Step Using Machine Learning Techniques/Statistical Techniques
Having now established our baseline, we then approached the problem of designing a mapping function from a different angle: using machine learning and information retrieval techniques to generate relevant answers. establish how similarity and relationships between words can be modelled in a computer program. The modern approach is to use Vector south korea rcs data Space models which map each individual word to a unique point in vector space, in other words, each word is represented as a series of 100 numbers. The position of each word in vector space is relative to all other words in vector space: words that have similar meanings are close to each other, and the resulting vector produced by the subtraction of one word’s vector to another defines the relationship between two words. A common example is King-Queen: Man – Woman where each word is actually the vector of that word. The detail of how these vectors are generated is beyond the scope of this blog, just to note that they are critically important. Enabling words to be treated as numbers means that mathematical calculations can be performed on lexical items.
Step Siamese Neural Network
Since the previous two methods performed unsatisfactorily, we adopted a different approach which centres on using “neural networks” to learn and generate a mapping function instead. As the dataset we are working with is rather small (only 171 correct QA pairs). We opted to use a Siamese Neural network (SNN) which is a special type of neural network consisting of two identical neural networks which share a set of weights. The question vector is fed into one neural network and the answer is inputted into the other network (see diagram below).