MetaNet: A Multilingual Metaphor Extraction, Representation, and Validation System
Srini Narayanan ICSI Open House 2/23/2012
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Presentations Srini Narayanan (AI and Cognitive Science) MetaNet: The Overall Project
George Lakoff (Linguistics and Cognitive Science) Metaphor and Cognition
Ekaterina Shutova (AI, NLP) Machine Learning for Metaphor Extraction
FrameNet Demo and Poster (Collin Baker)
The ICSI Team
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ICSI, UCB Srini Narayanan (AI and Cognitive Science) George Lakoff (Linguistics and Cognitive Science) Collin Baker (Project Manager) – FrameNet demo Jerome Feldman (EECS and Cognitive Science) Ekaterina Shutova (Computational Linguistics) – Chuck Fillmore (Linguistics) Dan Klein (EECS)
UC Merced Teenie Matlock (Cognitive Science)
Stanford Lera Boroditsky (Psychology)
UCSD Ben Bergen (Cognitive Science)
USC Lisa Aziz-Zadeh (Neuroscience)
Eötvös Loránd University, Hungary Zoltan Kovecses (Language)
Conceptual Metaphor
Many abstract concepts have conventional metaphorical conceptualizations normal everyday ways of using concrete concepts to reason systematically about abstract concepts.
Most abstract reasoning makes use of embodied
reasoning via metaphorical mappings from concrete to abstract domains
Metaphorical Grasping There is a conceptual metaphor, Understanding Is Grasping,
according to which one can grasp ideas. One can begin to grasp an idea, but not quite get a
hold of it. If you fail to grasp an idea, it can go right by you — or
over your head! If you grasp it, you can turn it over in your mind. You can’t hold onto an idea before having grasped it. In short, reasoning patterns about physical grasping
can be mapped by conceptual metaphor onto abstract reasoning patterns.
A large cross-cultural embodied metaphor is the Event structure metaphor Maps motion and manipulation to abstract action
Goals Build a methodology for metaphor analysis
Automated extraction Cross-cultural repository Affect identification Belief/world-view discovery
Validate/Evaluate methodology Extraction in four languages for target concepts
English, Persian, Russian, Spanish Computational model based on Cognitive Linguistics results
Functional repository with framings and mappings Mappings at multiple levels and cultural variations Dimensions relevant to world-views/belief discovery and intervention
Demonstrate coherence, inference, decision impact of metaphors in a series of case studies
Investigate metaphoric affect and role in decision making
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A Pilot Task: Interpret simple newspaper stories France fell into recession. Pulled out by Germany. US Economy on the verge of falling back into recession after
moving forward on an anemic recovery. One year ago, the American economy was teetering on the
verge of total collapse. Indian Government stumbling in implementing Liberalization
plan. Moving forward on all fronts, we are going to be ongoing and
relentless as we tighten the net of justice. The Government is taking bold new steps. We are loosening
the stranglehold on business, slashing tariffs and removing obstacles to international trade.
Technical Details: A Pilot System (Narayanan 2010)
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Results
Model was implemented and tested on discourse fragments from a database of 50 newspaper stories in international economics from standard sources such as WSJ, NYT, and the Economist.
Results show that motion terms are often the most effective method to provide the following types of information about abstract plans and actions. Information about uncertain events and dynamic changes in goals and
resources. (sluggish, fall, off-track, no steam) Information about evaluations of policies and economic actors and
communicative intent (strangle-hold, bleed). Communicating complex, context-sensitive and dynamic economic
scenarios (stumble, slide, slippery slope). Communicating complex event structure and aspectual information (on
the verge of, sidestep, giant leap, small steps, ready, set out, back on track).
ALL THESE BINDINGS RESULT FROM REFLEX, AUTOMATIC INFERENCES PROVIDED BY SIMULATION BASED INFERENCES.
Papers at (http://www.icsi.berkeley.edu/~snarayan/publications.html)
Theoretical Framework 1. Metaphors form structured, hierarchical, relational networks connecting
frames, frame roles, fillers, and other mappings. 2. Mappings from source to target domain are partial. 3. Metaphor is not always in the words, but rather evidenced by patterns of
thought. 1. Linguistic metaphor is a reflection of deep and elemental cognitive processes. 2. Learning metaphors is a natural outgrowth of neural processing
4. Metaphor systems and cultural variation in metaphor is experientially grounded.
1. systematically extends different patterns of our shared embodiment and experience (perceptual, motor, and social).
5. Metaphors often occur in larger narrative structures. 1. Narratives provide coherence to metaphor structures and to lived experience and
are indispensable to understand morals, beliefs, attitudes, and behaviors
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Approach
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Technical Details: Extraction Cognitive Linguistic Theory
Initial analysis and feature selection Frame Identification Seeding
Unsupervised Learning Shutova et al., (2010)
Supervised Learning Gedigian et al., (2006)
Embodied Construction Grammar (ECG) Analyzer Produces linked schemas with role bindings Implements cognitive linguistics in a computational model
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Past Results: ICSI Recognizer (ScaNLU 2006) Max-Ent Classifier
Features include: Target FN Verb, Propbank Args, Head (Collins) Head type (NE, backoff to WN)
Extraction (Shutova 2010)
Minimally-supervised approach using seed metaphorical expressions
Identifying new metaphors by means of verb and noun clustering
Spectral clustering Verb sub-categorization frames and rich
annotation features High precision of 0.79 (Shutova 2010).
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Evaluation and Social Science Validation Corpus Methods Basic Scalar measures
familiarity, accessibility, acceptability, imageability, well-formedness, conventionality, metaphoricity, informativeness, and productivity
Behavioral Tests lexical and conceptual priming, inference. measures of memorability,
paraphrasing and explication, gesture, eye, body tracking Affective Aspects
Behavioral (IAT, Psychological measures) Imaging
Metaphoric activation of emotional circuits anterior insula, and the fear and reward circuits of the amygdala and the
nucleus accumbens.
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Validation: Metaphors and Crime (Thibodeau and Boroditsky (2011))
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Question: Can metaphors overcome priors?
Véronique Boulenger, Olaf Hauk, and Friedemann Pulvermüller, Cerebral Cortex, August 2009
SOMATOTOPY FOR GRASPING AN IDEA KICKING THE HABIT
Does metaphoric affect impact emotional systems? Titrate based on individual and cultural dispositions
Concluding remarks Exciting project, that provides an opportunity for us as a team
to consolidate our main line of research and extend it in specific challenging ways Multiple languages Automating deep semantics Validation
Articulates with more basic science efforts on Computation, Cognition, and Language. Basic research provides constraints and uses the resources
produced by this project Large metaphor repository Cultural differences in frequency and usage statistics
Computational model and results suggest new experiments
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Presentations
Srini Narayanan (AI and Cognitive Science) MetaNet: The Overall Project
George Lakoff (Linguistics and Cognitive Science) Metaphor and Cognition
Ekaterina Shutova (AI, NLP) Machine Learning for Metaphor Extraction
FrameNet Demo and Poster (Collin Baker)