Towards Semantic Query Segmentation
Table 1. Segmentation and Query accuracies on AOL queries
['Method', 'Segmentation Accuracy', 'Query Accuracy']
We do a 60-20-20 train-val-test split on the 50k queries. To train the query embeddings, we remove the test set from the 36M AOL queries. Since queries are short texts we use a smaller context window (2-3). Our experiments show this smaller context window learns better embeddings for queries especially for the segmentation task. We set the dimension to 300, which leads to a 600 dimensional vector when we concatenate the 2 vectors. This 600 dimensional vector becomes the input feature vector to the classifier. Once the embeddings model is built, we build a classifier on the training set and tune the parameters using validation set. We tried multiple classifiers but Logistic Regression and XGBoost We found higher lift in the segmentation accuracy when we use GBMs. We need to note that the authors propose an improved model with boosting segments using Wikipedia Titles but we dont use it to keep the models agnostic of external data augmentation. We plan to explore that as part of our future work. Google’s word2vec These pretrained embeddings were built on data set which has about 100 Billion words. It has has around 3 million words and phrases, each having 300 dimensional vector.