Abstract: A computerized method is disclosed including operations of receiving a plurality of request texts, and for each request text of the plurality of request texts: performing a pre-processing operation, performing a first text similarity procedure or a second text similarity procedure that each result in a determination of a most similar request text in a knowledge base, wherein the second text similarity procedure includes performance of word embedding operations, determining a degree of similarity between the current request text and the most similar request text, when the degree of similarity satisfies a similarity threshold comparison, associating an answer associated with the most request text with the current text request, and when the degree of similar does not satisfy the similarity threshold comparison, flagging the current request text or associating a placeholder answer with the current request text. Performing pre-processing may include removing stop words and punctuation and creating tokenized text.

Abstract: A computerized method is disclosed including operations of receiving one or more request texts, including at least a first request text, automatically performing processing on the first request text to determine a most similar request text in a knowledge base, determining a degree of similarity between the first request text and the most similar request text, and in response to a comparison between the degree of similarity and a similarity threshold, retrieving, from the knowledge base, an answer corresponding to the most similar request text. Performing processing may include (i) removing stop words and punctuation and creating tokenized text, (ii) converting the tokenized text into a vector using a trained neural network, and (iii) performing an analysis of the vector with the entries of the knowledge base using one or more of a Word Mover's Distance (WMD) algorithm, or a Soft Cosine Measure (SCM) algorithm.