# Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge
## 4 Experiments
### Baselines  
To measure the improvement of our model, we compare it with multiple traditional and state-of-the-art methods below.  
**BoW+TFIDF--**BoW is a traditional text representation method widely used in natural language processing, the terms in the text can be regarded as a bag of word, and the term frequency in the dataset is used as weight. In this method, we use TF-IDF instead of term frequency as weight.  
**CNN--**CNN is a classical neural network model for classification task. [PERSON] [3] first introduced CNN to text classification. Only a word embedding layer is used in this network and we use the same parameter settings as our proposed model.  
**LSTM--**LSTM [30] is a variant of Recurrent Neural Network. LSTM can capture the long-term dependency among words in short texts. Only a word embedding layer is used in this network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Training** & **Testing** & **Features** & **Value** \\ \hline Business & 1200 & 300 & Avg.Len per document & 12 \\ Computers & 1200 & 300 & Total entity & 1,494,181 \\ Cul-Arts-Ent. & 1880 & 330 & Total document & 12,240 \\ Edu-Sci & 2360 & 300 & & \\ Engineering & 220 & 150 & & \\ Health & 880 & 300 & & \\ Politics-Society & 1200 & 300 & & \\ Sports & 1120 & 200 & & \\ \hline Total & 10,060 & 2180 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Google Snippet data set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Training** & **Testing** & **Features** & **Values** \\ \hline Politics & 2241 & 959 & Avg.Len per document & 18 \\ Sports & 2326 & 999 & Total entity & 1,586,965 \\  & & & Total document & 6,525 \\ \hline Total & 4567 & 1958 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Twitter data set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Training** & **Testing** & **Features** & **Value** \\ \hline World & 30,000 & 1900 & Avg.Len per document & 7 \\ Sport & 30,000 & 1900 & Total entity & 2,270,042 \\ Business & 30,000 & 1900 & Total document & 127,600 \\ Sci/Tec & 30,000 & 1900 & & \\ \hline Total & 120,000 & 7600 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The AG news data set.  
**Bi-LSTM--**It is a bidirectional LSTM [31], which learns bidirectional long-term dependencies between time steps of sequence data. Only a word embedding layer is used in this network.  
**GRU--**Gate Recurrent Unit (GRU) [32] is similar with LSTM but has fewer parameters than LSTM. Only a word embedding layer is used in this network.  
**Attention--**Attention [4] is a mechanism widely used in NLP. Here, we use self-attention in our experiment. Only a word embedding layer is used in this network.  
**KBSTC--**This method is proposed by [PERSON] et al [18], and it utilizes the entity and knowledge base (Wikipedia) for the short text classification.  
**WCCNN--**This method is proposed by [PERSON] et al [7]. It utilizes word embedding and concept embedding for the short text classification. We re-implement their code for evaluation on the Twitter and Google snippet data sets.