Conceptual Role Semantics

- And a model for

cross-system translations –Presented by Ellie Hua Wang

The background

• Traditional philosophy of language: meaning – symbol–world relation

• Frege-problem: Hesperus, Phosphorus

• Philosophy of mind: Content of thought - concept-world relation

• External grounding account vs. Conceptual web account

• Putnam-problem: the twin earth case

Conceptual role semantics (CRS)

• The meanings of expressions (concepts) depend on their connections to each other in a system

• Defenders in philosophy: Wilfrid Sellars(1963),Ned Block (1986, 1999),Gilbert Harman (1974, 1987),and Michael Devitt, Brian Loar,

William Lycan, Hartry Field

Two theories for CRS in Philosophy

• Putnam-problem

• Two-factor semantic theory (Block 1986, Field 1977, Lycan 1984): a theory of meaning consists of two components, a theory of truth and CRS.

• One-factor (long arm) semantic theory (Harman 1987): the conceptual roles reach out into the world of referents

Applications of CRS

• Linguistics - Saussure (1915, 1959) : concepts are negatively defined

• Philosophy of Science - Kuhn (1962) : incommensurability thesis

• Psychology - Barr and Caplan (1987), Goldstone (1993, 1995, 1996) : concepts are frequently characterized by their associative relations to other concepts

More applications

• Computer science:

- Lenat and Feigenbaum (1991)

- Landauer and Dumais (1997) – Latent Semantic Analysis

- Rapaport (2002) - SNePs

External grounding account

• Are roles in conceptual network sufficient for meaning?– Symbol grounding problem– Putnam’s problem

• Arguments from Psychology and Computer Science

Criticisms of CRS

• Concept identity - a problem with meaning holism

• Concept similarity

• The problems with the two theories

Cross-system translation (Goldstone and Rogosky 2002)

• The notion of Conceptual Correspondence: two concepts correspond to each other if they play equivalent roles within their systems

• A neural network ‘ABSURDIST’ (Aligning Between Systems Using Relations Derived Inside Systems for Translation) provides a formal method for deciding conceptual correspondence across systems solely on the basis of relations between concepts within a system

ABSURDIST

• Goal:

- to illustrate the sufficiency of a conceptual web account for translating between systems

- to indicate synergistic interactions between within-system information and extrinsic information

ABSURDIST

• Does not connect concepts to the external world

• Is not to create rich translations between systems, but just explores the simplest representations of concepts

• Is not a complete model for meaning

• Is not a simulation on human translation

ABSURDIST – inputtwo 2D proximity matrices

ABSURDIST-the algorithm

ABSURDIST-the algorithm cont.

ABSURDIST - assessment

• The distances between every pair of elements within a system D (x , y) are computed by:

ABSURDIST’s tolerance to distortion

• It also shows that the algorithm’s ability to recover correspondences generally increases as a function of the number of elements in each system, at least for small levels of noise.

• The performance gradually deteriorates with added noise, but that the algorithm is robust to at least modest amounts of noise.

• The performance is a lot better than chance performance.

ABSURDIST’s tolerance to distortion cont.

This graph reveals that partially correct translations are rarely obtained. With relatively few exceptions, either ABSURDIST finds all of the correct correspondences, or finds none.

More graphs

The number of iterations required for good performance is not appreciably affected by the number of items per system.

Different-size systems

When different-sized systems are compared, ABSURDIST’s correspondences are still typically one-to-one, but not all elements of the larger system are placed incorrespondence.

Subset matching

ABSURDIST will draw correspondences between the three pairs of elements that share the majority of their roles in common, but not between the fourth, mismatching elements.

Indirect similarity relations

if two elements within a system enter into the same set of similarity relations, they still may be disambiguated because all correspondences are worked out simultaneously

Integrating internal and external information

One way to incorporate extrinsic biases into the system is by initially seeding correspondence units with values. The amount by which translation accuracy improves beyond the amount predicted generally increases as a function of system size.

Integrating internal and external information cont.

• Using only information intrinsic to a system results in better correspondences than using only extrinsic information.

• The superior performance of the network that uses both intrinsic and extrinsic information derives from its robustness in the face of noise.

Conclusions

• Translations between two systems can be found using only information about the relations between elements within a system.

• Intrinsic relations suffice to determine cross-system translations, but if extrinsic information is available, more robust, noise resistant translations can be found.

Discussions

• The correspondence between concepts within an internal system and physically measurable elements of an external system

• Analytic and synthetic distinction

• Psychological distance determination

• The assumption of the model – similar structures of the systems