Cogprints

Answering Subcognitive Turing Test Questions: A Reply to French

Turney, Peter (2001) Answering Subcognitive Turing Test Questions: A Reply to French. [Preprint]

Full text available as:

[img]
Preview
Postscript
572Kb
[img]PDF
47Kb

Abstract

Robert French has argued that a disembodied computer is incapable of passing a Turing Test that includes subcognitive questions. Subcognitive questions are designed to probe the network of cultural and perceptual associations that humans naturally develop as we live, embodied and embedded in the world. In this paper, I show how it is possible for a disembodied computer to answer subcognitive questions appropriately, contrary to French’s claim. My approach to answering subcognitive questions is to use statistical information extracted from a very large collection of text. In particular, I show how it is possible to answer a sample of subcognitive questions taken from French, by issuing queries to a search engine that indexes about 350 million Web pages. This simple algorithm may shed light on the nature of human (sub-) cognition, but the scope of this paper is limited to demonstrating that French is mistaken: a disembodied computer can answer subcognitive questions.

Commentary on:Turing, A. M. (1950) Computing Machinery and Intelligence. [Journal (Paginated)]
Item Type:Preprint
Keywords:subcognitive questions, Turing Test, PMI-IR, co-occurrence, word associations, mutual information.
Subjects:Computer Science > Language
Computer Science > Statistical Models
Philosophy > Philosophy of Mind
ID Code:1798
Deposited By:Turney, Peter
Deposited On:13 Sep 2001
Last Modified:11 Mar 2011 08:54

Commentary/Response Threads

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Church, K.W., and Hanks, P. (1989). Word association norms, mutual information and

lexicography. In Proceedings of the 27th Annual Conference of the Association of

Computational Linguistics, pp. 76-83.

Church, K.W., Gale, W., Hanks, P., and Hindle, D. (1991). Using statistics in lexical

analysis. In Uri Zernik (ed.), Lexical Acquisition: Exploiting On-Line Resources to

Build a Lexicon. New Jersey: Lawrence Erlbaum, pp. 115-164.

Firth, J.R. (1957). A synopsis of linguistic theory 1930-1955. In Studies in Linguistic

Analysis, pp. 1-32. Oxford: Philological Society. Reprinted in F.R. Palmer (ed.),

Selected Papers of J.R. Firth 1952-1959, London: Longman (1968).

French, R.M. (1990). Subcognition and the limits of the Turing Test. Mind, 99: 53-65.

French, R.M. (2000). Peeking behind the screen: The unsuspected power of the standard

Turing Test. Journal of Experimental and Theoretical Artificial Intelligence, 12, 331-340.

Landauer, T.K., and Dumais, S.T. (1997). A solution to Plato’s problem: The Latent

Semantic Analysis theory of the acquisition, induction, and representation of

knowledge. Psychological Review, 104: 211-240.

Manning, C.D., and Schütze, H. (1999). Foundations of Statistical Natural Language

Processing. Cambridge, Massachusetts: MIT Press.

Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59: 433-460.

Turney, P.D. (2001). Mining the Web for synonyms: PMI-IR versus LSA on TOEFL.

Proceedings of the Twelfth European Conference on Machine Learning (ECML-2001),

in press.

Metadata

Repository Staff Only: item control page