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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

A bottleneck for medical domain Temporal Expression Recognition (TER) is the availability of data. An open-domain TER system may not be able to capture domain-specific expressions, while domain-specific TER may be cumbersome to implement. We present a novel neural network based medical TER system that uses corpora from news and medical domains. Thus, it serves as a middle ground between an open-domain and a domain-specific TER. We show that our system outperforms state-of-art open-domain baselines, and gets close to domain-specific skylines. Thus, our system proves to be a promising alternative for domain specific TER for domains where data may be limited.

/pdf/a-temporal-expression-recognition-system-for-medical-1w4bcgikr4.pdf

A temporal expression recognition system for medical documents by.

We have developed an online interface for running all the current state-of-the-art algorithms for WSD. This is motivated by the fact that exhaustive comparison of a new Word Sense Disambiguation (WSD) algorithm with existing state-of-the-art algorithms is a tedious task. This impediment is due to one of the following reasons: (1) the source code of the earlier approach is not available and there is a considerable overhead in implementing it or (2) the source code/binary is available but there is some overhead in using it due to system requirements, portability issues, customization issues and software dependencies. A simple tool which has no overhead for the user and has minimal system requirements would greatly benefit the researchers. Our system currently supports 3 languages, viz., English, Hindi and Marathi, and requires only a web-browser to run. To demonstrate the usability of our system, we compare the performance of current state-of-the-art algorithms on 3 publicly available datasets.

/pdf/i-can-sense-it-a-comprehensive-online-system-for-wsd-k44srdn8s4.pdf

I Can Sense It: a Comprehensive Online System for WSD

The rate of publishing scientific articles is increasing day by day which has created difficulty for the researchers to learn about the recent advancements in a faster way. Also, relying on the abstract of these published articles is not a good idea as they cover only broad ideas of the article. The summarization of scientific documents (SDS) addresses this challenge. In this paper, we propose a system for SDS having two components: identifying the relevant sentences in the article using citation context; generation of the summary by posing SDS as a binary optimization problem. For the purpose of optimization, a metaheuristic evolutionary algorithm is utilized. In order to improve the quality of summary, various aspects measuring the relevance of sentences are simultaneously optimized using the concept of multi-objective optimization. Inspired by the popularity of graph-based algorithms like LexRank which is popularly used in solving summarization problems of different real-life applications, its impact is studied in fusion with our optimization framework. An ablation study is also performed to identify the most contributing aspects for the summary generation. We investigated the performance of our proposed framework on two datasets related to the computational linguistic domain, CL-SciSumm 2016 and CL-SciSumm 2017, in terms of ROUGE measures. The results obtained illustrate that our framework effectively improves other existing methods. Further, results are validated using the statistical paired t-test.

Scientific Document Summarization using Citation Context and Multi-objective Optimization

In this paper, we present an approach for merging fine-grained verb senses of Hindi WordNet Senses are merged based on gloss similarity score We explore the use of word embeddings for gloss similarity computation and compare with various WordNet based gloss similarity measures Our results indicate that word embeddings show significant improvement over WordNet based measures Consequently, we observe an increase in accuracy on merging fine-grained senses Gold standard data constructed for our experiments is made available

/pdf/merging-verb-senses-of-hindi-wordnet-using-word-embeddings-i8gi1wsl86.pdf

Merging Verb Senses of Hindi WordNet using Word Embeddings

Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc. Important though the problem is, we are unaware of any benchmark document level data that correctly addresses the evaluation of automatic novelty detection techniques in a classification framework. To bridge this gap, we present here a resource for benchmarking the techniques for document level novelty detection. We create the resource via event-specific crawling of news documents across several domains in a periodic manner. We release the annotated corpus with necessary statistics and show its use with a developed system for the problem in concern.

Pushpak Bhattacharyya

Papers

A temporal expression recognition system for medical documents by.

I Can Sense It: a Comprehensive Online System for WSD

Scientific Document Summarization using Citation Context and Multi-objective Optimization

Merging Verb Senses of Hindi WordNet using Word Embeddings

TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection