# Unsupervised Predominant Sense Detection and Its Application to Text Classification
## 2 Related Work
[PERSON] [31] adopted EMD to measure document similarity because EMD-based measure allowed many-to-many matching between subtopics whereas the existing Optimal Matching (OM) based measure allowed only one-to-one matching.Therefore, evaluating the document similarity with EMD-base measure is more suitable. [PERSON] et al. presented WMD to compute the similarity between two sentences [32]. In the context of graph-based model, a well-known technique is to utilize the analysis the strength of relationship between nodes(vertices) through edges(links) and the direction of relationship (undirected graphs, directed graphs) that can lead to detect the latent information in the graph. Each vertex initially votes other vertices and then applied the ranking algorithm to measure the importance of vertex in the graph. Many authors adopted graph-base model to their works that is, text semantic similarity [33], document summarization [34] WSD [35]. [PERSON] attempted to use the Personalized PageRank algorithm [36] over a graph representing WordNet to disambiguate ambiguous words [37]. They combined sense distribution scores and keyword ranking scores into the graph to personalize the graph for the given domain. The results showed that exploiting domain-specific information within the graph-based methods generated better results than using them was an individual. However, sense distribution scores were based on the frequency of neighbors of the target word from the thesaurus which was difficult to capture the distance between individual words. Recently, [PERSON] [38] proposed a word similarity method based on the semantic structure of BabelNet from a knowledge-based graph. They evaluated on the SemEval-2013 and SemEval-2015 dataset and the results indicated their method performed better than the state-of-art method in the SemEval-2013 dataset. [PERSON] [39] presented Path2 vec which encoded synset paths between graph nodes into dense vectors. Their results were better than graph embedding baselines. [PERSON] et al. presented DeepWalk to learn latent representations of vertices in a network [40]. They used local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. They applied DeepWalk to several multi-label network classification tasks including Flickr and Youtube and showed that it outperforms baseline methods. In the context of text classification, many authors have attempted to apply deep learning techniques including LSTM technique [41; 42; 43; 44], CNN [45], the attention based CNN [46], bag-of-words based CNN [47], Simple Graph Convolution (SGC) [48], the combination of CNN, Gated Recurrent Units (GRU) and attention mechanism [49], and the combination of CNN and recurrent neural network [50] to text classification. In the real world, [PERSON] et al. [51] built a dataset from Brazilian legal documents and tested document classification with different techniques to reduce sorting cases by humans. The F1 score results demonstrate the CNN and the Bidirectional Long Short-Term Memory (BiLSTM) outperform than other techniques. A celebrated technique was proposed by [PERSON] [52] that applied CNN technique which was commonly used in computer vision into sentence classification. He reported simple CNN which was tuned with little hyperparameters that could obtain impressive results on multiple benchmarks. [PERSON] and [PERSON] [53] reported tuning for the number of feature maps and filter region size are the important factors. Furthermore, They proposed grouped weight sharing for external resource such as Brown clusters, WordNet, and so forth, into text classification. They used the two-channel model as input. The first channel was word embedding from external resources and the second channel was weight-sharing embedding among resources. The result showed two-channel perform better than single-channel. Most of them demonstrated that neural network models were powerful for learning features from texts, while they focused on single-label or a few labels problem.