DMCA
Towards Developing Probabilistic Generative Models for Reasoning with Natural Language Representations (2005)
| Venue: | In the Proceedings of the 6th International Conference on Computational Linguistics and Text Processing (Lecture Notes in Computer Science 2406 Springer 2005, ISBN |
| Citations: | 4 - 2 self |
BibTeX
@INPROCEEDINGS{Marcu05towardsdeveloping,
author = {Daniel Marcu and Ana-maria Popescu},
title = {Towards Developing Probabilistic Generative Models for Reasoning with Natural Language Representations},
booktitle = {In the Proceedings of the 6th International Conference on Computational Linguistics and Text Processing (Lecture Notes in Computer Science 2406 Springer 2005, ISBN},
year = {2005},
pages = {3--540}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Probabilistic generative models have been applied successfully in a wide range of applications that range from speech recognition and part of speech tagging, to machine translation and information retrieval, but, traditionally, applications such as reasoning have been thought to fall outside the scope of the generative framework for both theoretical and practical reasons. Theoretically, it is difficult to imagine, for example, what a reasonable generative story for firstorder logic inference might look like. Practically, even if we can conceive of such a story, it is unclear how one can obtain sufficient amounts of training data. In this paper, we discuss how by embracing a less restrictive notion of inference, one can build generative models of inference that can be trained on massive amounts of naturally occurring texts; and text-based deduction and abduction decoding algorithms. 1.
Keyphrases
natural language representation towards developing probabilistic generative model reasonable generative story training data probabilistic generative model sufficient amount speech recognition text-based deduction firstorder logic inference generative framework information retrieval machine translation restrictive notion wide range generative model massive amount practical reason speech tagging
