@inproceedings{kordjamshidi-etal-2016-better,
title = "Better call {S}aul: Flexible Programming for Learning and Inference in {NLP}",
author = "Kordjamshidi, Parisa and
Khashabi, Daniel and
Christodoulopoulos, Christos and
Mangipudi, Bhargav and
Singh, Sameer and
Roth, Dan",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1285",
pages = "3030--3040",
abstract = "We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP). We enrich Saul with components that are necessary for a broad range of learning based Natural Language Processing tasks at various levels of granularity. We illustrate these advances using three different, well-known NLP problems, and show how these generic learning and inference modules can directly exploit Saul{'}s graph-based data representation. These properties allow the programmer to easily switch between different model formulations and configurations, and consider various kinds of dependencies and correlations among variables of interest with minimal programming effort. We argue that Saul provides an extremely useful paradigm both for the design of advanced NLP systems and for supporting advanced research in NLP.",
}
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<abstract>We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP). We enrich Saul with components that are necessary for a broad range of learning based Natural Language Processing tasks at various levels of granularity. We illustrate these advances using three different, well-known NLP problems, and show how these generic learning and inference modules can directly exploit Saul’s graph-based data representation. These properties allow the programmer to easily switch between different model formulations and configurations, and consider various kinds of dependencies and correlations among variables of interest with minimal programming effort. We argue that Saul provides an extremely useful paradigm both for the design of advanced NLP systems and for supporting advanced research in NLP.</abstract>
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%0 Conference Proceedings
%T Better call Saul: Flexible Programming for Learning and Inference in NLP
%A Kordjamshidi, Parisa
%A Khashabi, Daniel
%A Christodoulopoulos, Christos
%A Mangipudi, Bhargav
%A Singh, Sameer
%A Roth, Dan
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F kordjamshidi-etal-2016-better
%X We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP). We enrich Saul with components that are necessary for a broad range of learning based Natural Language Processing tasks at various levels of granularity. We illustrate these advances using three different, well-known NLP problems, and show how these generic learning and inference modules can directly exploit Saul’s graph-based data representation. These properties allow the programmer to easily switch between different model formulations and configurations, and consider various kinds of dependencies and correlations among variables of interest with minimal programming effort. We argue that Saul provides an extremely useful paradigm both for the design of advanced NLP systems and for supporting advanced research in NLP.
%U https://aclanthology.org/C16-1285
%P 3030-3040
Markdown (Informal)
[Better call Saul: Flexible Programming for Learning and Inference in NLP](https://aclanthology.org/C16-1285) (Kordjamshidi et al., COLING 2016)
ACL
- Parisa Kordjamshidi, Daniel Khashabi, Christos Christodoulopoulos, Bhargav Mangipudi, Sameer Singh, and Dan Roth. 2016. Better call Saul: Flexible Programming for Learning and Inference in NLP. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3030–3040, Osaka, Japan. The COLING 2016 Organizing Committee.