AI for Low-frequency Data
Nowadays, with the growth of Natural language processing (NLP) and related fields, computer programs can translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. Natural language processing (NLP) refers to the branch of Artificial Intelligence (AI) concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. There are many applications of NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. NLP also plays a growing role in enterprise solutions, e-commerce, entertainment, health, education and many other fields. In our lab, works focus on new NLP methods that can be used to improve users’ experience.
One research in this category is the classification of reviews focusing on the components of a product. Product reviews on e-commerce websites are used by users for the purpose of efficiently gathering information of interest. However, product reviews are often not broken down by the parts or aspects mentioned, which makes information gathering difficult. This research focus on the assistance in review browsing by classifying and extracting reviews by the parts mentioned and their aspects.
Another research is related to lyricist classification using BERT. Lyrics are considered to be influenced by components such as singers, lyricists and themes. Lyric analysis in this research aims to extract features contained in lyrics. In this work, lyrics are analyzed by evaluating the results of classifying the dataset considering the relationship between lyricist and singer with a lyricist classifier based on BERT.