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CONCEPTUAL KNOWLEDGE: EVIDENCE FROM CORPORA, THE MIND, AND THE BRAIN

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Title: CONCEPTUAL KNOWLEDGE: EVIDENCE FROM CORPORA, THE MIND, AND THE BRAIN


1
CONCEPTUAL KNOWLEDGE EVIDENCE FROM CORPORA, THE
MIND, AND THE BRAIN
  • Massimo PoesioUni Trento, Center for Mind /
    Brain SciencesUni Essex, Language
    Computation(joint work with A. Almuhareb, E.
    Barbu, M. Baroni, B. Murphy)

2
MOTIVATIONS
  • Research on conceptual knowledge is carried out
    in Computational Linguistics, Neural Science, and
    Psychology
  • But there is limited interchange between CL and
    the other disciplines studying concepts
  • Except indirectly through the use of WordNet
  • This work use data from Psychology and Neural
    Science to evaluate (vector-space) models
    produced in CL

3
OUTLINE
  • Vector space representations
  • A semantic vector space model
  • How to evaluate such models
  • Attribute extraction and Feature norms
  • Category distinctions and Brain data

4
CONCEPTUAL SEMANTICS IN VECTOR SPACE
5
LEXICAL ACQUISITION IN CORPUS / COMP LING
  • Vectorial representations of lexical meaning
    derived from IR
  • WORD-BASED vector models
  • vector dimensions are words
  • Schuetze 91, 98 HAL, LSA, Turney, Rapp
  • GRAMMATICAL RELATION models
  • vector dimensions are pairs ltRel,Wgt
  • Grefenstette 93, Lin 98, CurranMoens, Pantel,
    Widdows, Pado Lapata, ..

6
FEATURES IN VECTOR SPACE MODELS
GRAMMATICAL RELATIONS
WORDS
7
STRENGHTS OF THIS APPROACH CATEGORIZATION
8
LIMITATIONS OF THIS WORK
  • Very simplistic view of concepts
  • In fact, typically extract lexical
    representations for WORDS (non-disambiguated)
  • Limited evaluation
  • Typical evaluation judges opinions about
    correctness of distances / comparing with WordNet
  • Most work not connected with work on concepts in
    Psychology / Neural Science

9
OUR WORK
  • Acquire richer, more semantic-oriented concept
    descriptions by exploiting relation extraction
    techniques
  • Develop task-based methods for evaluating the
    results
  • Integrate results from corpora with results from
    psychology neural science

10
THIS TALK
  • Acquire richer, more semantic-oriented concept
    descriptions by exploiting relation extraction
    techniques
  • Develop task-based methods for evaluating the
    results
  • Integrate results from corpora with results from
    psychology neural science

11
OUTLINE
  • Vector space representations
  • A semantic vector space model
  • How to evaluate such models
  • Attribute extraction and Feature norms
  • Category distinctions and Brain data

12
OUTLINE
13
MORE ADVANCED THEORIES OF CONCEPTS
  • In Linguistics
  • Pustejovsky
  • In AI
  • Description Logics
  • Formal ontologies
  • In Psychology
  • Theory Theory (Murphy, 2002)
  • FUSS (Vigliocco Vinson et al)

14
SEMANTIC CONCEPT DESCRIPTIONSPUSTEJOVSKY (1991,
1995)
  • Lexical entries have a QUALIA STRUCTURE
    consisting of four roles
  • FORMAL role what type of object it is (shape,
    color, .)
  • CONSTITUTIVE role what it consists of (parts,
    stuff, etc.)
  • E.g., for books, chapters, index, paper .
  • TELIC role what is the purpose of the object
    (e.g., for books, READING)
  • AGENTIVE role how the object was created (e.g.,
    for books, WRITING)

15
BEYOND BUNDLES OF ATTRIBUTES DESCRIPTION
LOGICS, THEORY THEORY
  • We know much more about concepts than the fact
    that they have certain attributes
  • We know that cars have 4 wheels whereas bicycles
    have 2
  • We dont just know that people have heads, bodies
    and legs, but that heads are attached in certain
    positions whereas legs are attached in other ones
  • Facts of this type can be expressed even in the
    simplest concept description languages, those of
    description logics

