknadel, Béatrice Daille and Driss
Aboutajdine LINA University of Nantes GSCM_LRIT Un
iversity of Rabat
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Outline

• Multi-word term
• Motivation
• Approach
• Comparing statistical methods
• Conclusion and future work

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Terms

• Refer to a defined concept ... (ISO 704).
• Represent a limited number of part of speech
  nouns, verbs, adjectives, and adverbs.
• Given subject domain

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Multi-word terms

• ????? ?????? ?????????? ????? ????? ??????
  ???????? ???? ??? ?? ????? ??????? ???????
  wikipidea
• Nitrogen oxides consists of all combustion
  processes taking place at high temperature
• MWTs extracted
• ?????? ??????????
• ????? ??????? ???????
• ?????? ????????

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Motivation

• Frequent MWTs
• Application
• for building index from unstructured documents
• for enhancing document retrieval system

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MWT extraction system Concept extraction
Corpus
Identification of Term Candidates - linguistic
filtering (shallow parsing)
Filtering of Term Candidates - statistical
significance (LLR, FLR, MI3,T-score)
Candidate list
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MWT evaluation

• unithood measure the strengh of association of
  the constituents of MWU
• United nations environment domain
• Unithood
• termhood measure relatedness to existing domain
  specific concepts.
• Soil degradation environment domain
• Termhood Unithood

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MWT patterns
Pattern Sub-pattern Arabic MWT English translation
N ADJ ?????? ????????? Chemical pollution
N1 N2 ???? ????? Water pollution
N1 PREP N2 N1 ? N2 ?????? ??????? Pollution with lead
N1 PREP N2 N1 ? N2 ?????? ??????? Exposure to diseases
N1 PREP N2 N1 ?? N2 ?????? ?? ???????? Waste disposal
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MWT variations

• Multiple forms for the same concept
• Variations types
• Inflexional morphology
• Number
• N1 N2 / N1 N2 suffix(??, ??)
• ???? ?????? ocean pollution 
• ???? ???????? oceans pollution 
• Definite form
• N Adj / Prefix(??) N prefix(??) Adj
• ???? ??????? chemical polution
• ?????? ????????? the chemical pollution
• Derivational morphosyntactic phenomena
• N1 ADJ /N1 PREP N2
• ??? ???? gt ??? ?? ????? oil well
• Syntactically (modification postposition)
• N1 N2 / N1 N2 ADJ
• ???? ??????? degree of temperature
• ???? ??????? ??????? high degree of temperature
  

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Comparing statistical filtering

• Mutual Information (MI3) (Daille, 1994) as
  baseline
• Loglikelihood (Dunning, 1994)
• t-Score (Church, 1991)
• FLR (Nakagawa and Mori, 2003)

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Experiment Data

• Arabic specific domain corpus on environment
• Compiled from the web Al-Khat Alakhdar Akhbar
  Albiae from 2004-2006
• 475,148 words
• Motivation
• The no-availability of Arabic specific domain
  corpora

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Gold standard

• Reference list
• Arabic environment terminology Agrovoc
• Total 65,000 unique known terms ( single and
  MWT)
• Dynamic search
• Eurodicautom

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Preprocessing

• Moving diacritics
• Buckwalters transliteration
• Diabs parsing (Diab, 2004)
• Input
• wlm yHtsb AlHkm Almjry sAndwr bwl rklp jzA' SHyHp
  Avr Erqlp dAxl AlmnTqp mn qbl AlysAndrw.
• Output
• w/CC lm/RP yHtsb/VBP Al/DT Hkm/NN Al/DT mjry/JJ
  sAndwr/NNP bwl/NNP rklp/NN jzA'/NN SHyHp/JJ
  Avr/IN Erqlp/NN dAxl/IN Al/DT mnTqp/NN mn/IN
  qbl/NN Al/DT ysAndrw/NNP ./PUNC

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Evaluation and results
Precision
LLR 85
FLR 60
IM3 26
T-score 57

