Self-organizing Conceptual Map and Taxonomy of Adjectives

1. Self-organizing Conceptual Map and Taxonomy of Adjectives Noriko Tomuro, DePaul University Kyoko Kanzaki, NICT Japan Hitoshi Isahara, NICT Japan April 20, 2007

2. Overview • In natural languages, adjectives are polysemous. • “warm soup” (temperature), “warm person” (personality) • Conversely, a given adjectival concept includes adjectives extended from various domains. • e.g. Adjectives which express feeling –“happy”(emotion), “cold”(temperature), “painful”(sensation) • We use Kohonen Self-Organizing Map (SOM) to • Visualize the adjectival concept space. • Create a taxonomy of adjectival concepts. • A comprehensive, corpus-based study of adjectives.

3. Outline • Adjectival Concepts • Kohonen Self-Organizing Map (SOM) • Conceptual Map of Adjectival Concepts • Taxonomy of Adjectival Concepts • Conclusions • Future Work

4. 1. Adjectival Concepts (1) • An adjectival concept is a semantic class of adjectives (adjectives which express XX). • Some adjectival concepts are more closely related than others. • perception – “warm”, “painful” • personality – “warm”, “gentle” • degree – “high”, “wide” • We wish to visualize the adjectival concept space • automatically, using corpus data; • on a 2-dimensional map.

5. 1. Adjectival Concepts (2) • Use abstract nouns to represent an adjectival concept. • Extract examples from corpus (Japanese newspaper articles) where adjectives modify abstract nouns. • e.g. “warm personality”, “warm feeling” • Dataset: 361 Japanese abstract nouns, defined by 2374 adjectives • Frequency counts are changed to Mutual Information (MI) values (for feature weighting).

6. 2. Kohonen Self-Organizing Map (SOM) (1) • SOM is an unsupervised learning method, originally developed by T. Kohonen in 1980’s. • Used for: • neuroscience (to map sensory stimuli to brain) • clustering • visualizing high-dimensional data in low dimension (usually a 2-dimensional grid)

8. 2. SOM (2) • Each node in a SOM map is associated with a reference vector. • During learning, weights on the reference vectors are adjusted so that similar input instances are mapped to nearby nodes in the map.

9. 3. Conceptual Map of Adjectival Concepts Map size: 45 * 45

10. Overlaying tight clusters the “TOP” node

11. Degree fast, high, cheap, wide, … State, Status unhappy, dangerous, difficult, … Effect, Influence powerful, extreme,… Talent, Ability excellent, creative, … Viewpoint, Domain, Attitudetraditional, conservative, historical, … TOP, Thing, Feeling, Aspect Shape, Appearance round, square, flat, three-dimensional, Image, Figure, Atmosphere tender, brilliant, quiet, … Characteristics of humans or things, Relation brave, monopolistic, privileged, intimate, … Sense, Perception soft, bitter, red, white, … Conceptual Area Map of Adjectives

12. happy, sad,hot, cold,painful, tickling Feeling hot, cold,painful, tickling happy,sad Sense Emotion Tactualsensation hot,cold painful,tickling Temperature 4. Taxonomy of Adjectival Concepts (1) • We also wish to extract a taxonomy of adjectival concepts – indicates the breadth of the concepts. • Hierarchical relation is based on subsumption.

13. 4. Taxonomy of Adjectival Concepts (2) • The derived SOM map also readily formed a taxonomy, because: • highly abstract nouns take on many adjectives; • less abstract nouns take on specific adjectives. • Connect map nodes in the parent-child relation(using cosine & entropy) => a taxonomy overlaid on the SOM map highly abstract moderately abstract less abstract

14. Degree State, Status Effect, Influence Talent, Ability Viewpoint, Domain, Attitude TOP, Matter, Feeling, Shape, Appearance Image, Figure, Atmosphere Characteristics of humans or things, Relation … Sense, Perception • Branches are descending to specific concept areas. • Concepts near “TOP” are densely overlapping – extremely abstract concepts are vague and indistinguishable.

15. Feeling Aspect Image Direction Circumstance Atmosphere Nature Situation Perception Personality Shade Degree Order Taxonomy of “kibishii (tough/hard/strict)” • Taxonomy is a graph (not a tree), forming a complex hierarchical structure. • We can observe the breadth of various concepts. • e.g. Many kinds of “image” – atmospheric (“quiet image”), perceptual (“soft image”), personality (“brave image”). • Also we can observe relative closeness between concepts.

16. 5. Conclusions • We used SOM to visualize the adjectival concept space and derive a taxonomy. • Our results will be useful in various areas: • to study how adjectives extend meanings (meaning shift) -- linguistics • to study how adjectives are acquired or cognitively modeled -- cogsci and psychology • to (automatically) derive the meaning of a sentence at a deeper level -- NLP • as meta-tags to describe data instances – semantic web

17. 7. Future Work • Apply the method to other data: • other language (e.g. English) • other genre (e.g., web texts) • Conduct psychological experiments to see the correlation with human cognition of adjectives. • Develop lexical representation for adjectives.