16
BEYOND SIMPLE RELATIONS DESCRIPTION LOGICS
Bear ? (and Animal (? 4 Paw)
) Strawberry ? (and Fruit
(fills Color red)
) Female ? (and Human
(? Male))
17
WORD SENSE DISCRIMINATION
  • The senses of palm in WordNet
  • the inner surface of the hand from the wrist to
    the base of the fingers
  • a linear unit based on the length or width of the
    human hand
  • any plant of the family Palmae having an
    unbranched trunk crowned by large pinnate or
    palmate leaves
  • an award for winning a championship or
    commemorating some other event

18
CONCEPT ACQUISITION MEETS RELATION EXTRACTION
  • We developed methods to identify SEMANTIC
    properties of concepts (Deep lexical relations)
  • ATTRIBUTES and their VALUES
  • Almuhareb Poesio 2004, 2005
  • Extracting QUALIA
  • Poesio Almuhareb 2005
  • Letting relations emerge from the data STRUDEL
  • Baroni et al, Cognitive Science to appear
  • Extracting Wu Barsalou style relations
  • Poesio Barbu Giuliano Romano, 2008

We showed that for a variety of tasks such
conceptual descriptions are better than
word-based or grammatical function-based
descriptions
19
ALMUHAREB POESIO 2005 USING A PARSER
LOOKING ONLY FOR (POTENTIAL) ATTRIBUTES AND THEIR
VALUES BETTER THAN USING ALL GRS
EVEN IF ATTRIBUTES OBTAINED USING TEXT PATTERNS
(THE X OF THE Y )
20
ATTRIBUTES AND VALUES VS. ALL CORPUS FEATURES
Description Vector size Vector size Vector size Vector size Vector size
Description 500 1522 3044 4753 4969
Values only 64.86 94.59 - - 94.59
Attributes only 97.30 97.30 97.30 -
Attributes (1522) Values (1522) - - 100 - -
21
SUPERVISED EXTRACTION OF CONCEPT DESCRIPTIONS
  • Using a theory of attributes merging ideas from
    Pustejovsky and Guarino (Poesio and Almuhareb,
    2005)
  • Using Wu and Barsalous theory of attributes
    (Poesio Barbu Romano Giuliano, 2008)

22
SUPERVISED EXTRACTION OF CONCEPT DESCRIPTIONS
  • Using a theory of attributes merging ideas from
    Pustejovsky and Guarino (Poesio and Almuhareb,
    2005)
  • Using Wu and Barsalous theory of attributes
    (Poesio Barbu Romano Giuliano, 2008)

23
THE CLASSIFICATION SCHEME FOR ATTRIBUTES OF
POESIO ALMUHAREB 2005
  • PART
  • (cfr. Guarinos non-relational attributes,
    Pustejovskys constitutive roles)
  • RELATED OBJECT
  • Non-relational attributes other than parts,
    relational roles
  • QUALITY
  • Guarinos qualities, Pustejovskys formal roles
  • ACTIVITY
  • Pustejosvkys telic and agentive roles
  • RELATED AGENT
  • NOT AN ATTRIBUTE ( everything else)

24
A SUPERVISED FEATURE CLASSIFIER
  • We developed a supervised feature classifier that
    relies on 4 types of information
  • Morphological info (Dixon, 1991)
  • Question patterns
  • Features of features
  • Feature use
  • Some nouns used more commonly as features than as
    concepts i.e., the F of the C is more frequent
    than the of the F is
  • (These last four methods all rely on info
    extracted from the Web)

25
THE EXPERIMENT
  • We created a BALANCED DATASET
  • 400 concepts
  • representing all 21 WordNet classes, including
    both ABSTRACT and CONCRETE concepts
  • balanced as to ambiguity and frequency
  • We collected from the Web 20,000 candidate
    features of these concepts using patterns
  • We hand-classified 1,155 candidate features
  • We used these data to train
  • A binary classifier (feature / non feature)
  • A 5-way classifier

26
OUTLINE
  • Vector space representations
  • An example of Semantic-based vector space model
  • Evaluating such models
  • Attribute extraction and Feature norms
  • Category distinctions and Brain data

27
EVALUATION
  • Qualitative
  • Visual inspection
  • Ask subjects to assess correctness of the
    classification of the attributes
  • Quantitative
  • Use conceptual descriptions for CLUSTERING
    (CATEGORIZATION)

