Supervised training for word embedding

A system for training word embedding of domain-specific words, comprising:

• a graphical user interface configured to enable a user to input one or more terms;
• a processor, configured to:
• train a first word embedding, using unsupervised data including a general domain corpus, on the one or more terms inputted by a user;
• retrain the first word embedding, using a specific domain corpus, for a Neuro-Linguistic Programming task, to create a tuned word embedding, wherein the retraining of the first word embedding includes incorporating a list of terminologies from the domain specific corpus, and wherein the retraining of the first word embedding includes determining the list of terminologies by:
• calculating a term frequency-inverse document frequency for each term in the general domain corpus;
• calculating a term frequency-inverse document frequency for each term in the specific domain corpus;
• and
• selecting terms in which a ratio of the term frequency-inverse document frequency for each word in the general corpus to the term frequency-inverse document frequency for each word in the specific domain corpus is larger than a predefined threshold, wherein the retraining of the first word embedding corrects for inappropriate results due to a high degree of cosine similarity between multiple words of phrases in the first word embedding;
• train a Neural Network for the Neuro-Linguistic Programming task, using the specific domain corpus;
• and
• incorporate the trained Neural Network and tuned word embedding into a Neural Network-based Neuro-Linguistic Programming task;
• and
• a memory configured to store the one or more terms and the tuned word embedding,
• wherein the processor retrains the first word embedding and trains the Neural Network together, and wherein the tuned word embedding is accelerated due to a change in a hyper parameter for domain-specific words.

A non-transitory computer-readable storage medium including a computer-readable program for training word embedding of domain-specific words, wherein the computer-readable program when executed on a computer causes the computer to perform a method comprising the steps of:

• training, by a processor, a first word embedding, using unsupervised data including a general domain corpus, on one or more terms inputted by a user;
• retraining, by the processor, the first word embedding, using a specific domain corpus, for a Neuro-Linguistic Programming task, to create a tuned word embedding, wherein the retraining of the first word embedding includes incorporating a list of terminologies from the domain specific corpus, and wherein the retraining of the first word embedding includes determining the list of terminologies by:
• calculating a term frequency-inverse document frequency for each term in the general domain corpus;
• calculating a term frequency-inverse document frequency for each term in the specific domain corpus;
• and
• selecting terms in which a ratio of the term frequency-inverse document frequency for each word in the general corpus to the term frequency-inverse document frequency for each word in the specific domain corpus is larger than a predefined threshold, wherein the retraining of the first word embedding corrects for inappropriate results due to a high degree of cosine similarity between multiple words of phrases in the first word embedding;
• training, by the processor, a Neural Network for the Neuro-Linguistic Programming task, using the specific domain corpus;
• and
• incorporating, by the processor, the trained Neural Network and tuned word embedding into a Neural Network-based Neuro-Linguistic Programming task,
• wherein the retraining of the first word embedding and the training of the Neural Network are performed together, and wherein the tuned word embedding is accelerated due to a change in a hyper parameter for domain-specific words.