Authors: Vasileios Hatzivassiloglou and Kathleen R. McKeown Presenter: Marian Olteanu

1. Towards the automatic identification of adjectival scales: clustering adjectives according to meaning Authors: Vasileios Hatzivassiloglou and Kathleen R. McKeown Presenter: Marian Olteanu

2. Introduction • Group adjectives according to their meaning • Semantic relateness – between adjectives which describe the same property • Goal • Adjectival scales • Method • Statistical • Augmented with linguistic information derived from the corpus

3. Adjectival scales • Linguistic scale – set of words of the same grammatical category that can be ordered by their semantic strength or degree of informativeness • Example: lukewarm, warm, hot • Adjectives – elements on the scale can be partitioned into 2 groups, in each group – total order • Negative and positive degrees

4. Adjectival scales • Tests for acceptance • Horn: “x even y” • Data sparseness – infrequent patterns in real corpora • Scales vary accros domains

5. Methodology • Four stages • Extract linguistic data from the parsed corpus – word pairs • Info processed by morphological component – group together similar pairs • Independent similarity modules – number between 0 and 1

6. Methodology • Four stages (cont) • Module that combines all the similarity measures into one dissimilarity measure • Module that clusters adjectives into groups based on dissimilarity measure • Linguistic data • That tell if adjectives are related – adj.-noun pairs • That tell if adjectives are unrelated – adj.-adj. pairs

7. Methodology • Adj.-noun pairs • Distribution of nouns and adjective modifiers • Expectation: similar adjectives tend to modify the same set of nouns • Adj.-adj. pairs • Adjectives that describe the same property do not appear in the same minimal NP • Antithetical: hot cold, red black • Non-antithetical: hot warm • Adj. that modifies each other: light blue shirt

8. Computing similarity between adjectives • Adjective-noun pairs • Robust non-parametric method – Kendall’sτ coefficient for two random variables with paired observations • (Xi,Yi) and (Xj,Yj) – two pairs of observations for adj. X and Y on the nouns I and j • Concordant if Xi>Xj and Yi>Yj or Xi<Xj and Yi<Yj • Discordant, if Xi>Xj and Yi<Yj or Xi<Xj and Yi>Yj • τ = pc-pd • Unbiased estimator:

9. Methodology • Adjective-adjective pairs • Reject pairs that occur in the same NP • High accuracy, low coverage • Combining similarity estimates • If pair was rejected by adj.-adj. module: dissimilarity = k (usually 10) • Else, dissimilarity = 1 – (similarity by adj.-noun module)

10. Clustering the adjectives • Goal – optimal partition • Algorithm • Non-hierarchical • Number of partitions – input parameter • Exchange method • K-means is not applicable • Minimizing the objective function Φ

11. Clustering the adjectives • Algorithm (cont.) • Random partition • Compute the improvement by moving an adjective to a different cluster • Hill-climbing • Local minima • Call the algorithm multiple times with different starting positions

12. Results

13. Results • Clusters #5 and #8 – adjectives that indicate size • Clustering discourages large clusters • Cluster #6: 5 words • Methods to increase number of pairs • Larger corpus • More syntactical patterns

14. Evaluation • Evaluation • 9 human judges • manually created partitions (6 to 11 clusters) • “Cross-validation” for human judges: 49% to 59% for F-measure

15. Evaluation • Lower bound • Monte Carlo analysis • F-measure: 1 in 20,000 trials • Fallout: 4.9%