28
QUANTITATIVE EVALUATION PROBLEMS
  • Attribute extraction
  • WordNet only contains ISA and PART attributes
  • Attribute extraction can only be evaluated by
    hand
  • Categorical distinctions
  • The WordNet category structure is highly
    subjective

29
VISUAL EVALUATION TOP 400 FEATURES OF DEER
ACCORDING TO OUR CLASSIFIER
Class Attributes
Parts Related Objects antlers, leg, carcass, head, eyes, skin, body, blood, track, neck, horns, flesh, meat, legs, hide, loin, chest, throat, tongue, heart, horn, coat, trail, tail, bones, ears, scent, home, coverts, nose, feet, shoulder, stomach, foot, sight, rack, skull, hair, intestines, necks, line, brain, belly, tendons, step, heads, mind, entrails, skins, hooves, cells, bell, cavity, picture, testicles, photo, forehead, genitals, knees, innards, rump, butt, fur, face, shank, brains, image, ear, statue, path, corpse, jaw, bladder, muzzle, calf, hoofs, abdomen, hill, quarters, shit, senses, paths, wound, stream, feces, varieties, protector, hoof, pics, nostrils, portrait, liver, flanks, pen, forest, vitals, side, hips, garden, food, muscle, muscles, guts, droppings, bodies, veil, footprints, wounds, hearts, homes, teeth, underside, breast, turn, haunches, forelegs, brow, hip, figure, torso, village, spring, chin, tails, organs, enclosure, lips, hindquarters, valley, cave, flank, figures, tissues, spot, insides, dick, backs, skeleton, bark
30
VISUAL EVALUATION QUALITIES
Class Attributes
Quality death, age, beauty, sex, form, sense, reaction, cry, terror, curiosity, health, fleetness, appetite, survival, condition, thirst, life, ways, swiftness, plight, whereabouts, fate, grace, gender, sickness, need, perspective, slaughter, capture, modesty, ecology, preservation, detriment, agility, heartbeat, greed, gentleness, behaviour, behavior, aggressiveness, screams, favor, predicament, genetics, honours, elegance, propensity, reactions, harvest, rescue, curse, mercy, gaze, sustainability, intelligence, lives, thoughts
31
QUANTITATIVE EVALUATION
  • ATTRIBUTES
  • PROBLEM cant compare against WordNet
  • Precision / recall against hand-annotated
    datasets
  • Human judges (ourselves)
  • We used the classifiers to classify the top 20
    features of 21 randomly chosen concepts
  • We separately evaluated the results
  • CATEGORIES
  • Clustering of the balanced dataset
  • PROBLEM The WordNet category structure is highly
    subjective

