Lyrics Retrieval for Tourist Attractions Based on Shared Word-Embedding Vectors.

This paper proposes a lyrics retrieval method based on word-embedding technology to perform music recommendations that matched a tourist attraction. In the proposed method, word importance is calculated by Term Frequency ‒ Inverse Document Frequency (TFIDF) and Smooth Inverse Frequency (SIF). We built a vectorization model from the lyrics corpus using fastText with Continuous Bag of Words (CBOW) and applied this model to both the lyrics corpus and the tourist attraction reviews corpus to create wordembedding vectors for lyrics and tourist attraction reviews. And, the review vectors are integrated for each tourist attraction to generate tourist attraction vectors. Based on the similarity calculation between the tourist attraction vectors and the lyric vectors, the song with the most suitable lyrics for the tourist attraction comes to be the recommended result. Subjective evaluation experiments on the recommendation results of the proposed method was conducted. The results of the experiments showed that the subjects reacted positively to the lyrics of the recommended songs. However, their evaluations of the music of the recommended songs were not as positive as those accorded to the lyrics were. A joint study of acoustic information and text information is an approach to the issue for the future. [ABSTRACT FROM AUTHOR]