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Linguists have been focused on a qualitative comparison of the semantics from different languages. Evaluation of the semantic interpretation among disparate language pairs like English and Tamil is an even more formidable task than for Slavic languages. The concept of word embedding in Natural Language Processing (NLP) has enabled a felicitous opportunity to quantify linguistic semantics. Multi-lingual tasks can be performed by projecting the word embeddings of one language onto the semantic space of the other. This research presents a suite of data-efficient deep learning approaches to deduce the transfer function from the embedding space of English to that of Tamil, deploying three popular embedding algorithms: Word2Vec, GloVe and FastText. A novel evaluation paradigm was devised for the generation of embeddings to assess their effectiveness, using the original embeddings as ground truths. Transferability across other target languages of the proposed model was assessed via pre-trained Word2Vec embeddings from Hindi and Chinese languages. We empirically prove that with a bilingual dictionary of a thousand words and a corresponding small monolingual target (Tamil) corpus, useful embeddings can be generated by transfer learning from a well-trained source (English) embedding. Furthermore, we demonstrate the usability of generated target embeddings in a few NLP use-case tasks, such as text summarization, part-of-speech (POS) tagging, and bilingual dictionary induction (BDI), bearing in mind that those are not the only possible applications.

The neural network, one of the intelligent data mining technique that has been used by researchers in various areas for the past 10 years. Prediction and analysis of stock market data have got an important role in today’s economy. The various algorithms used for forecasting can be categorized into linear (AR, MA, ARIMA, ARMA) and non-linear models (ARCH, GARCH, Neural Network). In this paper, we are using four types of deep learning architectures i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available. Here we are using day-wise closing price of two different stock markets, National Stock Exchange (NSE) of India and New York Stock Exchange (NYSE). The network was trained with the stock price of a single company from NSE and predicted for five different companies from both NSE and NYSE. It has been observed that CNN is outperforming the other models. The network was able to predict for NYSE even though it was trained with NSE data. This was possible because both the stock markets share some common inner dynamics. The results obtained were com- pared with ARIMA model and it has been observed that the neural networks are outperforming the existing linear model (ARIMA).

Tree adjoining grammars (TAGs) are mildly context sensitive formalisms used mainly in modelling natural languages. Usage and research on these psycho linguistic formalisms have been erratic in the past decade, due to its demanding construction and difficulty to parse. However, they represent promising future for formalism based NLP in multilingual scenarios. In this paper we demonstrate basic synchronous Tree adjoining grammar for English-Tamil language pair that can be used readily for machine translation. We have also developed a multithreaded chart parser that gives ambiguous deep structures and a par dependency structure known as TAG derivation. Furthermore we then focus on a model for training this TAG for each language using a large corpus of text through a map reduce frequency count model in spark and estimation of various probabilistic parameters for the grammar trees thereafter; these parameters can be used to perform statistical parsing on the trained grammar.

Background/Objectives: This paper is aimed at performing real time bigdata analytics on vehicular data collected from a network of ECUs (Electronic Control Unit) in cooperated into the different automobiles. Methods/Statistical Analysis: The analytics has been performed by building a software model that is capable of handling the vehicular data in real time. Bigdata platforms like hadoop, Apache Storm, Apache Spark(real time streaming), Kafka are utilised here. Automotive sensor data from different Electronic Control Units are collected into a central data server and this data is pushed to kafka, from which the real time streaming models pulls the data and perform analysis. Findings: Automotive industry has undergone a drastic revolutionised innovation in the past decade in all of its respective segments. The industry had started utilizing the computational and mathematical aspects from top to bottom in its design strategies to achieve greater reliability on its products out on roads. Latest advancements in this field is the fully autonomous car. Today an automotive is a collection of innumerable sensors and microcontrollers which are under the command of the master ECU. A network of ECUs connected across the globe is a source tap of bigdata. Leveraging the new sources of bigdata by automotive giants boost vehicle performance, enhance loco driver experience, accelerated product designs. Statistical Projections reveal that automotive industry is likely to be the 2nd largest generator of data by mid of 2016. The contribution of this paper to the automotive industry is the real time vehicle monitoring utilizing Bigdata platforms. This can contribute to better customer-industry relations. Applications/Improvements: The model developed in this paper can contribute a lot to the automobile industry as it facilitates real time monitoring of the vehicles. This can improve customer-industry relation.