## Intro: Analog Function Generator

### Vocab word analogy quiz generator. Software Downloads.

In this article, we presented a new approach for generating multiple-choice analogy questions from existing ontologies. We described the design of analogy-generator and analogy-solver. The solver achieved a maximum accuracy of 88%. However, it achieved a low accuracy value of 8% when used to solve analogies generated from the Gene Ontology. We assume that the difficulty of the domain is considered as an additional dimension to our difficulty controlling model.

### Vocab word analogy quiz generator in description

For future work, we are going to generalise our approach for analogy generator to include user-defined relations. To evaluate analogies generated from arbitrary relations, we suggest using Latent Relational Similarity (LRS) (Turney ) which has the advantage of learning relations instead of using predefined joining terms.

In order to evaluate the proposed approach for analogy generation, we follow the method explained by Turney and Littman (Turney and Littman ) for evaluating analogies using a large corpus. In their study, Turney and Littman reported that their method can solve about 47% of multiple-choice analogy questions (compared to an average of 57% correct answers solved by high school students). The solver takes a pair of words representing the stem of the question and five other pairs representing the answers presented to students. Their proposed method is inspired by the Vector Space Model (VSM) of informational retrieval. For each provided answer, the solver creates two vectors representing the stem (R_{1}) and the given answer (R_{2}). The solver returns a numerical value for the degree of analogy between the stem and the given answer. Then, the answers are ranked according to their analogy value and the answer with the highest rank is considered the correct answer. To create the vectors, they proposed a table of 64 joining terms that can be used to join the two words in each pair (stem or answer). The two words and joined by these joining terms in two different ways (e.g. “X is Y” and “Y is X”) to create a vector of 128 features. The actual values stored in each vector are calculated by counting the frequencies of those constructed terms in a large corpus (e.g. web resources indexed by a search engine). To improve the accuracy of their proposed method, they suggested using the logarithm of the frequency instead of the frequency itself.