# Unsupervised Predominant Sense Detection and Its Application to Text Classification
## 2 Related Work  
Semantic-oriented applications such as Question Answering and Machine Translation systems need not only fine-grained and large-scale semantic knowledge but also tune the sense of the word heuristic depending on the domain in which the word is used. [PERSON] et al. presented a lexical resource where WordNet 2.0 synsets were annotated with Subject Field Codes (SFC) [7; 8]. They annotated 96% of WordNet synsets of the noun hierarchy, while mapping domain labels for word senses were semi-automated and required hand-labeling. Several authors have attempted to use different techniques to solve with the WSD technique [9; 10; 11; 12; 13; 14; 15]. Some of them addressed the problem and have attempted to use specific domain knowledge and showed that it improves WSD performance compared with generic supervised WSD [16; 17; 18]. [PERSON] et al. [19] proposed the D-bee method which assumed hive as target word is chosen at random and the number of bees was determined from the number of word sense of the target word. Each bee carried one sense and then move to find the random senses of the next word to calculate the similarity value, the bee with the maximum similarity value is used for identifying the proper senses in a context. [PERSON] et al. [20] proposed an unsupervised WSD approach for lexical sample task with two corpora containing test corpus which had ambiguous words and their instances were assigned by hand-labeling. The second was auxiliary corpus which was generated from web pages to obtain more contexts and their instances are unlabeled. They reported that the results by using two domain-specific corpora(finance, sports) had better performance compared with general-domain corpus (British National Corpus [BNC]), and even better than FS heuristics. [PERSON] et al. proposed an automated method for assigning predominant noun senses [21]. They found words with a similar distribution to the target word from parsed data. The motivation for their work is similar to ours, that is, to capture predominant senses in different domains by ranking senses among documents. They tested 38 words containing two domains of Sports and Finance from the Reuters corpus [22], while we tested 14 domains with 4488 senses in all. [PERSON] et al. [23] also proposed an approach to acquired Identifying Domain-Specific Senses (IDSS) and applied to text classification. However, they only focused on nouns. Moreover, their method is based on the traditional Support Vector Machines (SVMs), that is, the selection of the associated senses and text classification are conducted by using SVMs. In the context of similarity metric, there have been many attempts which project documents into implicit semantic space, for example, Latent Semantic Indexing (LSI) [24] and Latent Dirichlet Allocation (LDA) [25]. [PERSON] et al. presented Word2 Vec model that is a well known shallow model for training text and generates word embedding [26]. [PERSON] and [PERSON] developed Doc2 Vec [27] by adding the paragraph vector that is a numeric representation of a document based on Word2 Vec. They proposed two different models including the Distributed Model of Paragraph Vectors(PV-DM) and the Distributed Bag of Words version of Paragraph Vector (PV-DBOW) which are similar to Continuous Bag of Words (CBOW) and Skip-gram, respectively. [PERSON] et al. proposed unsupervised learning named Skip-thoughts model for learning vector representations of sentences with the ordering of sentences for training model [28]. Skip-thoughts model has three components consisting encoder network, previous decoder network, next decoder network. [PERSON] et al. proposed Sent2 Vec which builds document embeddings by averaging the word embeddings [29]. Another attempt is Earth Mover's Distance (EMD) proposed by [PERSON] [30] that is one of the solvers for a transportation problem to find out the shortest distance from one distribution to another distribution. The result showed similarity of image retrieval effectively.