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Acquisition of Semantic Classes for Adjectives from Distributional Evidence

Acquisition of Semantic Classes for Adjectives from Distributional Evidence. Gemma Boleda Universitat Pompeu Fabra Barcelona. general picture. automatic classification of adjectives Catalan according to broad semantic characteristics clustering syntactic evidence. motivation.

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Acquisition of Semantic Classes for Adjectives from Distributional Evidence

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  1. Acquisition of Semantic Classes for Adjectives from Distributional Evidence Gemma Boleda Universitat Pompeu Fabra Barcelona

  2. general picture • automatic classification of adjectives • Catalan • according to broad semantic characteristics • clustering • syntactic evidence

  3. motivation • Lexical Acquisition • infer properties of words • lexical bottleneck • both symbolic and statistical approaches • adjectives • determining NP reference • the French general • establishing properties of entities • this maimai is round and sweet

  4. motivation • initial motivation: POS-tagging • 55% remaining ambiguity involves adjectives general francès:‘French general’ or ‘general French’? • observations • general tendencies in syntactic behaviour of adjectives • ... which correspond to broad semantic properties • generalisation: best at semantic level • low-level tasks (POS-tagging) • initial schema for lexical semantic representation

  5. approach • no general, well established semantic classification • have to build and test ours! • clustering: unsupervised technique • groups objects according to feature distribution • does not depend on pre-classification • provides insight into the nature of the data • shallow approach to syntax: n-grams • limited syntactic distribution • local relationship to arguments => test feasibility

  6. Boleda, Badia, Batlle (2004) outline • adjective syntax and semantic classification • methodology • experiment 1 • experiment 2 • partial conclusions • outlook: rest of the thesis

  7. outline • adjective syntax and semantic classification • methodology • experiment 1 • experiment 2 • partial conclusions • outlook: rest of the thesis

  8. adjective syntax • default function: noun modifier (92%) • right of the noun (default position: 72%) • some to the left (‘epithets’: 28%) • predicative uses unfrequent (7%), but significant

  9. two-way classification • number of arguments • unary: pilota vermella ‘red ball’ • binary: professor gelós de la Maria ‘teacher jealous of Maria’ • ontological kind (Ontological Semantics) • basic:vermell ‘red’ • object: malaltia pulmonar ‘pulmonary disease’ (=> lung) • event: propietat constitutiva ‘constitutive property’ (=> constitutes)

  10. Ontological Semantics • coverage (ordinary cases) • machine tractability • explicit model of world: ontology • vermell => attribute::colour::red(x) • pulmonar => related-to::lung(x) • constitutiu => event::benef::constitute(x) • however: no commitment to particular framework

  11. rationale • observation: syntactic preferences correspond to semantic properties • hypothesis: we can use syntactic features to infer semantic classes

  12. outline • adjective syntax and semantic classification • methodology • experiment 1 • experiment 2 • conclusions and future work

  13. data and procedure • 2283 adjectives >50 times in 16 million word Catalan corpus • lemma and morphological info • cluster the whole set • perform different tasks on different subsets • tuning subset: choose features • Gold Standard: evaluation and analysis

  14. features and feature selection • features: • empirically chosen from blind distribution • double bigram, simplified POS-representation • tuning subset: 100 adjectives • choose features (distribution)

  15. Fig. A: Feature selection

  16. analysis • Gold Standard • 80 adjectives • annotated by 3 human judges, acceptable agreement (92 and 84%, .72 and .74 kappa)

  17. outline • adjective syntax and semantic classification • methodology • experiment 1 • experiment 2 • partial conclusions • outlook: rest of the thesis

  18. experiment 1: unary / binary • final evaluation:10 features, raw percentage • clustering algorithm: k-means (cosine) • predictions: • binary adjectives cooccur with prepositions more frequently than unary ones • unary adjectives are more flexible

  19. unary (yellow) binary (red) Fig. B: Clusters vs. unary/binary unary / binary: results • agreement with Gold Standard: • 97%, kappa = 0.87 • comparable to humans • features:

  20. outline • adjective syntax and semantic classification • methodology • experiment 1 • experiment 2 • partial conclusions • outlook: rest of the thesis

  21. experiment 2: basic / object / event • final evaluation: 32 features, normalisation • clustering algorithm: k-means (cosine) • predictions: • basic adjectives are flexible, work as epithets, occur in predicative contexts, appear further from the noun • object adjectives appear rigidly after the noun • event adjectives tend to occur in predicative positions and do not act as epithets

  22. Fig C: Clusters vs. basic/event/object basic / object / event: results object (yellow) • agreement with Gold Standard: • 73%, kappa = 0.56 • lower than humans • features: event (orange) basic (red)

  23. Fig C: Clusters vs. basic/event/object Fig D: Clusters vs. unary/binary basic/object/event: error analysis • something has gone wrong! • characterisation of event adjectives basic adjectives with an object reading (polysemy) binary! unary event adjectives binary event adjectives

  24. outline • adjective syntax and semantic classification • methodology • experiment 1 • experiment 2 • partial conclusions • outlook: rest of the thesis

  25. partial conclusions • overall, results seem to back up: • use of syntax-semantics interface for adjectives • linguistic predictions as to relevant features and differences across classes • shallow approach • unary / binary: piece of cake • few binary adjectives, but worth spotting (denote relationships)

  26. partial conclusions • basic / object / event: need reworking • object adjectives seem to be the most robust class • variation in basic adjectives (default class), polysemy • event adjectives: seem to behave much like basic adjectives with respect to features chosen => redefine class?

  27. outline • adjective syntax and semantic classification • methodology • experiment 1 • experiment 2 • partial conclusions • outlook: rest of the thesis

  28. outlook: rest of the thesis • rethink classification • redefine features in light of results • integrate polysemy judgments into the experiment and analysis • perform experiments with other corpora

  29. classification • what to do with event adjectives? cp.: • constitutiu ‘constitutive’ (“active”) • legible ‘readable’ (“passive”) • reproductor ‘reproducing’ (“active, habituality”) • yet another parameter: gradability • important for adjectives • should be easy to induce

  30. better blind distribution or self-defined features? • n-grams: sparseness, selection • other features? • account for different levels of description

  31. polysemy • crucial aspect, explains much of results • difficult to integrate! • meaningless kappa values • alternatives? • clearer definition of polysemy within task • specific tests • other resources: dictionary?

  32. other resources • CUCWeb (208 million word) http://www.catedratelefonica.upf.es • test whether “more data is better data” (Mercer and Church 1993: 18-19) • advantages and challenges of Web corpora • current results: for verb subcategorisation experiment, results 12 points lower than using smaller, balanced, controled corpus

  33. Acquisition of Semantic Classes for Adjectives from Distributional Evidence Gemma Boleda Universitat Pompeu Fabra Barcelona

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