In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Example Input: In this paper, we briefly introduce MiniNLP, a natural language processing library for clinical narratives. MiniNLP is an experiment of our ideas on efficient and effective medical language processing. We introduce the overall design of MiniNLP and its major components, and show the performance of it in real projects.
Example Output: A Short Introduction to MiniNLP

Example Input: In this article, how word embeddings can be used as features in Chinese sentiment classification is presented. Firstly, a Chinese opinion corpus is built with a million comments from hotel review websites. Then the word embeddings which represent each comment are used as input in different machine learning methods for sentiment classification, including SVM, Logistic Regression, Convolutional Neural Network (CNN) and ensemble methods. These methods get better performance compared with N-gram models using Naive Bayes (NB) and Maximum Entropy (ME). Finally, a combination of machine learning methods is proposed which presents an outstanding performance in precision, recall and F1 score. After selecting the most useful methods to construct the combinational model and testing over the corpus, the final F1 score is 0.920.
Example Output: An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding

Example Input: A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. One approach to concept drift handling is by feeding the whole training data set once again into a learning machine for retraining. Another approach is by rebuilding an ensemble classifiers to adapt to a new training data set. In either approach, retraining or rebuilding classifiers are expensive and not practical. In this paper, we propose an enhancement of Online-Sequential Extreme Learning Machine (OS-ELM) and its variant Constructive Enhancement OS-ELM (CEOS-ELM) by adding an adaptive capability for classification and regression problem. The scheme is named as Adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and both drifts occurred at the same time (hybrid drift). The AOS-ELM also works well for sudden drift as well as recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using various public dataset widely used for concept drift verification from SEA and STAGGER; and other public datasets such as MNIST and USPS. Experiments show that our method gives higher kappa value compared to the multi-classifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose to take the rank of the pseudo inverse matrix as an indicator parameter to detect 'under-fitting' condition. Keywords adaptive, concept drift, extreme learning machine, online sequential.
Example Output:
Adaptive Online Sequential ELM for Concept Drift Tackling