1. A processor-implemented method of lemmatizing a phrase for a specific category, the processor-implemented method comprising: receiving, by a processor, a string of binary data that represents an initial phrase, wherein the initial phrase is an initial version of the phrase, wherein the phrase comprises multiple words, and wherein the phrase is associated with a specific category; removing one or more letters from an end of a word in the initial phrase to form an initial truncated version of the phrase; running, by the processor, a term frequency-inverse document frequency (TF-IDF) algorithm on the initial truncated version of the phrase; the processor lemmatizing subsequent truncated versions of the initial phrase by recursively removing a remaining said one or more letters from the end of the word in a subsequent truncated version of the initial truncated version of the initial phrase; the processor running the TF-IDF algorithm on subsequent truncated versions of the initial truncated version of the initial phrase until a highest TF-IDF value is identified for a specific truncated version of the initial phrase when compared to TF-IDF values of other truncated versions of the initial phrase; assigning the specific truncated version of the initial phrase that is associated with the highest TF-IDF value to the specific category; in response to receiving a request for the phrase within the specific category, returning the specific truncated version of the initial phrase that is associated with the highest TF-IDF value for said specific category; and using the specific truncated version of the initial phrase that is associated with the highest TF-IDF value for said specific category to search a database that is dedicated to the specific category.
1. A processor-implemented method of lemmatizing a phrase for a specific category, the processor-implemented method comprising: receiving, by a processor, a string of binary data that represents an initial phrase, wherein the initial phrase is an initial version of the phrase, wherein the phrase comprises multiple words, and wherein the phrase is associated with a specific category; removing one or more letters from an end of a word in the initial phrase to form an initial truncated version of the phrase; running, by the processor, a term frequency-inverse document frequency (TF-IDF) algorithm on the initial truncated version of the phrase; the processor lemmatizing subsequent truncated versions of the initial phrase by recursively removing a remaining said one or more letters from the end of the word in a subsequent truncated version of the initial truncated version of the initial phrase; the processor running the TF-IDF algorithm on subsequent truncated versions of the initial truncated version of the initial phrase until a highest TF-IDF value is identified for a specific truncated version of the initial phrase when compared to TF-IDF values of other truncated versions of the initial phrase; assigning the specific truncated version of the initial phrase that is associated with the highest TF-IDF value to the specific category; in response to receiving a request for the phrase within the specific category, returning the specific truncated version of the initial phrase that is associated with the highest TF-IDF value for said specific category; and using the specific truncated version of the initial phrase that is associated with the highest TF-IDF value for said specific category to search a database that is dedicated to the specific category. 3. The processor-implemented method of claim 1 , wherein the specific truncated version of the initial phrase is a lemma for a lexeme, and wherein the specific category defines a breadth of the lemma.