Abstract: A method overfits a word vector generating process to identify implicit relationships between two or more terms in a corpus. A server identifies instances of multiple user-generated pairs of terms in an original corpus of documents, in which the terms are labeled but a relationship between two or more of the corpus terms are not identified. The server then extracts sentences, from the original corpus of documents, that contain one or more of the multiple user-generated pairs of terms, and combines the sentences into a training corpus, which is used to purposely overfit a word embedding model. This word embedding model leads to a vector that is used to identify other terms that have a same type of relationship as that found in the multiple user-generated pairs of terms, such that search corpus of documents can be searched for similar terms that trained the word embedding model.

Abstract: A method overfits a word vector generating process to identify implicit relationships between two or more terms in a corpus. A server identifies instances of multiple user-generated pairs of terms in an original corpus of documents, in which the terms are labeled but a relationship between two or more of the corpus terms are not identified. The server then extracts sentences, from the original corpus of documents, that contain one or more of the multiple user-generated pairs of terms, and combines the sentences into a training corpus, which is used to purposely overfit a word embedding model. This word embedding model leads to a vector that is used to identify other terms that have a same type of relationship as that found in the multiple user-generated pairs of terms, such that search corpus of documents can be searched for similar terms that trained the word embedding model.