32
ATTRIBUTE CLASSIFICATION
Description Cross-Validation Cross-Validation Cross-Validation Human Judge (AA) Human Judge (AA) Human Judge (AA) Human Judge (MP) Human Judge (MP) Human Judge (MP)
Correctly Classified Instances (Accuracy) 928 (out of 1155) 80.35 928 (out of 1155) 80.35 928 (out of 1155) 80.35 244 (out of 365) 66.85 244 (out of 365) 66.85 244 (out of 365) 66.85 182 (out of 260) 70.00 182 (out of 260) 70.00 182 (out of 260) 70.00
Measure P R F P R F P R F
Activity 0.822 0.878 0.849 0.833 0.593 0.693 0.875 0.622 0.727
Part/Related-Object 0.842 0.882 0.862 0.832 0.712 0.767 0.793 0.802 0.798
Quality 0.799 0.821 0.810 0.767 0.629 0.691 0.864 0.646 0.739
Related-Agent 0.930 0.970 0.950 0.815 0.917 0.863 0.826 0.826 0.826
Not-Attribute 0.602 0.487 0.538 0.339 0.627 0.440 0.297 0.633 0.404
33
CLUSTERING WITH 2-WAY CLASSIFIER
All Candidate Attributes Heuristic filtering Filtering by classification
Purity 0.657 0.672 0.693
Entropy 0.335 0.319 0.302
Vector size 24,178 4,296 3,824
Clustered Concepts 402 402 401
34
CLUSTERING ERROR ANALYSIS
ANIMAL bear, bull, camel, cat, cow, deer, dog, elephant, horse, kitten, lion, monkey, puppy, rat, sheep, tiger, turtle
35
CLUSTERING ERROR ANALYSIS
ANIMAL bear, bull, camel, cat, cow, deer, dog, elephant, horse, kitten, lion, monkey, puppy, rat, sheep, tiger, turtle
EDIBLE FRUIT apple, banana, berry, cherry, fig, grape, kiwi, lemon, lime, mango, melon, olive, orange, peach, pear, pineapple, strawberry, watermelon, (pistachio, oyster)
36
CLUSTERING ERROR ANALYSIS
ANIMAL bear, bull, camel, cat, cow, deer, dog, elephant, horse, kitten, lion, monkey, puppy, rat, sheep, tiger, turtle
EDIBLE FRUIT apple, banana, berry, cherry, fig, grape, kiwi, lemon, lime, mango, melon, olive, orange, peach, pear, pineapple, strawberry, watermelon, (pistachio, oyster)
ILLNESS acne, anthrax, arthritis, asthma, cancer, cholera, cirrhosis, diabetes, eczema, flu, glaucoma, hepatitis, leukemia, malnutrition, meningitis, plague, rheumatism, smallpox, (superego, lumbago, neuralgia, sciatica, gestation, menopause, quaternary, pain)
37
CLUSTERING ERROR ANALYSIS
ANIMAL bear, bull, camel, cat, cow, deer, dog, elephant, horse, kitten, lion, monkey, puppy, rat, sheep, tiger, turtle
EDIBLE FRUIT apple, banana, berry, cherry, fig, grape, kiwi, lemon, lime, mango, melon, olive, orange, peach, pear, pineapple, strawberry, watermelon, (pistachio, oyster)
ILLNESS acne, anthrax, arthritis, asthma, cancer, cholera, cirrhosis, diabetes, eczema, flu, glaucoma, hepatitis, leukemia, malnutrition, meningitis, plague, rheumatism, smallpox, (superego, lumbago, neuralgia, sciatica, gestation, menopause, quaternary, pain)
IN WORDNET PAIN
38
LIMITS OF THIS TYPE OF EVALUATION
  • No way of telling how complete / accurate are our
    concept descriptions
  • Both in terms of relations and in terms of their
    relative importance
  • No way of telling whether the category
    distinctions we get from WordNet are empirically
    founded

39
BEYOND JUDGES / EVALUATION AGAINST WORDNET
  • Task-based evaluation
  • Evidence from other areas of cognitive science
  • (ESSLLI 2008 Workshop - Baroni / Evert / Lenci )

40
TASK-BASED (BLACK-BOX) EVALUATION
  • Tasks requiring lexical knowledge
  • Lexical tests
  • TOEFL test (Rapp 2001, Turney 2005)
  • NLP tasks
  • Eg, anaphora resolution (Poesio et al 2004)
  • Actual applications
  • E.g., language models (Mitchell Lapata ACL
    2009, Lapata invited talk)

41
EVIDENCE FROM OTHER AREAS OF COGNITIVE SCIENCE
  • Attributes evidence from psychology
  • Association lists (priming)
  • E.g., use results of association tests to
    evaluate proximity (Lund et al, 1995 Pado and
    Lapata, 2008)
  • Comparison against feature norms Schulte im
    Walde, 2008)
  • Feature norms
  • Category distinctions evidence from neural
    science

42
OUTLINE
  • Vector space representations
  • An example of Semantic-based vector space model
  • How to evaluate such models
  • Attribute extraction and Feature norms
  • Category distinctions and Brain data

43
FEATURE-BASED REPRESENTATIONS IN PSYCHOLOGY
  • Feature-based concept representations assumed by
    many cognitive psychology theories (Smith and
    Medin, 1981, McRae et al, 1997)
  • Underpin development of prototype theory (Rosch
    et al)
  • Used, e.g., to account for semantic priming
    (McRae et al, 1997 Plaut, 1995)
  • Underlie much work on category-specific defects
    (Warrington and Shallice, 1984 Caramazza and
    Shelton, 1998 Tyler et al, 2000 Vinson and
    Vigliocco, 2004)

44
FEATURE NORMS
  • Subjects produce lists of features for a concept
  • Weighed by number of subjects that produce them
  • Several existing (Rosch and Mervis, Garrard et
    al, McRae et al, Vinson and Vigliocco)
  • Substantial differences in collection methodology
    and results

