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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

Speech acts help in uncovering and understanding the communicative intention and behavior of a speaker utterance. This is pertinent for communication on any platform that embodies social media platforms like Twitter. This paper presents a supervised speech act (tweet act in our case) classifier for tweets for assessing the content and intent of tweets, thereby, exploring the valuable communication amongst the tweeters. With the recent success of Bi-directional Encoder Representations from Transformers (BERT), a newly introduced language representation model that provides pre-trained deep bi-directional representations of vast unlabeled data, we introduce BERT-extended that is built on top of BERT. Our model is based on calculating attention weights over the representations of tokens of a sequence to identify tweet acts. The proposed model attained a benchmark accuracy of 75.97% and outperformed several strong baselines and state-of-the-art approaches on an open-source, tweet act annotated Twitter dataset.

A Transformer based Approach for Identification of Tweet Acts

Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks. We introduce automatic drunk-texting prediction as the task of identifying whether a text was written when under the influence of alcohol. We experiment with tweets labeled using hashtags as distant supervision. Our classifiers use a set of N-gram and stylistic features to detect drunk tweets. Our observations present the first quantitative evidence that text contains signals that can be exploited to detect drunk-texting.

/pdf/a-computational-approach-to-automatic-prediction-of-drunk-yxgqi5bgli.pdf

A Computational Approach to Automatic Prediction of Drunk-Texting

In this paper we present our work on the usage of lexical resources for the Machine Translation English and Malayalam. We describe a comparative performance between different Statistical Machine Translation (SMT) systems on top of phrase based SMT system as baseline. We explore different ways of utilizing lexical resources to improve the quality of English Malayalam statistical machine translation. In order to enrich the training corpus we have augmented the lexical resources in two ways (a) additional vocabulary and (b) inflected verbal forms. Lexical resources include IndoWordnet semantic relation set, lexical words and verb phrases etc. We have described case studies, evaluations and have given detailed error analysis for both Malayalam to English and English to Malayalam machine translation systems. We observed significant improvement in evaluations of translation quality. Lexical resources do help uplift performance when parallel corpora are scanty.

Lexical Resources to Enrich English Malayalam Machine Translation

pyiwn: A Python based API to access Indian Language WordNets.

The self-organizing map (SOM) helps in exploratory phase of data mining by projecting the input data into a lower dimensional map. In recent years SOM has also been applied for classification of data points. The prominent utility of SOM based classification is evident from the use of no labeled data during training. In a multi-class classification problem where classes have high degree of overlap, it would be difficult to design a single-level SOM based classification system which can perform well for all the classes. In order to deal with the multi-class classification efficiently, the current paper proposes to develop a Cascaded SOM based architecture where classes are handled in a hierarchical way. Also, it can be applied for solving any multi-class classification problem where labeled data is limited. As a case study, in the first part of the paper results are shown for single label version of complex email classification problem where classes are highly overlapping to each other and in the second part, results are shown for some multi-labeled data sets. Different representation schemas for emails and a large set of features are also adopted for the purpose of experiment. Proposed Cascaded SOM based classification model performs well in email-classification compared to standard classification approaches and classical SOM based model.

Pushpak Bhattacharyya

Papers

A Transformer based Approach for Identification of Tweet Acts

A Computational Approach to Automatic Prediction of Drunk-Texting

Lexical Resources to Enrich English Malayalam Machine Translation

pyiwn: A Python based API to access Indian Language WordNets.

Cascaded SOM: An Improved Technique for Automatic Email Classification