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Machine Learning for (Psycho-)Linguistics

Machine Learning for (Psycho-)Linguistics. Walter Daelemans daelem@uia.ua.ac.be http://cnts.uia.ac.be CNTS, University of Antwerp ILK, Tilburg University QITL-02. Outline. Machine Learning of Language Induction of rules and classes Learning by Analogy Case Studies

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Machine Learning for (Psycho-)Linguistics

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  1. Machine Learning for (Psycho-)Linguistics Walter Daelemans daelem@uia.ua.ac.be http://cnts.uia.ac.be CNTS, University of Antwerp ILK, Tilburg University QITL-02

  2. Outline • Machine Learning of Language • Induction of rules and classes • Learning by Analogy • Case Studies • Discovery of phonological categories and morphological rules • A single-route model of morphological processing • Issues • Probabilities versus symbolic structure induction • Nativism versus empiricism • Exemplar analogy versus rules

  3. Experience BIAS Learning Component Search Rj Ri Rk Output Input Rl Performance Component

  4. Problems with Probabilities • Explanation • Also applies to neural networks • Event relevance • Especially in unsupervised learning (clustering) • Incorporation of linguistic knowledge • Smoothing zero-frequency events

  5. (symbolic) machine learning • Rule induction (understandable induced theories) • Inductive Logic Programming (incorporating linguistic knowledge) • Memory-based learning (similarity-based smoothing of sparse data, feature weighting) • …

  6. Common Fallacies • Rules = nativism (and connections = empiricism) • Generalization = abstraction (and memory = table-lookup)

  7. Rule-Based  Innate • Rules can be induced from primary linguistic data as well • Applications in Linguistics • Evaluation and comparison of linguistic hypotheses • Discovery of linguistic generalizations and categories

  8. Allomorphy in Dutch Diminutive • “one of the more spectacular phenomena of modern Dutch morphophonemics” Trommelen (1983) • Base form of Noun + [tje] (5 variants) • Linguistic theory (from Te Winkel 1862) Rime last syllable, stress, morphological structure, … Trommelen 1983 Local phenomenon, stress & morphological structure do not play a role • CELEX data (3900 nouns) - b i = - z @ = + m A nt  je

  9. Allomorphs

  10. Decision Tree Learning • Given a data set, construct a decision tree that reflects the structure of the domain • A decision tree is a tree where • non-leaf nodes represent features (tests) • branches leading out of a test represent possible values for the feature • leaf nodes represent outcomes (classes) • Decision Tree can be translated into a set of IF-THEN rules (with further optimization) • Value grouping

  11. Decision Tree Construction Given a set of examples T • If T contains one or more cases all belonging to the same class C, then the decision tree for T is a leaf node with category C. • If T contains different classes then • Choose a feature, and partition T into subsets that have the same value for the feature chosen. The decision tree consists of a node containing the feature name, and a branch for each value leading to a subset. • Apply the procedure recursively to subsets created this way.

  12. Induced rule set Default class is -tje • IF coda last is /lm/ or /rm/ THEN -pje • IF nucleus last is [+bimoraic] AND coda last is /m/ THEN -pje • IF coda last is /N/ THEN IF nucleus penultimate is empty or schwa THEN -etje ELSE -kje • IF nucleus last is [+short] and coda last is [+nas] or [+liq] THEN -etje • IF coda last is [+obstruent] THEN -je

  13. Results • Problem is almost perfectly learnable (98.4%) • More than last syllable is needed for a full solution • Only rime of last syllable (not stress or onset) is relevant • Induced Categories • Nasals, liquids, obstruents, short vowels, bimoraic vowels (consists of vowels, diphtongs, schwa) • Task-dependent categories? Category formation is dependent on the task to be learned, not absolute, not language-independent

  14. Conclusions: Rule Induction in Linguistics • Falsify existing linguistic theories • Evaluate role of linguistic information sources • (Re)discover interesting linguistic rules (= supervised learning) • (Re)discover interesting linguistic categories (= unsupervised learning) • Empiricist alternative for (mostly nativist) rule-based systems