45
SPEAKER-GENERATED FEATURES (VINSON AND VIGLIOCCO)
46
COMPARING CORPUS FEATURES WITH FEATURE NORMS
(Almuhareb et al 2005, Poesio et al 2007)
  • 35 concepts in common between the Almuhareb
    Poesio dataset and the dataset produced by
    Vinson and Vigliocco (2002, 2003)
  • ANIMALS bear, camel, cat, cow, dog, elephant,
    horse, lion, mouse, sheep, tiger, zebra
  • FRUIT apple, banana, cherry, grape, lemon,
    orange, peach, pear, pineapple, strawberry,
    watermelon
  • VEHICLE airplane, bicycle, boat, car,
    helicopter, motorcycle, ship, truck, van
  • We compared the features we obtained for these
    concepts with the speaker-generated features
    collected by Vinson and Vigliocco

47
RESULTS
  • Best recall 52 (using all attributes and
    values)
  • Best precision 19
  • But high correlation (ro.777) between the
    distances between concept representations
    obtained from corpora, and the distances between
    the representations for the same concepts
    obtained from subjects (using the cosine as a
    measure of similarity)

48
DISCUSSION
  • Substantial differences in features and overlap,
    but correlation similar
  • Problems
  • Each feature norm slightly different
  • They have been normalized by hand LOUD, NOISY,
    NOISE all mapped to LOUD

49
AN EXAMPLE STRAWBERRY
Speaker-generated features Matching Features Collected Using Our Text Patterns (with frequency)
red (20) red (5), colour (5), color (1)
fruit (18) fruit (5)
sweet (13) sweetness (8)
has seeds (12) seeds (6), seed (2)
grows (10) growth (1), ripening (10)
small (6) size (19)
taste (6) taste (6), flavor (6), flavour (2)
food (5) nutrition (1)
from garden (5) cultivation (7), harvest (6), harvester (2)
juice (5) juice (10), juices (3)
dessert (3) sweetness (8)
eat (3) nutrition (1)
50
Problem differences between feature norms
  • motorcycle
  • Vinson Vigliocco
  • wheel, motor, loud, vehicle, wheel, fast, handle,
    ride, transport, bike, human, danger, noise,
    seat, brake, drive, fun, gas, machine, object,
    open, small, travel, wind
  • Garrard et al
  • vehicle, wheel, fast, handlebar, light, seat,
    make a noise, tank, metal, unstable, tyre,
    coloured, sidecar, indicator, pannier, pedal,
    speedometer, manoeuvrable, race, brakes, stop,
    move, engine, petrol, economical, gears
  • McRae et al
  • wheels, 2_wheels, dangerous, engine, fast,
    helmets, Harley_Davidson, loud, 1_or_2_people,
    vehicle, leather, transportation, 2_people, fun,
    Hell's_Angels, gasoline
  • Mutual correlation of ranks ranges from 0.4 to 0.7

51
FEATURE NORMS (GARRARD ET AL 2001)
52
DISCUSSION
  • Preliminary conclusions need to collect new
    feature norms for CL
  • E.g., use similar techniques to collect
    attributes for WordNet
  • See Kremer Baroni 2008
  • For more work on using feature norms for
    conceptual acquisition, see
  • Schulte im Walde 2008
  • Baroni et al to appear
  • For the correlation between feature norms and
    information in WordNet (meronymy, isa, plus info
    from glosses) Barbu Poesio GWC 2008

53
OUTLINE
  • Vector space representations
  • An example of Semantic-based vector space model
  • How to evaluate such models
  • Attribute extraction and Feature norms
  • Category distinctions and brain data

54
USING BRAIN DATA TO IDENTIFY CATEGORY DISTINCTIONS
  • Studies of brain-damaged patients have been shown
    to provide useful insights in the organization of
    conceptual knowledge in the brain
  • Warrington and Shallice 1984, Caramazza Shilton
    1998
  • fMRI has been used to identify these distinctions
    in healthy patients as well
  • E.g., Martin Chao
  • See, e.g., Capitani et al 2003 for a survey

55
CATEGORY DISTINCTIONS IN THE BRAIN
56
CORPUS DATA AND BRAIN DATA
  • Can brain data (from healthy patients) be used to
    get an objective picture of categorical
    distinctions in the brain?
  • Can our findings be useful to understand better
    the neurological results?
  • Ongoing project using EEG and fMRI to identify
    such distinctions

