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)