Parsing PCFG within a General Probabilistic Inference Framework

DOI

10.2991/agi.2009.46How to use a DOI?

Abstract

One of the aims of Artificial General Intelligence(AGI) is to use the same methods to reason over a large num- ber of problems spanning different domains. Therefore, advancing general tools that are used in a number of domains like language, vision and intention reading is a step toward AGI. Probabilistic Context Free Gram- mar (PCFG) is one such formalism used in many do- mains. However, many of these problems can be dealt with more effectively if relationships beyond those en- coded in PCFGs (category, order and parthood) can be included in inference. One obstacle to using more general inference approaches for PCFG parsing is that these approaches often require all state variables in a domain to be known in advance. However, since some PCFGs license infinite derivations, it is in general im- possible to know all state variables before inference. Here, we show how to express PCFGs in a new proba- bilistic framework that enables inference over unknown objects. This approach enables joint reasoning over both constraints encoded by a PCFG and other con- straints relevant to a problem. These constraints can be encoded in a first-order language that in addition to encoding causal conditional probabilities can also represent (potentially cyclic) boolean constraints.

Copyright

© 2009, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Arthi Murugesan
AU  - Nicholas L. Cassimatis
PY  - 2009/06
DA  - 2009/06
TI  - Parsing PCFG within a General Probabilistic Inference Framework
BT  - Proceedings of the 2nd Conference on Artificial General Intelligence (2009)
PB  - Atlantis Press
SP  - 218
EP  - 223
SN  - 1951-6851
UR  - https://doi.org/10.2991/agi.2009.46
DO  - 10.2991/agi.2009.46
ID  - Murugesan2009/06
ER  -