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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

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CFILT IIT Bombay@HASOC-Dravidian-CodeMix FIRE 2020: Assisting ensemble of transformers with random transliteration.

Scientific documents are getting published at expanding rates and create challenges for the researchers to keep themselves up to date with the new developments. Scientific document summarization solves this problem by providing summaries of essential facts and findings. We propose a novel extractive summarization technique for generating a summary of scientific documents after considering the citation context. The proposed method extracts the scientific document’s relevant sentences with respect to citation text in semantic space by utilizing the word mover’s distance (WMD); further, it clusters the extracted sentences. Moreover, it assigns a rank to cluster of sentences based on different aspects like similarity with the title of the paper, position of the sentence, length of the sentence, and maximum marginal relevance. Finally, sentences are selected from different clusters based on their ranks to form the summary. We conduct our experiments on CL-SciSumm 2016 and CL-SciSumm 2017 data sets. The obtained results are compared with the state-of-the-art techniques. Evaluation results show that our method outperforms others in terms of ROUGE-2, ROUGE-3, and ROUGE-SU4 scores.

Let’s Summarize Scientific Documents! A Clustering-Based Approach via Citation Context

There has been significant progress in image captioning in recent years. The generation of video descriptions is still in its early stages; this is due to the complex nature of videos in comparison to images. Generating paragraph descriptions of a video is even more challenging. Amongst the main issues are temporal object dependencies and complex object-object relationships. Recently, many works are proposed on the generation of multi-sentence video descriptions. The majority of the proposed works are based on a two-step approach: 1) event proposals and 2) caption generation. While these approaches produce good results, they miss out on globally available information. Here we propose the use of global object proposals while generating the video captions. Experimental results on ActivityNet dataset illustrate that the use of global object proposals can produce more informative and correct captions. We also propose three scores to evaluate the object detection capacity of the generator. Comparison of captions generated by the proposed method and the state-of-the-art techniques proves the efficacy of the proposed method.

Global Object Proposals for Improving Multi-Sentence Video Descriptions

Utilizing Wordnets for Cognate Detection among Indian Languages.

Unsupervised Word Sense Disambiguation (WSD) is one of the challenging problems in natural language processing. Recently, an unsupervised bilingual WSD approach has been proposed. This approach uses context aware EM formulation for estimating the sense distribution by using the co-occurrence counts of cross-linked words in comparable corpora. WordNetbased similarity measures are used for approximating the co-occurrence counts. In this paper, we explore the feasibility of the use of Word Embeddings for approximating these counts, which is an extension to the existing approach. We evaluated our approach for Hindi-Marathi language pair, on Health domain. On using the combination of Word Embeddings and WordNet-based similarity measures, we observed 8.5% and 2.5% improvement in the F-score of verbs and adjectives respectively for Marathi and 7% improvement in the F-score of adjectives for Hindi. The experiments show that the combination of Word Embeddings and WordNetbased similarity measures is a reasonable approximation for the bilingual WSD.

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Pushpak Bhattacharyya

Papers

CFILT IIT Bombay@HASOC-Dravidian-CodeMix FIRE 2020: Assisting ensemble of transformers with random transliteration.

Let’s Summarize Scientific Documents! A Clustering-Based Approach via Citation Context

Global Object Proposals for Improving Multi-Sentence Video Descriptions

Utilizing Wordnets for Cognate Detection among Indian Languages.

Using Word Embeddings for Bilingual Unsupervised WSD