• Corpus ID: 51801236

The Open A.I. Kit: General Machine Learning Modules from Statistical Machine Translation

@inproceedings{Walker2005TheOA,
  title={The Open A.I. Kit: General Machine Learning Modules from Statistical Machine Translation},
  author={Daniel J. Walker},
  booktitle={MTSUMMIT},
  year={2005}
}
The Open A.I. Kit implements the major components of Statistical Machine Translation as an accessible, extendable Software Development Kit with broad applicability beyond the field of Machine Translation. The high-level system design policies of the kit embrace the Open Source development model to provide a modular architecture and interface, which may serve as a basis for collaborative research and development for endeavors in Artificial Intelligence. 
1 Citations

MOOD: A Modular Object-Oriented Decoder for Statistical Machine Translation

TLDR
MOOD has been modularized using an object-oriented approach which makes it suitable for the fast development of state-of-the-art decoders and a clone of the pharaoh decoder has been implemented and evaluated.

References

SHOWING 1-10 OF 16 REFERENCES

Pharaoh: A Beam Search Decoder for Phrase-Based Statistical Machine Translation Models

We describe Pharaoh, a freely available decoder for phrase-based statistical machine translation models. The decoder is the implement at ion of an efficient dynamic programming search algorithm with

SRILM - an extensible language modeling toolkit

TLDR
The functionality of the SRILM toolkit is summarized and its design and implementation is discussed, highlighting ease of rapid prototyping, reusability, and combinability of tools.

Statistical language learning

TLDR
Eugene Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages and is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning.

The mathematics of statistical machine translation

TLDR
A series of five statistical models of the translation process are described and algorithms for estimating the parameters of these models given a set of pairs of sentences that are translations are given.

Fast Decoding and Optimal Decoding for Machine Translation

TLDR
This paper compares the speed and output quality of a traditional stack-based decoding algorithm with two new decoders: a fast greedy decoder and a slow but optimal decoder that treats decoding as an integer-programming optimization problem.

Statistical language modeling using the CMU-cambridge toolkit

TLDR
The CMU Statistical Language Modeling toolkit was re leased in in order to facilitate the construction and testing of bigram and trigram language models and the technology as implemented in the toolkit is outlined.

Design of a linguistic statistical decoder for the recognition of continuous speech

TLDR
This paper describes the overall structure of a linguistic statistical decoder (LSD) for the recognition of continuous speech and describes a phonetic matching algorithm that computes the similarity between phonetic strings, using the performance characteristics of the AP.

A Systematic Comparison of Various Statistical Alignment Models

TLDR
An important result is that refined alignment models with a first-order dependence and a fertility model yield significantly better results than simple heuristic models.

Artificial Intelligence: A Modern Approach

The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.

Understanding Open Source and Free Software Licensing

TLDR
This concise guide focuses on annotated licenses, offering an in-depth explanation of how they compare and interoperate, and how license choices affect project possibilities.