  15. There is one small problem … • Current methodology for comparative machine learning experiments is not reliable (especially with small data) • Different runs of the algorithm provide different resulting rule sets • Algorithm can be tweaked to get high performance with any information source combination • Algorithm is highly sensitive to training data, feature selection, algorithm parameter settings, … • Only to be used as a heuristic • As with your own rule induction module

  16. Word Sense Disambiguation (do) Similar: experience, material, say, then, … Local Context + keywords Default 49.0 47.9 Optimized parameters LC 60.8 59.5 Optimized parameters 61.0 60.8

  17. Generalisation  Abstraction Rule Induction Connectionism Inductive Logic Programming Statistics Handcrafting + abstraction … (Fill in your most hated linguist here) + generalisation - generalisation Memory-Based Learning Table Lookup - abstraction

  18. MBL: Use memory traces of experiences as a basis for analogical reasoning, rather than using rules or other abstractions extracted from experience and replacing the experiences. This “rule of nearest neighbor” has considerable elementary intuitive appeal and probably corresponds to practice in many situations. For example, it is possible that much medical diagnosis is influenced by the doctor's recollection of the subsequent history of an earlier patient whose symptoms resemble in some way those of the current patient. (Fix and Hodges, 1952, p.43)

  19. -etje Rule Induction -kje Coda last syl Nucleus last syl

  20. -etje MBL -kje Coda last syl ? Nucleus last syl

  21. Memory-Based Learning • Basis: k nearest neighbor algorithm: • store all examples in memory • to classify a new instance X, look up the k examples in memory with the smallest distance D(X,Y) to X • let each nearest neighbor vote with its class • classify instance X with the class that has the most votes in the nearest neighbor set • Choices: • similarity metric • number of nearest neighbors (k) • voting weights

  22. ib1 Metrics ib1-ig ib1-mvdm

  23. Metrics (2) Voting options: • Equal weight for each nearest neighbor • Distance weighted voting • Inverse distance 1/D(X,Y) (Wettschereck, 1994) • RBF-style gaussian voting function (Shepard, 1987) • Linear voting function (Dudani, 1976) (NB: weighted NN distribution can be used as conditional probability)

  24. MBL Acquisition • Inflectional process is represented by a set of exemplars in memory • Exemplars act as models • Learning is incremental storage of exemplars • Compression and Metrics • Exemplar consists of set of (mostly symbolic) features

  25. MBL Processing • New instances of a performance process are solved through • Memory-lookup • Analogical (Similarity-Based) Reasoning • Similarity metric • Language (faculty) - independent • Adaptive (feature and exemplar weighting)

  26. The properties of language processing tasks … • Language processing tasks are mappings between linguistic representation levels that are • context-sensitive (but mostly local!) • complex (sub/ir/regularity), pockets of exceptions • Similar representations at one linguistic level correspond to similar representations at the other level • Several information sources interact in (often) unpredictable ways at the same level • Data is sparse

  27. … fit the bias of MBL • Inference is based on Similarity-Based / Analogical Reasoning • Adaptive data fusion / relevance assignment is available through feature weighting • It is a non-parametric approach • Similarity-based smoothing is implicit • Regularities and subregularities / exceptions can be modeled uniformly

  28. German and Dutch plurals

  29. Data & Representation • Symbolic features • segmental information (syllable structure) • stress • gender • German Plural (~ 25,000 from CELEX) Vorlesung (lecture) l e - z U N F en Classes: e (e)n s er - U- Uer Ue • Dutch Plural (~ 62,000 from CELEX) ontruiming (evacuation) 0 - O nt 1 r L - 0 m I N en Classes: (e)n s (-eren, -i, -a, …)

  30. Cognitive Architectures of Inflectional Morphology Dual Route • Dual Route (Pinker, Clahsen, Marcus …) • Rules for regular cases • (over)generalization • default behaviour • Associative memory for exceptions • irregularization / family effects • Single Route (R&M, MacWhinney, Plunkett, Elman, …) • Frequency-based regularity Suffix-class Memory Failure Pattern Rule Associator Input Features

  31. German Plural • Notoriously complex but routinely acquired (at age 5) • Evidence for Dual Route ? -s suffix is default/regular (novel words, surnames, acronyms, …) -s suffix is infrequent (least frequent of the five most important suffixes)

  32. The default status of -s • Similar item missing Fnöhk-s • Surname, product name Mann-s • Borrowings Kiosk-s • Acronyms BMW-s • Lexicalized phrases Vergissmeinnicht-s • Onomatopoeia, truncated roots, derived nouns, ...

