ing the distance between them, therefore one should avoid calling those measures
"metrics".

## 4.4 Performance Of Hamming And Euclidean Measures

In this Section we present some test results comparing the effectiveness of Hamming and Euclidean measures against the computation of similarity by inner product. These tests are conducted on the data set presented in Table 1. Once the inner product test indicates more than one potential answer, Hamming and Euclidean measures are employed upon the subset of the potential answers - not upon the whole clean-up memory. Figures 10 through 12 show test results for sentences with various numbers of blades using two types of construction: agent-object construction and agent-object construction with odding blades.

There was no significant difference between results obtained using the agentobject construction with odding blades and those obtained with the help of Plate construction, therefore results for Plate construction are not presented in the diagrams. Nevertheless, it is more in the spirit of distributed representations to use agent-object construction with odding blades since the additional blade is drawn at random, whereas the use of Plate construction makes data more predictable. Poor recognition in case of the agent-object construction without odding blades results from complete or partial similarity cancellation.

It becomes apparent that the best types of construction of sentences for GA are agent-object construction with odding blades and the Plate construction, as they ensure that sentences have an odd number of blades. Further, it is advisable to compute similarity by the means of Hamming measure or the Euclidean measure instead of the inner product. The Euclidean measure recognizes 100% of items much faster
(i.e. for smaller data size), but for large data size both measures behave identically.

Therefore Hamming measure should be used to calculate similarity since its computation requires less time. The success of those measures is due to the fact that the differences between matrices or their signatures lessen the similarity, whereas differences in blades did not lessen the value of the inner product considerably.

## 5 The Average Number Of Potential Answers

Our point of interest in this Section will be to analyze the influence of accidental blade equality on the number of potential answers under agent-object construction with appropriate-hand-side reversed questions. The following estimates assume ideal conditions, i.e. no two chunks of a sentence are identical up to a constant at any time. Intuitively, such conditions could be met for sufficiently large lengths of the input vectors, whereas vectors of short length will with high probability be linearly dependent. We will estimate the average number of times that a nonzero inner