Information processing apparatus, information processing method, and information processing program

An information processing method executed by a processor of an information processing apparatus, the method comprising:

• extracting a phrase corresponding to a named entity included in an input sentence used for training a machine-learning-type natural language processing model from data in which a phrase and a classification name of a named entity are associated with each other, wherein the machine-learning-type natural language processing model is pre-trained using bidirectional encoder representations from transformers (BERT) or a robustly optimized BERT approach (RoBERTa) technology;
• adding the extracted phrase to the classification name corresponding to the extracted phrase among a plurality of different classification names which are set in advance;
• deriving a similarity between a distributed representation of each word included in the input sentence and a distributed representation of each of the plurality of classification names to which the phrase is added;
• and
• selecting a classification name of each word included in the input sentence from among the plurality of classification names based on the derived similarity.

A non-transitory computer-readable storage medium storing an information processing program causing a processor of an information processing apparatus to execute a process comprising:

• extracting a phrase corresponding to a named entity included in an input sentence used for training a machine-learning-type natural language processing model from data in which a phrase and a classification name of a named entity are associated with each other, wherein the machine-learning-type natural language processing model is pre-trained using bidirectional encoder representations from transformers (BERT) or a robustly optimized BERT approach (RoBERTa) technology;
• adding the extracted phrase to the classification name corresponding to the extracted phrase among a plurality of different classification names which are set in advance;
• deriving a similarity between a distributed representation of each word included in the input sentence and a distributed representation of each of the plurality of classification names to which the phrase is added;
• and
• selecting a classification name of each word included in the input sentence from among the plurality of classification names based on the derived similarity.