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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

In this paper we describe the system that we develop as part of our participation in WAT 2016. We develop a system based on hierarchical phrase-based SMT for English to Hindi language pair. We perform re-ordering and augment bilingual dictionary to improve the performance. As a baseline we use a phrase-based SMT model. The MT models are fine-tuned on the development set, and the best configurations are used to report the evaluation on the test set. Experiments show the BLEU of 13.71 on the benchmark test data. This is better compared to the official baseline BLEU score of 10.79.

IITP English-Hindi Machine Translation System at WAT 2016.

Semantically Relatable Sets: Building Blocks for Representing Semantics

In the current paper, a system for multi-document summarization (MLDS) is developed which simultaneously optimized different quality measures to obtain a good summary. These measures include anti-redundancy, coverage, and, readability. For optimization, multi-objective binary differential evolution (MBDE) is utilized which is an evolutionary algorithm. MBDE consists of a set of solutions and each solution represents a subset of sentences to be selected in the summary. Generally, MBDE uses a single DE variant, but, here, an ensemble of two different DE variants measuring diversity among solutions and convergence towards the global optimal solution, respectively, is employed for efficient search. Three versions of the proposed model are developed varying the syntactic/semantic similarity between sentences and DE parameters selection strategy. Results are evaluated on the standard DUC datasets using ROUGE-2 measure and significant improvements of \(15.5\%\) and \(4.56\%\) are attained by the proposed approach over the two exiting techniques.

Multi-document Summarization Using Adaptive Composite Differential Evolution

Noun compounds are interesting constructs in Natural Language Processing (NLP). Interpretation of noun compounds is the task of uncovering a relationship between component nouns of a noun compound. There has not been much progress in this field due to lack of a standardized set of relation inventory and associated annotated dataset which can be used to evaluate suggested solutions. Available datasets in the literature suffer from two problems. Firstly, the approaches to creating some of the relation inventories and datasets are statistically motivated, rather than being linguistically motivated. Secondly, there is little overlap among the semantic relation inventories used by them. We attempt to bridge this gap through our paper. We present a dataset that is (a) linguistically grounded by using Levi (1978)’s theory, and (b) uses frame elements of FrameNet as its semantic relation inventory. The dataset consists of 2,600 examples created by an automated extraction from FrameNet annotated corpus, followed by a manual investigation. These attributes make our dataset useful for noun compound interpretation in a general-purpose setting.

/pdf/towards-a-standardized-dataset-for-noun-compound-2nutthaj6v.pdf

Towards a Standardized Dataset for Noun Compound Interpretation

In a previous work of ours Chinnakotla et al. (2010) we introduced a novel framework for Pseudo-Relevance Feedback (PRF) called MultiPRF. Given a query in one language called Source, we used English as the Assisting Language to improve the performance of PRF for the source language. MulitiPRF showed remarkable improvement over plain Model Based Feedback (MBF) uniformly for 4 languages, viz., French, German, Hungarian and Finnish with English as the assisting language. This fact inspired us to study the effect of any source-assistant pair on MultiPRF performance from out of a set of languages with widely different characteristics, viz., Dutch, English, Finnish, French, German and Spanish. Carrying this further, we looked into the effect of using two assisting languages together on PRF.

The present paper is a report of these investigations, their results and conclusions drawn therefrom. While performance improvement on MultiPRF is observed whatever the assisting language and whatever the source, observations are mixed when two assisting languages are used simultaneously. Interestingly, the performance improvement is more pronounced when the source and assisting languages are closely related, e.g., French and Spanish.

/pdf/multilingual-pseudo-relevance-feedback-performance-study-of-4qvxfs1u12.pdf

Pushpak Bhattacharyya

Papers

IITP English-Hindi Machine Translation System at WAT 2016.

Semantically Relatable Sets: Building Blocks for Representing Semantics

Multi-document Summarization Using Adaptive Composite Differential Evolution

Towards a Standardized Dataset for Noun Compound Interpretation

Multilingual Pseudo-Relevance Feedback: Performance Study of Assisting Languages