Word Alignment: Difference between revisions

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{{#ev:youtube|https://www.youtube.com/watch?v=mqyMDLu5JPw|800|center}}
{{#ev:youtube|https://www.youtube.com/watch?v=mqyMDLu5JPw|800|center}}
In the [[Sentence Alignment|previous lecture]], we saw how to find sentences in parallel data which correspond to each other. Now we move one step further and look for '''words''' which correspond to each other in our parallel sentences.
This task is usually called '''word alignment'''. Note that once we have solved it, we get a (probabilistic) translation dictionary for free. See our sample "parallel corpus":
[[File:word-alignment.png|300px]]
For example, if we look for translations of the Russian word "дом", we collect all ''alignment links'' in our data and


<ref name="ibmmodels">Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, Robert L. Mercer ''[http://www.aclweb.org/anthology/J93-2003 The Mathematics of Statistical Machine Translation: Parameter Estimation]''</ref>
<ref name="ibmmodels">Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, Robert L. Mercer ''[http://www.aclweb.org/anthology/J93-2003 The Mathematics of Statistical Machine Translation: Parameter Estimation]''</ref>

Revision as of 15:15, 24 March 2015

Lecture 8: Word Alignment
Lecture video: web TODO
Youtube
Exercises: IBM-1 Alignment Model

{{#ev:youtube|https://www.youtube.com/watch?v=mqyMDLu5JPw%7C800%7Ccenter}}

In the previous lecture, we saw how to find sentences in parallel data which correspond to each other. Now we move one step further and look for words which correspond to each other in our parallel sentences.

This task is usually called word alignment. Note that once we have solved it, we get a (probabilistic) translation dictionary for free. See our sample "parallel corpus":

For example, if we look for translations of the Russian word "дом", we collect all alignment links in our data and

[1]


Exercises

References

  1. Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, Robert L. Mercer The Mathematics of Statistical Machine Translation: Parameter Estimation