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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

Entity extraction is one of the important tasks in various natural language processing (NLP) application areas. There has been a significant amount of works related to entity extraction, but mostly for a few languages (such as English, some European languages and few Asian languages) and doamins such as newswire. Nowadays social media have become a convenient and powerful way to express one’s opinion and sentiment. India is a diverse country with a lot of linguistic and cultural variations. Texts written in social media are informal in nature, and perople often use more than one script while writing. User generated content such as tweets, blogs and personal websites of people are written using Roman script or sometimes users may use both Roman as well as indigenous scripts. Entity extraction is, in general, a more challenging task for such an informal text, and mixing of codes further complicates the process. In this paper, we propose a hybrid approah for enity extraction from code mixed language pairs such as English-Hindi and EnglishTamil. We use a rich linguistic feature set to train Conditional Random Field (CRF) classifier. The output of classifier is post-processed with a carefully hand-crafted feature set. The proposed system achieve the F-scores of 62.17% and 44.12% for English-Hindi and English-Tamil language pairs, respectively. Our system attains the best F-score among all the systems submitted in Fire 2016 shared task for the English-Tamil language pairs. CCS Concepts •Computing methodologies→Natural Language Processing; •Information System → Information Extraction; •Algorithm → Conditional Random Field(CRF);

/pdf/a-hybrid-approach-for-entity-extraction-in-code-mixed-social-4wca3g2hv0.pdf

A Hybrid Approach for Entity Extraction in Code-Mixed Social Media Data.

In this paper, we present a unique approach to Knowledge Extraction from Hindi text which preserves the predicate till the end. This approach helps in highly accurate analysis of sentences. The analysis produces a semantic net like structure expressed by means of Universal Networking Language (UNL) - a recently proposed interlingua. Extremely varied and complex phenomena of Hindi language have been tackled as the case studies reveal. This representation can be used for developing web based applications in Hindi.

/pdf/knowledge-extraction-from-hindi-text-4e70zpbr63.pdf

Knowledge Extraction from Hindi Text

Word sense disambiguation is a core problem in many tasks related to language processing. In this paper, we introduce the notion of soft word sense disambiguation which states that given a word, the sense disambiguation system should not commit to a particular sense, but rather, to a set of senses which are not necessarily orthogonal or mutually exclusive. The senses of a word are expressed by its WordNet synsets, arranged according to their relevance. The relevance of these senses are probabilistically determined through a Bayesian Belief Network. The main contribution of the work is a completely probabilistic framework for word- sense disambiguation with a semi-supervised learning technique utilising WordNet. WordNet can be customized to a domain using corpora from that domain. This idea applied to question answering has been evaluated on TREC data and the results are promising.

/pdf/soft-word-sense-disambiguation-4lq9j4426a.pdf

Soft Word Sense Disambiguation

Wordnets, which are repositories of lexical semantic knowledge containing semantically linked synsets and lexically linked words, are indispensable for work on computational linguistics and natural language processing. While building wordnets for Hindi and Marathi, two major Indo-European languages, we observed that the verb hierarchy in the Princeton Wordnet was rather shallow. We set to constructing a verb knowledge base for Hindi, which arranges the Hindi verbs in a hierarchy of is-a (hypernymy) relation. We realized that there are unique Indian language phenomena that bear upon the lexicalization vs. syntactically derived choice. One such example is the occurrence of conjunct and compound verbs (called Complex Predicates) which are found in all Indian languages. This paper presents our experience in the construction of lexical knowledge bases for Indian languages with special attention to Hindi. The question of storing versus deriving complex predicates has been dealt with linguistically and computationally. We have constructed empirical tests to decide if a combination of two words, the second of which is a verb, is a complex predicate or not. Such tests provide a principled way of deciding the status of complex predicates in Indian language wordnets.

/pdf/complex-predicates-in-indian-languages-and-wordnets-bheqwzq63v.pdf

Complex predicates in Indian languages and wordnets

In this paper, we describe our EnglishHindi and Hindi-English statistical systems submitted to the WMT14 shared task. The core components of our translation systems are phrase based (Hindi-English) and factored (English-Hindi) SMT systems. We show that the use of number, case and Tree Adjoining Grammar information as factors helps to improve English-Hindi translation, primarily by generating morphological inflections correctly. We show improvements to the translation systems using pre-procesing and post-processing components. To overcome the structural divergence between English and Hindi, we preorder the source side sentence to conform to the target language word order. Since parallel corpus is limited, many words are not translated. We translate out-of-vocabulary words and transliterate named entities in a post-processing stage. We also investigate ranking of translations from multiple systems to select the best translation.

/pdf/the-iit-bombay-hindi-english-translation-system-at-wmt-2014-25p7hz8bc0.pdf

Pushpak Bhattacharyya

Papers

A Hybrid Approach for Entity Extraction in Code-Mixed Social Media Data.

Knowledge Extraction from Hindi Text

Soft Word Sense Disambiguation

Complex predicates in Indian languages and wordnets

The IIT Bombay Hindi-English Translation System at WMT 2014