System and method for natural language processing

A method for processing natural language, the method comprising:

• receiving at least one data object;
• identifying, for a first aspect of the at least one data object that includes free form textual information, text strings of the free form textual information having a first text string type;
• in response to identifying at least one text string of the free form textual information having the first text string type, generating updated free form textual information by removing the at least one text string having the first text string type;
• generating one or more feature vectors based on the updated free form textual information using at least one of a unigram, a bigram, and a trigram;
• using an artificial intelligence engine that uses at least one machine learning model configured to provide, using the one or more feature vectors, an output that includes at least one prediction indicating at least one resource domain and a weight value indicating a probability that the at least one resource domain corresponds to the free form textual information;
• and
• providing, at a display, the output.

An apparatus for processing natural language comprising:

• a processor;
• and
• a memory including instructions that, when executed by the processor, cause the processor to:
• receive at least one data object having an aspect that includes free form textual information corresponding to a project description;
• identify text strings of the free form textual information having a first text string type;
• in response to identifying at least one text string of the free form textual information having the first text string type, generate updated free form textual information by removing the at least one text string having the first text string type;
• generate a plurality of feature vectors using the updated free form textual information, wherein each feature vector of the plurality of feature vectors includes at least one of a unigram, a bigram, and a trigram;
• use an artificial intelligence engine that uses at least one machine learning model configured to provide, using the plurality of feature vectors, an output that includes at least one prediction indicating at least one resource domain and a weight value indicating a probability that the at least one resource domain corresponds to the at least one data object, wherein the machine learning model is initially trained using a supervised learning technique and iteratively trained using at least the output of the at least one machine learning model;
• and
• provide, at a display, the output.