1. Tagging and Statistically Translating Latin Sentences
Andrew Runge
Computer Systems Lab

2. Abstract Goal: Create an accurate Latin translator Methods:
Language Rules
Machine Learning
Computer-Assisted Translation
Statistical Translation
Language: Python

3. Introduction Language Translators Machine Learning Word Tagging
Statistical Translation
Parts of Speech

4. Background Machine Learning Word Tagging Type, case, tense, etc.
Statistical Translation
N-Grams
Part of Speech Pairings
Computer Assisted Translation

5. Figure 1: Tree of words sorted by sentence role from the
assorted works of Cicero generated by the methods of
McMahon and Smith

6. Discussion Dictionary creation Dictionary Keys and values Word Tagging
Nouns v. Verbs

7. Example of word tagging from similar experiment by Bowden

8. Discussion Translation: Forms Human Element

9. Figure 2: Demonstration of n-gram generation for determining
word order in a sentence. Generated by Chen et al.

10. Dictionaries

11. Dictionaries

12. Results

13. Future Work Current Continued Work: Tag Culling Machine translation
Additional grammar support