  33. Discussion • Three “classes” of plurals: ((-en -)(-e -er))(s) the former 4 suffixes seem “regular”, can be accurately learned using information from phonology and gender -s is learned reasonably well but information is lacking • Hypothesis: more “features” are needed (syntactic, semantic, meta-linguistic, …) to enrich the “lexical similarity space” • No difference in accuracy and speed of learning with and without Umlaut • Overall generalization accuracy very high: 95% • Schema-based learning (Köpcke). *,*,*,*,i,r,M e

  34. Acquisition Data:Summary of previous studies • Existing nouns: (Park 78; Veit 86; Mills 86; Schamer-Wolles 88; Clahsen et al. 93; Sedlak et al. 98) • Children mainly overapply -e or -(e)n • -s plurals are learned late • Novel words: (Mugdan 77; MacWhinney 78; Phillis & Bouma 80; Schöler & Kany 89) • Children inflect novel words with -e or -(e)n • More “irregular” plural forms produced than “defaults”

  35. MBL simulation • model overapplies mainly -en and -e • -s is learned late and imperfectly • Mainly but not completely parallel to input frequency (more -s overgeneralization than -er generalization)

  36. Bartke, Marcus, Clahsen (1995) • 37 children age 3.6 to 6.6 • pictures of imaginary things, presented as neologisms • names or roots • rhymes of existing words or not • choice -en or -s • results: • children are aware that unusual sounding words require the default • children are aware that names require the default

  37. MBL simulation • sort CELEX data according to rhyme • compare overgeneralization • to -en versus to -s • percentage of total number of errors • results: • when new words don’t rhyme more errors are made • overgeneralization to -en drops below the level of overgeneralization to -s

  38. Dutch Plural Suffixes -en and -s are both defaults, and are in complementary distribution Selection of -en or -s governed by • phonological structure of the base noun (stressed vs. unstressed last syllable) • morphological structure (suffix of the base noun) • loan word status • semantic feature person vs. thing • both are possible after // (Baayen et al. 2001)

  39. Feature Relevance

  40. Accuracy on CELEX • Methodology • “Leave-one-out” • Results: • MBL 94.9 % accuracy Prec Rec F -(e)n 95.8 97.2 96.4 -s 93.8 91.4 92.6 -i 82.0 77.2 79.5 • without stress 94.9 % accuracy • last syllable with stress 92.6 % accuracy • last syllable without stress 92.4 % accuracy • rhyme last syllable 89.6 % accuracy

  41. Accuracy on pseudo-words • Methodology • Train: Celex (all) and Celex (1000 most frequent types) • Test: 8 * 10 pseudo-words (Baayen et al., 2001) dreip - workel - bastus - bestroeting - kloertje stape - stree - kadisme • Results: accuracy = number of decisions equal to subject majority for each item • Subjects 87.5 % • MBL (all) 83.8 % • MBL (top 1000) 90.0 %

  42. muidus, muidi nn: modus, modi Low frequency and loan word nearest neighbours Celex bias

  43. Conclusions Memory-Based Single Route • MBLP picks up the main “schemata” of Dutch and German plural formation and their exceptions without recourse to explicit rules or a dual route architecture • MBLP trained on (part of) CELEX matches subject behavior on pseudo words and acquisition data • Segmental information suffices to reliably predict plural in Dutch and most plurals in German, additional information needed for German -s • Heterogeneity and density in lexical exemplar space as source of behavior predictions

  44. Overall Conclusions • Advantages of symbolic machine learning methods over ‘pure statistics’ • As a methodology for inducing interpretable linguistic generalizations and categories • As a way of introducing an operationalisation of analogy-based methods into (psycho)linguistics

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