The central idea of this paper is to demonstrate the strength of lyrics for
music mining and natural language processing (NLP) tasks using the distributed
representation paradigm. For music mining, we address two prediction tasks for
songs: genre and popularity. Existing works for both these problems have two
major bottlenecks. First, they represent lyrics using handcrafted features that
require intricate knowledge of language and music. Second, they consider lyrics
as a weak indicator of genre and popularity. We overcome both the bottlenecks
by representing lyrics using distributed representation. In our work, genre
identification is a multi-class classification task whereas popularity
prediction is a binary classification task. We achieve an F1 score of around
0.6 for both the tasks using only lyrics. Distributed representation of words
is now heavily used for various NLP algorithms. We show that lyrics can be used
to improve the quality of this representation.

该论文表明了利用分布式表示范式进行音乐挖掘和自然语言处理任务时歌词的优势，并通过使用分布式表示克服了手工特征和歌词被认为是流派和流行度的弱指标两个瓶颈。