Simplify the Usage of Lexicon in Chinese NER
Table 2: F1-score of our method with different implementations of vs. MP denotes the mean-pooling algorithm depicted in Eq. (13), WP denotes the frequency weighted pooling algorithm depicted in Eq. (14), and SWP denotes the smoothed weighted pooling algorithm depicted in Eq. (15).
['Dataset', 'MP', 'WP', 'SWP']
This also means that we should map each word set into a fixed-dimensional vector. To achieve this purpose, we first tried the mean-pooling algorithm to get the vector representation of a word set S: vs(S)=1|S|∑w∈Sew(w). (13) Here, ew denotes the word embedding lookup table. Through the comparison with Lattice-LSTM, we find out that in Lattice-LSTM, it applies a dynamic attention algorithm to weigh each matched word related to a single character. Motivated by this practice, we propose to weighing the representation of each word in the word set to get the pooling representation of the word set. However, considering the computational efficiency, we do not want to apply a dynamical weighing algorithm, like attention, to get the weight of each word. With this in mind, we propose to using the frequency of the word as an indication of its weight. The basic idea beneath this algorithm is that the more times a character sequence occurs in the data, the more likely it is a word. Note that, the frequency of a word is a static value and can be obtained offline. This can greatly accelerate the calculation of the weight of each word (e.g., using a lookup table). From the table, we can see that the weighted pooling algorithm performs generally better than the other two implementations. Of course, we may obtain better results with the smoothed weighted pooling algorithm by reducing the value of c (when c=0, it is equivalent to the weighted pooling algorithm). We did not do so for two reasons. The first one is to guarantee the generality of our system for unexplored tasks. The second one is that the performance of the weighted pooling algorithm is good enough compared with other state-of-the-art baselines. Therefore, in the following experiments, we in default applied the weighted pooling algorithm to implement vs.