Difference between revisions of "User:Francis Tyers/Perceptron"
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===Features=== |
===Features=== |
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The features we will be working with are ngram contexts around the "problem word". |
The features we will be working with are ngram contexts around the "problem word". These can be extracted from the word alignments calculated from a parallel corpus. |
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{|class=wikitable |
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! Catalan !! English |
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|Durant l' estació seca les pluges són escasses. || During the dry season it rains infrequently. |
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===Training data=== |
===Training data=== |
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Revision as of 20:02, 8 November 2014
A perceptron is a classifier that
The classifier consists of:
- Binary features
- Weights
Example
Here is a worked example of a perceptron applied to the task of lexical selection. Lexical selection is the task of choosing a target translation Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle t*} for a given source word Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle s} in a context Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle C} out of a set of possible translations Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle T} . A perceptron makes a classification decision for a single class, so we need to train a separate perceptron for each possible target word selection.
In the example,
- Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle s} = estació
- Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle T} = {season, station}
- Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle t*} = season
Features
The features we will be working with are ngram contexts around the "problem word". These can be extracted from the word alignments calculated from a parallel corpus.
| Catalan | English |
|---|---|
| Durant l' estació seca les pluges són escasses. | During the dry season it rains infrequently. |
Training data
_ sec |
1 |
_ de el any |
1 |
_ de tren |
0 |
_ de el línia |
0 |
_ humit |
1 |
_ plujós |
1 |
un _ a |
0 |