You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: T his paper describes experimental research investigating the genre classification utility of combining features extracted from lyrical, audio, symbolic and cultural sources of musical information. It was found that cultural features consisting of information extracted from both web searches and mined listener tags were particularly effective, with the result that classification accuracies were achieved that compare favorably with the current state of the art of musical genre classification. It was also found that features extracted from lyrics were less effective than the other feature types. Finally, it was found that, with some exceptions, combining feature types does improve classification performance. The new lyricFetcher and jLyrics software are also presented as tools that can be used as a framework for developing more effective classification methodologies based on lyrics in the future.

[2]: Musical genre and mood classification techniques combining lyric and audio features for Music Information Retrieval (MIR) have been studied widely in recent years. This paper investigates the performances of musical genre and mood classification using only lyric features. In this preliminary study, the Part-of-Speech (POS) feature is utilizes for classification of a collection of 600 songs. Ten musical genre and mood categories were selected respectively based on a summary from the literature. Experiments show that classification accuracies for mood categories outperform genres.

[3]: 

[4]: We proposed a method to classify songs in the Million Song Dataset according to song genre. Since songs have several data types, we trained sub-classifiers by different types of data. These sub-classifiers are combined using both classifier authority and classification confidence for a particular instance. In the experiments, the combined classifier surpasses all of these sub-classifiers and the SVM classifier using concatenated vectors from all data types. Finally, the genre labels for the Million Song Dataset are provided.

[5]: How individuals perceive music is influenced by many different factors. The audible part of a piece of music, its sound, does for sure contribute, but is only one aspect to be taken into account. Cultural information influences how we experience music, as does the songs’ text and its sound. Next to symbolic and audio based music information retrieval, which focus on the sound of music, song lyrics, may thus be used to improve classification or similarity ranking of music. Song lyrics exhibit specific properties different from traditional text documents – many lyrics are for example composed in rhyming verses, and may have different frequencies for certain parts-of-speech when compared to other text documents. Further, lyrics may use ‘slang’ language or differ greatly in the length and complexity of the language used, which can be measured by some statistical features such as word verse length, and the amount of repetative text. In this paper, we present a novel set of features developed for textual analysis of song lyrics, and combine them with and compare them to classical bag-of-words indexing approaches. We present results for musical genre classification on a test collection in order to demonstrate our analysis.

[6]: Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, "powerful," "strong" and "Paris" are equally distant. In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models. Empirical results show that Paragraph Vectors outperforms bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks.

[7]: We explore the use of song lyrics for automatic indexing of music. Using lyrics mined from the Web, we apply a standard text processing technique to characterize their semantic content. We then determine artist similarity in this space. We found lyrics can be used to discover natural genre clusters. Experiments on a publicly available set of 399 artists showed that determining artist similarity using lyrics is better than random, but inferior to a state-of-the-art acoustic similarity technique. However the approaches made different errors, suggesting they could be profitably combined

[8]: The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.
Existing works that have used lyrics for genre and popularity prediction can be partitioned into two categories. First, that use lyrics in augmentation with acoustic features of the song [1] [4] and second, that do not use acoustic features [7] [5] [2] [3] . However, all of them represent lyrics using either handcrafted features or bag-of-words models. Identifying features manually requires intricate knowledge of music, and such features vary with the underlying dataset. Mikolov and Le have shown that distributed representation of words and documents is superior to bag-of-words models [8] [6] . To the best of our knowledge, this is the first work that capitalizes on such representation of lyrics for genre and popularity prediction. However, our results cannot be directly compared with existing works as datasets, set of genres, the definition of popularity, and distribution of target classes are not identical. Still, our results stand in contrast with existing works that have concluded that lyrics alone are a weak indicator of genre and popularity. These works report significantly low performance of lyrics for genre prediction task. For example, report an accuracy of 34