Semantic Loss Application to Entity Relation Recognition

Usually, entity relation recognition systems either use a pipe-lined model that treats the entity tagging and relation identification as separate tasks or a joint model that simultaneously identifies the relation and entities. This paper compares these two general approaches for the entity relation recognition. State-of-the-art entity relation recognition systems are built using deep recurrent neural networks which often does not capture the symbolic knowledge or the logical constraints in the problem. The main contribution of this paper is an end-to-end neural model for joint entity relation extraction which incorporates a novel loss function. This novel loss function encodes the constraint information in the problem to guide the model training effectively. We show that addition of this loss function to the existing typical loss functions has a positive impact over the performance of the models. This model is truly end-to-end, requires no feature engineering and easily extensible. Extensive experimentation has been conducted to evaluate the significance of capturing symbolic knowledge for natural language understanding. Models using this loss function are observed to be outperforming their counterparts and converging faster. Experimental results in this work suggest the use of this methodology for other language understanding applications.

Sentiment Analysis of Twitter Data for Predicting Stock Market Movements

Predicting stock market movements is a well-known problem of interest. Now-a-days social media is perfectly representing the public sentiment and opinion about current events. Especially, twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on twitter has been an intriguing field of research. Previous studies have concluded that the aggregate public mood collected from twitter may well be correlated with Dow Jones Industrial Average Index (DJIA). The thesis of this work is to observe how well the changes in stock prices of a company, the rises and falls, are correlated with the public opinions being expressed in tweets about that company. Understanding author's opinion from a piece of text is the objective of sentiment analysis. The present paper have employed two different textual representations, Word2vec and N-gram, for analyzing the public sentiments in tweets. In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from twitter and analyze the correlation between stock market movements of a company and sentiments in tweets. In an elaborate way, positive news and tweets in social media about a company would definitely encourage people to invest in the stocks of that company and as a result the stock price of that company would increase. At the end of the paper, it is shown that a strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets.

An Improved Approach for Prediction of Parkinson's Disease using Machine Learning Techniques

Parkinson's disease (PD) is one of the major public health problems in the world. It is a well-known fact that around one million people suffer from Parkinson's disease in the United States whereas the number of people suffering from Parkinson's disease worldwide is around 5 million. Thus, it is important to predict Parkinson's disease in early stages so that early plan for the necessary treatment can be made. People are mostly familiar with the motor symptoms of Parkinson's disease, however, an increasing amount of research is being done to predict the Parkinson's disease from non-motor symptoms that precede the motor ones. If an early and reliable prediction is possible then a patient can get a proper treatment at the right time. Nonmotor symptoms considered are Rapid Eye Movement (REM) sleep Behaviour Disorder (RBD) and olfactory loss. Developing machine learning models that can help us in predicting the disease can play a vital role in early prediction. In this paper, we extend a work which used the non-motor features such as RBD and olfactory loss. Along with this the extended work also uses important biomarkers. In this paper, we try to model this classifier using different machine learning models that have not been used before. We developed automated diagnostic models using Multilayer Perceptron, BayesNet, Random Forest and Boosted Logistic Regression. It has been observed that Boosted Logistic Regression provides the best performance with an impressive accuracy of 97.159 % and the area under the ROC curve was 98.9%. Thus, it is concluded that these models can be used for early prediction of Parkinson's disease.