Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

18.2.3.1 Learning
For adaptation of weights in the neural net, shown in fig.18.7, one simple
method is to first map the patterns from different sentences onto the nodes of
the neurons and then update the weights by employing the principles of bidirectional
associative memory (BAM) [5]. For instance, suppose we have
two sentences:
1. A cat killed a rat.
2. A man threw a stone.
d0 d1 d2 d3 d4 d5 d6
a0 a1 a2 a3 a4 a5 a6
n0 n1 n2 n3 n4 n5 n6
v0 v1 v2 v3 v4 v5 v6
e0 e1 e2 e3 e4 e5 e6
Start of the cat killed a rat End of
Sentence (ART) (N) (V) (ART) (N) Sentence
←activated node
Fig. 18.7: A neural net with exhaustive connectivity (not shown for clarity)
between nodes of each two successive layers.
The activated nodes in the neural net of fig.18.7 for these patterns may
be evaluated. The activated node i will have a node value ai=1 and the rest of
the non-activated nodes in the net will have node value=-1. The weight wij
from neuron ni to nj is now given by
wij = ∑∀ pattern k (ai. aj)k (18.1)
In this example we have only two patterns; so k has a maximum value 2. Thus
18.2.3.2 Recognition
wij = (ai.aj)1 + (ai.aj)2 (18.2)
For recognizing the parts of speech of a missing word in a sentence, the
following recognition rules may be employed.