# Unsupervised Predominant Sense Detection and Its Application to Text Classification
## 3 Acquisition of Domain-Specific Senses  
The first sense heuristic is very powerful and often uses as a baseline of sense disambiguation systems. However, the major problems to use it as a sense disambiguation heuristic [1] contain the predominant sense of a word varies according to the source of the document belonging to the domain and the other problem is obtaining predominant senses, they need manual annotation of the corpus which causes a relatively small amount of resources such as SemCor. A methodology to find domain-specific senses without requiring manual annotation of data is needed. [PERSON], and [PERSON] [60] proposed a method that determined domain from context using LDOCE (the Longman Dictionary Of Contemporary English) with counting a frequency of subject code of each word in a context which subject code had the highest count is assigned to the domain of context. [PERSON] et al. [61] proposed one sense in the context and they reported shared sense in the same context is 98%. [PERSON] et al. [7] proposed WordNet Domains that was a lexical resource and they labeled 96% coverage of noun hierarchy, however, the domain annotation on word sense is still manually. The goal of our model is to identify predominant sense distributions of a word depending on the domain. Figure 1 illustrates our Framework. Our approach has three stages: pre-processing, measuring senses similarity and ranking senses. Pre-processing stage is to create senses list for each category. We first collect the documents of each category in the Reuters corpus in XML document format, often used as input for text processing. Each document is clearly structured including a category as stated in the automated processing of documents [62]. We extract the word that has frequency more than five and then apply with Part-Of-Speech (POS) tagger to obtain nouns and verbs list. We then use them as query to retrieve their senses with gloss texts in the WordNet. For Measuring senses similarity stage, we create senses embedding from gloss texts and calculate sense similarity with WMD. The last stage is ranking senses. We applied a simple graph centrality algorithm, Markov Random Walk (MRW) to the extracted noun words from the documents assigned to a specific domain/category and identify domain-specific senses for the domain. The idea of the MRW model is that of voting. When one vertex links to another one, it is casting a vote for that other vertex. The ranking is conducted by two metrics. One is a metric that the larger the number of votes that are cast for a vertex, the higher the importance of the vertex. Another is a metric that how important the vote itself is. We applied the algorithm to detect the domain-specific-sense of words. A graph denotes \(G_{d}=(V,\,E)\). It demonstrates the relationships between senses in a domain and it includes \(V\) is the set of vertices, and each vertex \(v_{i}\) in \(V\) is the gloss text from WordNet. \(E\) is a set of edges, which is a subset of \(V\times V\). Each edge \(e_{ij}\) in \(E\) is associated with an affinity weight \(f(i\to j)\) between senses \(v_{i}\) and \(v_{j}\) (\(i\
eq j\)). The standard cosine measure is used for calculating for the weight between senses. Two vertices are connected if their affinity weight is larger than 0 ans we let \(f(i\to i)=0\) to avoid self transition. The transition probability from \(v_{i}\) to \(v_{j}\) is then defined by  
\[p(i\to j) = \begin{cases}\frac{f(i\to j)}{\sum_{k=1}^{|V_{i}|}f(i\to k)},&if \ \Sigma f\
eq 0\\ 0,&otherwise.\end{cases}  \tag{1}\]  
We used the row-normalized matrix \(U_{ij}=(U_{ij})_{|V|\times|V|}\) to describe \(G\) with each entry corresponding to the transition probability, where \(U_{ij}=p(i\to j)\). To make \(U\) a stochastic matrix, the rows with all zero elements are replaced by a smoothing vector with all elements set to \(\frac{1}{|V|}\). The matrix form of the saliency score \(Score(v_{i})\) can be formulated in a recursive form as in the MRW model.