# Unsupervised Predominant Sense Detection and Its Application to Text Classification
## 5 Experiments
### Acquisition of Domain-Specific Senses  
The Reuters corpus consists of 806,791 documents organized into 126 categories. There are no existing sense-tagged data for domains that we can utilize for evaluation. Therefore, we used the Subject Field Codes (SFC) resource which semi-automatically annotates WordNet 2.0 synsets with domain labels [7]. The SFC consists of 115,424 words assigning 168 domain labels which include some of the Reuter's categories. We manually assigned Reuter's categories to SFC labels which are shown in Table 1. \"# doc\" in Table 1 refers to the number of documents in each category. \"# min\" and \"# max\" indicate the minimum and the maximum number of word frequencies that appear in each category respectively. In a pre-processing step, a POS tagger and lemmatizer from Stanford CoreNLP [66] were applied to the Reuters corpus and then we divided it into three for the experiments including training dataset for 3 months, test dataset for 6 months, and validation dataset for 3 months. The test dataset was fed in DSS. The initial step was to choose the first 20,000 words with the highest frequency from each category. Every word was converted to lowercase, removed punctuation, \begin{table}
\begin{tabular}{l l r r r} \hline \hline
**SFC** & **Reuters** & **\# doc** & **\# min** & **\# max** \\ \hline Law & Legal & 11,944 & 140 & 18,781 \\ Finance & Funding & 41,829 & 151 & 63,973 \\ Industry & Production & 25,403 & 197 & 21,957 \\ Publishing & Advertising & 2084 & 27 & 5821 \\ Admin. & Management & 11,354 & 56 & 18,587 \\ Economy & Economics & 117,539 & 1139 & 99,197 \\ Art & Arts & 3801 & 67 & 4562 \\ Fashion & Fashion & 313 & 7 & 775 \\ Politics & Politics & 56,878 & 967 & 94,970 \\ Religion & Religion & 2,849 & 54 & 3746 \\ Sports & Sports & 35,317 & 222 & 26,898 \\ Tourism & Travel & 680 & 8 & 1423 \\ Military & War & 32,615 & 546 & 45,085 \\ Meteorology & Weather & 3878 & 17 & 4602 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Reuters and Subject Field Codes (SFC) category correspondences. and stopwords, and applied lemmatization. Next, we selected only nouns and verbs then used them as a query into Wordnet to find out the word senses and their glosses for built the Word2 vec model with their glosses. The 14 different models were created according to category. The Word2 vec parameters consisted of the number of dimensions to 100, the window size to 5, Skip-gram as the training algorithm, and Hierarchical softmax as the model training. We used the word embedding from the Word2 vec model to measure sense similarity score for WMD which was a method for comparing dissimilarity score between sentences and then we rated the score with MRW which was used to determine the importance of senses. Eventually, the predominant senses per each category is obtained. The training data was used to estimate \(K\)% words (senses) according to rank score, and test data was used to test the method using the estimated value \(K\). We manually evaluated a sense-domain list. As a result, we set \(K\) to 10%. Table 2 shows the results obtained by the topmost 10% senses according to rank score. \"Sense\" indicates the total number of senses which should be assigned to each category. \"DSS\" and \"SFC\" refer to the number of senses obtained by our method and appeared in the SFC resource, respectively. \"Correct\" shows the number of senses tagged with the best domain ranking appearing in both of our method and SFC. \"F-score\" indicates F-measure of one domain tagging. \"IRS\" refers to Inverse Rank Score and the higher the IRS value, the better the system performance. \"IRS\" refer to IRS of one domain tagging. \"P_IRS\" indicates the perfect correct value of IRS. We can see from Table 2 that the overall performance depends on the categories. The best performance of one domain is \"Finance\". In contrast, the results of \"Politics\" and \"Administrator(Admin. )\" are 0.462\(\sim\)0.533. Table 3 illustrates some examples obtained by our method but that does not appear in the SFC.