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Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

How to Do Things With Words

We present ScholarlyRead, span-of-word-based scholarly articles’ Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances. ScholarlyRead was constructed in semi-automatic way. We consider the articles from two popular journals of a reputed publishing house. Firstly, we generate questions from these articles in an automatic way. Generated questions are then manually checked by the human annotators. We propose a baseline model based on Bi-Directional Attention Flow (BiDAF) network that yields the F1 score of 37.31%. The framework would be useful for building Question-Answering (QA) systems on scientific articles.

ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension.

Cross-lingual word embeddings have become ubiquitous for various NLP tasks. Existing literature primarily evaluate the quality of cross-lingual word embeddings on the task of Bilingual Lexicon Induction. They report very high accuracies for European languages. In this paper, we report the accuracy of Bilingual Lexicon Induction (BLI) task for cross-lingual word embeddings generated using two mapping based unsupervised approaches: VecMap and MUSE for Indian languages on a dataset created using linked Indian Wordnet. We also show the comparison of these approaches with a simple baseline where the embeddings for all languages are trained using fast-text on the combined corpora of 11 Indian languages. Our experiments show that existing cross-lingual word embedding approaches give low accuracy on bilingual lexicon induction for cognate words. Given the high cognate overlap of several Indian languages, this is a serious limitation of existing approaches.

A Study of Efficacy of Cross-lingual Word Embeddings for Indian Languages

Emotion analysis from texts has emerged as an important area of research in the field of Natural Language Processing (NLP) in the past few years. Several benchmark datasets have been released for this task, however most of the datasets are open domain in nature and for resource-rich language like English. These datasets may not always be sufficient for capturing domain specific emotions, and may tend to skew towards an emotion based on the domain. In this paper, we provide a framework for multilingual emotion detection to deal with the crisis situations. We collect and annotate disaster domain social media tweets and news data in two languages, namely English and Hindi. We derive 6 emotions from Plutchik's wheel of emotions suitable for disaster domain, and annotate the data using these disaster specific emotions. In total we create four emotionally enriched datasets i.e. 2 tweets datasets (English and Hindi) and 2 news dataset (English and Hindi). We also establish strong baselines on the dataset using two popular deep learning algorithms, stacked Bi-LSTM+CNN and BERT. Evaluation shows that the best model achieves the averaged accuracy of 68% across the four different datasets.

Emotion driven Crisis Response: A benchmark Setup for Multi-lingual Emotion Analysis in Disaster Situations

We present a quantitative, data-driven machine learning approach to mitigate the problem of unpredictability of Computer Science Graduate School Admissions. In this paper, we discuss the possibility of a system which may help prospective applicants evaluate their Statement of Purpose (SOP) based on our system output. We, then, identify feature sets which can be used to train a predictive model. We train a model over fifty manually verified SOPs for which it uses an SVM classifier and achieves the highest accuracy of 92% with 10-fold cross-validation. We also perform experiments to establish that Word Embedding based features and Document Similarity-based features outperform other identified feature combinations. We plan to deploy our application as a web service and release it as a FOSS service.

Is your Statement Purposeless? Predicting Computer Science Graduation Admission Acceptance based on Statement Of Purpose

Word is multilingual, and never before was the need for multilingual information processing felt more acutely than today. Advent of Web and the presence of enormous amount of text in multiple languages in electronic form have underlined the need for multilingual information access and transfer. The need for understanding and communication in the face of language barrier makes translation of languages a real and continuous need.

Pushpak Bhattacharyya

Papers

ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension.

A Study of Efficacy of Cross-lingual Word Embeddings for Indian Languages

Emotion driven Crisis Response: A benchmark Setup for Multi-lingual Emotion Analysis in Disaster Situations

Is your Statement Purposeless? Predicting Computer Science Graduation Admission Acceptance based on Statement Of Purpose

Tutorial on machine translation