Question: Where are the used dataset and benchmarks from?
Evidence: Author profiling is the characterization of an author through some key attributes such as gender, age, and language. It's an indispensable task especially in security, forensics, and marketing. Recently, social media has become a great data source for the potential learning approaches. Furthermore, gender prediction has been a popular profiling task.
The traditional approach to gender prediction problem is extracting a useful set of hand-crafted features and then feeding them into a standard classification algorithm. In their study, BIBREF0 work with the style-based features of message length, stop word usage, frequency of smiley etc. and use different classifiers such as k-nearest neighbor, naive bayes, covering rules, and backpropagation to predict gender on chat messages. Similarly, BIBREF1 select some hand-crafted features and feed them into various classifiers.
Most of the work on gender prediction rely on n-gram features BIBREF2. BIBREF3 give Latent Semantic Analysis (LSA)-reduced forms of word and character n-grams into Support Vector Machine (SVM) and achieve state-of-the-art performance. Apart from exploiting n-gram frequencies, there are studies BIBREF4, BIBREF5, BIBREF6 to extract cross-lingual features to determine gender from tweets. Some other work BIBREF4, BIBREF7 exploit user metadata besides using just tweets.
Recently, neural network-based models have been proposed to solve this problem. Rather than explicitly extracting features, the aim is to develop an architecture that implicitly learns. In author profiling, both style and content-based features were proved useful BIBREF8 and neural networks are able to capture both syntactic and semantic regularities. In general, syntactic information is drawn from the local context. On the other hand, semantic information is often captured with larger window sizes. Thus, CNNs are preferred to obtain style-based features while RNNs are the methods of choice for addressing content-based features BIBREF9. In literature, CNN BIBREF10 or RNN BIBREF11, BIBREF12, BIBREF13 is used on this task. BIBREF11 obtain state-of-the-art performance among neural methods by proposing a model architecture where they process text through RNN with GRU cells. Also, the presence of an attention layer is shown to boost the performance of neural methods BIBREF11, BIBREF10.
In this work, we propose a model that relies on RNN with attention mechanism (RNNwA). A bidirectional RNN with attention mechanism both on word level and tweet level is trained with word embeddings. The final representation of the user is fed to a fully connected layer for prediction. Since combining some hand-crafted features with a learned linear layer has shown to perform well in complex tasks like Semantic Role Labeling (SRL) BIBREF14, an improved version of the model (RNNwA + n-gram) is also tested with hand-crafted features. In the improved version, LSA-reduced n-gram features are concatenated with the neural representation of the user. Then the result is fed into a fully-connected layer to make prediction. Models are tested in three languages; English, Spanish, and Arabic, and the improved version achieves state-of-the-art accuracy on English, and competitive results on Spanish and Arabic corpus.
There are many datasets created for this task BIBREF15, BIBREF16. In this work, we have used the dataset and benchmarks provided by the PAN 2018 shared task on author profiling BIBREF15. As the dataset contains a constant number of 100 tweets per user, accuracy tests are performed both on user and tweet level (tweet-level predictions are made by removing the user-level attention). Tweet-level accuracy tests show interesting results during hyperparameter optimization. When the tweet-level predictions are averaged to produce user-level predictions, it is seen that the hyperparameters that gave the best results in terms of tweet-level accuracy, performs worse in user-level accuracy. The better user-level models, with different hyperparameters, that gave the highest user-level accuracy are observed to slightly overfit on tweet-level. It leads us to believe that the overfitting in the tweet-level predictions in best user-level models acts similar to an attention mechanism by over-emphasizing some distinctive tweets and ignoring the rest.
In this work, we have used the dataset and benchmarks provided by the PAN 2018 shared task on author profiling.
Answer: They are provided by the PAN 2018 shared task on author profiling.