Abstract: Methods, devices, and systems are described for creating and implementing search query vectors for knowledge base articles or other formal articles, the query vectors automatically created from informal correspondence such as a service request email to an information technology (IT) department. Term frequency-inverse document frequency (TF-IDF) scores are calculated for rarewords in the correspondence with respect to a corpus of other service requests. High scoring terms with the same neighbors as those in the corpus of formal articles are added to the search query vector, while high scoring terms that do not share the same neighbors are thrown out. The query vector is then used to run a search of the knowledge base for relevant articles.

Abstract: Methods, devices, and systems are described for creating and implementing search query vectors for knowledge base articles or other formal articles, the query vectors automatically created from informal correspondence such as a service request email to an information technology (IT) department. Term frequency-inverse document frequency (TF-IDF) scores are calculated for rarewords in the correspondence with respect to a corpus of other service requests. High scoring terms with the same neighbors as those in the corpus of formal articles are added to the search query vector, while high scoring terms that do not share the same neighbors are thrown out. The query vector is then used to run a search of the knowledge base for relevant articles.