Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations
Table 1. Similarity score comparisons between vector embeddings and WordNet (Wu-Palmer)
['[BOLD] Word Pair', '[ITALIC] vecsim', '[ITALIC] wup']
The vector embeddings consider the context of the words while learning vectors. By applying vector operations, information like word similarities, analogies can be obtained. and its supertypes. For example, for an intent to search hotels, we trained the vectors with description of hotels and its supertypes like lodging businesses. This gave us around 2.5M words. Typically vector embeddings work well with billions words in terms of training data, but we use this vector only as a supporting factor to include some verbs that the more generic ConceptNet Numberbatch embbeddings may be missing. Fasttext uses ngrams as the smallest units instead of words, this actually gives us the advantage of calculating similarity scores for words that do not exist in the vocabulary, which is more likely in a small vocabulary like 2.5M. The relevantWords function takes all the synonyms of a word as parameter. We first find the similar words in conceptnetVec filtered by the part-of-speech tag. Then, we compare the similar words from dsVec with the ones found in conceptnetVec and include them into our relevant words list. If the similarity score exceeds the β threshold. The threshold value 0.5 has been picked as an initial value after some manual observations. This threshold value can be picked more intelligently in the future. When we compare the verbs search and find, the vector similarity (vecsim) gives a better score then Wu-Palmer similarity (wup).