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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

Being less resource languages, Indian-Indian and English-Indian language MT system developments faces the difficulty to translate various lexical phenomena. In this paper, we present our work on a comparative study of 440 phrase-based statistical trained models for 110 language pairs across 11 Indian languages. We have developed 110 baseline Statistical Machine Translation systems. Then we have augmented the training corpus with Indowordnet synset word entries of lexical database and further trained 110 models on top of the baseline system. We have done a detailed performance comparison using various evaluation metrics such as BLEU score, METEOR and TER. We observed significant improvement in evaluations of translation quality across all the 440 models after using the Indowordnet. These experiments give a detailed insight in two ways : (1) usage of lexical database with synset mapping for resource poor languages (2) efficient usage of Indowordnet sysnset mapping. More over, synset mapped lexical entries helped the SMT system to handle the ambiguity to a great extent during the translation.

Indowordnets help in Indian Language Machine Translation

Automatic Cognate Detection helps NLP tasks of Machine Translation, Information Retrieval, and Phylogenetics. Cognate words are defined as word pairs across languages which exhibit partial or full lexical similarity and mean the same (e.g., hund-hound in German-English). In this paper, we use a Siamese Feed-forward neural network with word-embeddings to detect such word pairs. Our experiments with various embedding dimensions show larger embedding dimensions can only be used for large corpora sizes for this task. On a dataset built using linked Indian Wordnets, our approach beats the baseline approach with a significant margin (up to 71%) with the best F-score of 0.85% on the Hindi-Gujarati language pair.

Keep Your Dimensions on a Leash: True Cognate Detection using Siamese Deep Neural Networks

'To' is one of the most common lexemes in English (as found in 34% of the sentences in the Penn Tree Bank corpus). This paper describes the analysis of the sentences involving `to'. `To' poses problems at lexical, syntactic and semantic level, as we need to resolve whether the phrase which is fronted by `to' is a prepositional phrase (PP) or an infinitival clause which is a kind of inflectional phrase (IP). In case of PP, we need to locate the attachment site for it and assign the semantic relation. In case of IP, we need to resolve the empty pronominal in addition to the attachment resolution-a task essential to make the semantic analysis complete. We achieve an accuracy of about 80% in the above tasks.

Semantic analysis of 'to'

We present a Parallel Corpora Management tool that aides parallel corpora generation for the task of Machine Translation (MT). It takes source and target text of a corpus for any language pair in text file format, or zip archives containing multiple corresponding text files. Then, it provides with a helpful interface to lexicographers for manual translation / validation, and gives out the corrected text files as output. It provides various dictionary references as help within the interface which increase the productivity and efficiency of a lexicographer. It also provides automatic translation of the source sentence using an integrated MT system. The tool interface includes a corpora management system which facilitates maintenance of parallel corpora by assigning roles such as manager, lexicographer etc. We have designed a novel tool that provides aides like references to various dictionary sources such as Wordnets, Shabdkosh, Wikitionary etc. We also provide manual word alignment correction which is visualized in the tool and can lead to its gamification in the future, thus, providing a valuable source of word / phrase alignments.

/pdf/pacman-parallel-corpus-management-workbench-4etrinbldd.pdf

PaCMan : Parallel Corpus Management Workbench

During this pandemic situation, extracting any relevant information related to COVID-19 will be immensely beneficial to the community at large. In this paper, we present a very important resource, COVIDRead, a Stanford Question Answering Dataset (SQuAD) like dataset over more than 100k question-answer pairs. The dataset consists of Context-Answer-Question triples. Primarily the questions from the context are constructed in an automated way. After that, the system-generated questions are manually checked by hu-mans annotators. This is a precious resource that could serve many purposes, ranging from common people queries regarding this very uncommon disease to managing articles by editors/associate editors of a journal. We establish several end-to-end neural network based baseline models that attain the lowest F1 of 32.03% and the highest F1 of 37.19%. To the best of our knowledge, we are the first to provide this kind of QA dataset in such a large volume on COVID-19. This dataset creates a new avenue of carrying out research on COVID-19 by providing a benchmark dataset and a baseline model.

Pushpak Bhattacharyya

Papers

Indowordnets help in Indian Language Machine Translation

Keep Your Dimensions on a Leash: True Cognate Detection using Siamese Deep Neural Networks

Semantic analysis of 'to'

PaCMan : Parallel Corpus Management Workbench

COVIDRead: A Large-scale Question Answering Dataset on COVID-19.