• For each association score
• Examine the first candidates term
• Compute precision (termhood) for 100 candidates
  term
• Precision (termhood) is quotient of attested MWT
  and all extracted sequences.
• the loglikelihood is the best measure

LLR list Eng. transl
???? ??????? ?????? ??????? ?????? ??????? ???? ??????? ?????? ??????? ?????? ?????? Acidity degree Dioxide Waste water Sewage Climate change Nervous system
Agrovoc Eurodicautom Acidity degree Dioxide Waste water Sewage Climate change Nervous system
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Summary future work

• Develop MWT extraction for Arabic
• Define MWT patterns and variations
• Obtain best results than european languages
• Improvement of system
• Adding new variation
• Improve lemmatisation

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Introduction

• MWTs are sufficiently informative to help human
  readers get a feel of the essential topics
• Use in many text related applications
• Text clustering
• Document similarity
• Document summarization

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Related Work

• Linguistic Approach
• Based on linguistic pre-processing and
  annotations (result of taggers, shallow parsers)
• Detect recurrent syntactic term formation
  patterns
• Noun Noun
• (Adj Noun) Noun

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Systems based on linguistics

• Ananiadou, S. (1994) recognises single-word terms
  from domain of Immunology based on morphological
  analysis of term formation patterns (internal
  term make up)
• Justeson Katz (1995, TERMS) extract complex
  terms based on two characteristics (which
  distinguishes them from non terms)
• the syntactic patterns are restricted
• terms appear with the same form throughout the
  text, omissions of modifiers are avoided

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Systems based on linguistics

• The text is tagged a filter is applied to
  extract terms
• ((AN) ((AN) (N P)?) (AN))
  N
• AN / NA / AAN / ANN / NAN / NNN / NPN
• Filtering based on simple POS pattern
• A pattern must occur above a certain threshold to
  be considered a valid term pattern.
• Recall 71 Precision 71 -- 96
• LEXTER (Bourigault, 1994)
• Extracts French compound terms based on surface
  syntactic analysis and text heuristics
• Terms are identified according to certain
  syntactic patterns

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• Uses a boundary method to identify the extent of
  terms
• categories or sequences of categories that are
  not found in term patterns form the boundaries
  e.g. verbs, any preposition (except de and à)
  followed by a determiner. Non productive
  sequences become boundaries.
• Precision 95 although tests have shown that
  lots of noise is generated

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Approaches using statistical information

• Main measures used
• Frequency of occurrence
• Mutual Information
• C/NC value
• Experiments also with loglike coefficient
  Dunning, 1993

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Frequency of occurrence

• Simplest and most popular method for Domain
  independent, requires no external resources
• Some filtering is used in form of syntactic
  patterns
• Systems using frequency of occurrence
• Dagan Church (TERMIGHT, 1994)
• Enguehard Pantera (1994)
• Lauriston (TERMINO, 1996)

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Mutual Information

• The amount of information provided by the
  occurrence of the event represented by yi about
  the occurrence of the event represented by xk is
  defined as
• I(xk,yi) ? log P(xk,yi) / P(xk)
  P(,yi)
  Fano (196127-28)
• This measure is about how much a word tells us
  about the other.
• Problems for MI come from data sparseness
• Damerau (1993) and Daille (1994) used MI for the
  extraction of candidate terms (only for two-word
  candidate terms)

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C/NC value (Frantzi Ananiadou)

• C/value
• total frequency of occurrence of string in
  corpus
• frequency of string as part of longer candidate
  terms
• number of these longer candidate terms
• length of string (in number of words)

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NC value

• NC-value(a) 0.8 C-value(a) 0.2 CF(a)
• a is the candidate term,
• C-value(a) is the C-value for the candidate term
  a,
• CF(a) is the context factor for the candidate
  term a
• we obtain the CF by summing up the weights for
  its term context words, multiplied by their
  frequency appearing with this candidate term.

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Hybrid approaches

• Combination of linguistic information (filters),
  shallow parsing results and statistical measures
• Daille, B., Frantzi Ananiadou

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• Thank You