Abstract Scientific documents generally contain multiple mathematical expressions in them. Detecting inline mathematical expressions are one of the most important and challenging tasks in scientific text mining. Recent works that detect inline mathematical expressions in scientific documents have looked at the problem from an image processing perspective. There is little work that has targeted the problem from NLP perspective. Towards this, we define a few features and applied Conditional Random Fields (CRF) to detect inline mathematical expressions in scientific documents. Apart from this feature based approach, we also propose a hybrid algorithm that combines Bidirectional Long Short Term Memory networks (Bi‐LSTM) and feature‐based approach for this task. Experimental results suggest that this proposed hybrid method outperforms several baselines in the literature and also individual methods in the hybrid approach.

Every day, lots of events keep happening around the globe. Examples of events are festivals, concerts, shows, conferences, sports events, movie launches, etc. There are several event recommendation systems that suggest different events to the users. The popularity of an event plays an important role in event recommendation. If the system knows which event is popular in a region, then that can be recommended to more people in that region. However, since events take place at predefined location and time it is important to identify the popularity much before the occurrence of the event. Early prediction of future popularity is very crucial for the success of any event recommendation system. We plan to use event related discussions in social media as a signal for estimating the popularity of the events. Towards this task, given meta information (time, date, venue, title etc.) about an event, we first try to identify social media posts related to the event. These posts are then passed through several preprocessing stages in a pipeline to detect spam, identify the presence of emotions and their intensities, etc. The preprocessed posts can then be used along with several other contextual parameters to predict the future popularity of the events. Next, events can be recommended to the users by using a recommendation algorithm.

As the social networking sites get more popular, spammers target these sites to spread spam posts. Twitter is one of the most popular online social networking sites where users communicate and interact on various topics. Most of the current spam filtering methods in Twitter focus on detecting the spammers and blocking them. However, spammers can create a new account and start posting new spam tweets again. So there is a need for robust spam detection techniques to detect the spam at tweet level. These types of techniques can prevent the spam in real time. To detect the spam at tweet level, often features are defined, and appropriate machine learning algorithms are applied in the literature. Recently, deep learning methods are showing fruitful results on several natural language processing tasks. We want to use the potential benefits of these two types of methods for our problem. Toward this, we propose an ensemble approach for spam detection at tweet level. We develop various deep learning models based on convolutional neural networks (CNNs). Five CNNs and one feature-based model are used in the ensemble. Each CNN uses different word embeddings (Glove, Word2vec) to train the model. The feature-based model uses content-based, user-based, and n-gram features. Our approach combines both deep learning and traditional feature-based models using a multilayer neural network which acts as a meta-classifier. We evaluate our method on two data sets, one data set is balanced, and another one is imbalanced. The experimental results show that our proposed method outperforms the existing methods.