# DAWE: A Double Attention-Based Word Embedding Model with Sememe Structure Information
## 6 Discussions  
### Case Study  
To illustrate the dynamic semantic generation of our models, we select some specific cases for analysis. Tables 5-7 lists the relative weights of the different senses of the word \"Apple\" (Sense 1: \"Apple Brand\" (Sememe: \"computer\", \"PatternValue\", \"able\", \"bring\" and \"SpeBrand (specific brand)\"); Sense 2: \"Apple\" (Sememe: \"fruit\"); Sense 3: \"Apple Tree\" (Sememe: \"fruit\", \"reproduce\" and \"tree\")) in a specific context and the relative weights of different sememes within the senses. Those weights are calculated by sense-level attention and sememe-level attention of DAT. Tables 5-7 show that: (1) Our model correctly distinguishes the different senses of \"Apple\" from different contexts. This shows the power of our model in word sense disambiguation (WSD). (2) In the sense \"Apple Brand\", the sememe \"SpeBrand\" gets a large weight. This is consistent with our description in the Introduction. In the process of sense construction, the distribution of weights between sememes should be unequal. (3) When the meanings of \"Apple\" changing with different contexts (the meaning of \"Apple\" changes when the sentence changes), both the sense items of the word and the sememe items in each sense of the word do not change, what changes with the context are the weights of those senses and the weights of those sememes. The model of this paper is trained on the large text corpus Clean-SogouT1, and the learned word vectors and model parameters are consistent with the feature distribution of the entire corpus. As a result, the sense representation inside the words will tend to be stable, that is, the weight distribution of sememes inside the senses will also be stable (sememe consists of sense, sense and then word). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Senses** & \multicolumn{5}{c}{**Sememes**} \\ \hline Apple Brand & bring & PatternValue & SpeBrand & computer & able \\
0.00 & 4.22 & 0.00 & 6.94 & 1.20 & 4.55 \\ Apple & & & fruit & & \\
3.06 & & & 0.00 & & \\ Apple Tree & tree & fruit & reproduce & \\
0.08 & & 14.55 & 20.18 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \"I just hit you with an apple (**Apple**) core.\" \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Senses** & \multicolumn{5}{c}{**Sememes**} \\ \hline Apple Brand & bring & PatternValue & SpeBrand & computer & able \\
1.91 & 5.15 & 0.00 & 6.77 & 0.31 & 8.06 \\ Apple & & & fruit & & \\
0.86 & & & 0.00 & & \\ Apple Tree & tree & fruit & reproduce & \\
0.00 & & 19.93 & 21.28 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: “You can like apple (**Apple Brand**) computers, just don’t vilify other brands.”In the above cases, we take the word \"Apple (Apple/Apple Brand/Apple Tree)\" as an example to examine the weight distribution of sememes and verify the effectiveness of our model in WSD. We take the word \"Notebook (Notebook/ Laptop Computer)\" as an example to study the impact of sememe's weight distribution in a specific context. As shown in Table 8, when the meaning of word \"Notebook\" in the context tends to the sense \"Laptop Computer\", we observe the following:  
(1) When the word \"Notebook\" and the word \"Computer\" appear together, that is, \"Notebook Computer\", the weight of the sememe \"computer\" is the lowest among all the sememes of the sense \"Laptop Computer\". It can be explained that, when \"Notebook Computer\" appear together, \"Notebook\" is mainly used as a modifier of \"Computer\" to indicate that \"Computer\" is light, thin and portable. Therefore, \"Notebook\" will have less \"computer\" meaning. (2) When \"Notebook\" appears alone, sememe \"computer\" has more weight than when \"Notebook Computer\" appear together. At this point, \"Notebook\" no longer appears as a modifier of \"Computer\" but as a separate entity, thus it should cover the semantics that tends to favor \"computer\".