57
EEG Spectral Analysis of Concepts
  • Participants presented with aural or visual
    concept stimuli
  • EEG apparatus records electrical activity on the
    scalp
  • Waveforms can be reduced to frequency components

58
EEG vs. fMRI
59
EEG pros and cons
  • Pros
  • Lighter
  • Cheaper
  • Better temporal resolution (ms)
  • Cons
  • Coarser spatial resolution (cm)
  • Noisy (e.g., very sensitive to skull depth)

60
A CATEGORY DISTINCTION EXPERIMENT WITH EEG
  • Murphy Poesio Bovolo DalPonte Bruzzone, Cogsci
    2008
  • Seven Italian native speakers
  • Image stimuli only
  • 30 tools
  • 30 animals
  • Each stimulus presented six times
  • Optimal time / frequency window identified
    automatically
  • 100-370ms
  • 3-17 Hz

61
Stimuli Images from Web
62
Data analysis
  • Preprocessing
  • Artefact removal
  • Feature extraction
  • CSSD a form of supervised component analysis
  • Classification
  • Using SVMs

63
EEG SIGNALS TIME-FREQUENCY (PER CHANNEL)
64
Extraction of features (EEGOXELS) from EEG data
65
Data analysis
66
RESULTS
67
Classification resultsAnimals vs plants
Visual Auditory
Participant A 74.6 88.2
Participant B 72.4 65.4
Participant C 82.6 92.7
Participant D 81.8 77.7
68
RESULTSacross participants
69
Representation of categories in CSSD spaces
  • component analysis identifies 2-dimensional
    spaces
  • Analysis of these spaces may provide useful data
    against which to compare our corpus models

70
CSSD-derived conceptual spaces
71
BRAIN DATA AND CORPUS DATA
  • What is the relation between the conceptual
    spaces induced from corpora with the conceptual
    spaces elicited using EEG?

72
PREDICTING BRAIN (FMRI) ACTIVATION USING CONCEPT
DESCRIPTIONS
  • T. Mitchell, S. Shinkareva, A. Carlson, K. Chang,
    V. Malave, R. Mason and M. Just. 2008. Predicting
    human brain activity associated with the meanings
    of nouns. Science 320, 11911195

73
MITCHELL ET AL 2008 METHODS
  • Record fMRI activation for 60 nominal concepts
  • And extract 200 best features, or VOXELs
  • Build conceptual descriptions for these concepts
    from corpora (the Web)
  • 25 features for each concept
  • 25 verbs expressing typical properties of living
    things / tools
  • Collect strength of association between these
    features and each concept
  • Learn association between each voxel and the 25
    verbal features using 58 concepts
  • Use learned model to predict activation of 2
    held-out data (compare using Euclidean distance)
  • Accuracy 77

74
MITCHELL ET AL 2008
75
MITCHELL ET AL 2008 VERB FEATURES
76
MITCHELL ET AL LEARNING ASSOCIATIONS
77
OUR EXPERIMENTS
  • Replicate the Mitchell et al study using EEG data
    instead of fMRI
  • Different feature selection mechanisms
  • Compare different methods for building concept
    descriptions
  • In addition to hand-picked, also a variety of
    standard corpus models
  • For Italian
  • B. Murphy, M. Baroni, M. Poesio, EEG responds to
    conceptual stimuli and corpus semantics, EMNLP
    2009

78
RESULTS USING THE HAND-PICKED FEATURES
79
RESULTS USING AUTOMATICALLY SELECTED FEATURES
80
COMPARISON BETWEEN CORPUS MODELS
81
RECAP
  • We need to relate the evidence from corpora with
    evidence about concepts coming from empirical
    work in the neuroscience and psychology
  • Feature norms databases could be used to
    evaluate attribute extraction
  • But need to find better ways of collecting them
  • Brain data may give us information about the
    real conceptual categories
  • Results still preliminary

82
COLLABORATORS
MARCO BARONI(Trento)
BRIAN MURPHY(Trento)
ABDULRAHMAN ALMUHAREB(Essex PhD 2006 Now at
KACST, Saudi Arabia)
EDUARD BARBU(Trento PhD forthc)
HEBA LAKANY(formerly Essex, now Strathclyde)
83
THANKS
To the audience ..
And to Galja, Ruslan the other organizers for
yet another splendid RANLP!
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