# Unsupervised Predominant Sense Detection and Its Application to Text Classification
## 1 Introduction  
The most frequent words generally have many senses than less frequent words [1], so determining a proper sense of the word in a context is a challenging task in Natural Language Processing (NLP). The benefits from disambiguating a sense of a word can utilize in many applications, that is, Machine Translation, Question Answering, and Text Classification. Various techniques are applied to identify the actual sense of a word. The simplified version is to apply the First Sense Heuristic (FSH) that chooses the first or predominant sense of a word. It is often used as a baseline for supervised word sense disambiguation (WSD) systems [2, 3] because it is powerful, especially for words with highly skewed sense distributions [2, 4]. However, the disadvantage of the FSH obtained from the WordNet is a small amount of SemCor corpus which causes data sparseness problems, that is, we cannot apply the FSH to the senses that do not appear in SemCor corpus. Furthermore, the FSH is not based on the domain but instead on the simple frequency counts of SemCor corpus. Consider the noun word, \"party\". There are five noun senses of \"party\" in the WordNet [5]. The first sense of \"party\" is \"an organization to gain political power\", and the usage of it would frequently be found in the domain of \"politics\" more than the \"law\" domain. On the other hand, the fifth sense of \"party\", that is, \"a person involved in legal proceedings\" tends to be used in the \"law\" domain. A technique, predominant sense detection or domain-specific sense identification is an approach for detecting a sense of a word which is a most frequent use in a specific domain.  
In this paper, we focus on domain-specific senses of nouns and verbs and propose a method for detecting predominant sense in each domain/category. Our model employs distributed representations of words learned by using Word2 Vec and thus does not require manual annotation of sense-tagged data. We apply a simple Markov Random Walk (MRW) model to rank senses for each domain. The sense similarity scores which is used to decide the importance of senses is obtained by using distributed representations of, WordNet. To examine the effectiveness of our detection method, we apply the results to text classification on the dataset collected from the Reuters corpus. The result obtained by the Convolutional Neural Network (CNN) shows that our model gains great improvement over the result without assigning the Domain-Specific Senses (DSS). Moreover, we compared our DSS model with one of the word sense disambiguation techniques, Context2 vec [6] in the text classification task. The results demonstrate the DSS approach gains a great improvement over the WSD approach.  
The main contributions of our work can be summarized--(1) we propose a method for identifying domain-specific noun senses which leverage distributed representations of words and thus do not require manual annotation of sense-tagged data, while some of the existing work required a considerable amount of hand-labeling data. (2) From the perspective of robustness, the method is automated and required only documents from the given domains/categories such as the Reuters corpus, and thesaurus with gloss texts such as WordNet. The method is easily applicable to a new domain or sense inventory, given sufficient documents. (3) We empirically evaluate our model and show that the result of domain-specific senses is effective than WSD in the text classification task.