User:Francis Tyers/Perceptron

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 (Conversion error. Server ("https://wikimedia.org/api/rest_") reported: "Cannot get mml. Server problem."): {\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.
L' estiu és una estació de l' any.	Summer of one of the seasons of the year.
Barcelona-Sants és una estació de tren a Barcelona.	Barcelona-Sants is a train station in Barcelona.
...

Training data

s = estació, t* = season

_ sec	1
_ de el any	1
_ de tren	0
_ de el línia	0
_ humit	1
_ plujós	1
un _ a	0

Feature vector

This is the above training data expressed as an input vector 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 x_j} to the perceptron.

_ sec	_ de el any	_ de tren	_ de la línia	_ humit	_ plujós	un _ a		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 d}
1	0	0	0	0	0	0		1
0	1	0	0	0	0	0		1
0	0	1	0	0	0	0		0
0	0	0	1	0	0	0		0
0	0	0	0	1	0	0		1
0	0	0	0	0	1	0		1
0	0	0	0	0	0	1		0

Weight vector

_ sec	_ de el any	_ de tren	_ de la línia	_ humit	_ plujós	un _ a
0.0	0.0	0.0	0.0	0.0	0.0	0.0