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

Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.

A Survey on Recent Advances in Named Entity Recognition from Deep Learning models

In last few decades, with the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles to automate the process of literature review. Therefore, in this study, aim is to present the overall progress concerning automatic information extraction from scientific articles. The information insights extracted from scientific articles are classified in two broad categories i.e. metadata and key-insights. As available benchmark datasets carry a significant role in overall development in this research domain, existing datasets against both categories are extensively reviewed. Later, research studies in literature that have applied various computational approaches applied on these datasets are consolidated. Major computational approaches in this regard include Rule-based approaches, Hidden Markov Models, Conditional Random Fields, Support Vector Machines, Naive-Bayes classification and Deep Learning approaches. Currently, there are multiple projects going on that are focused towards the dataset construction tailored to specific information needs from scientific articles. Hence, in this study, state-of-the-art regarding information extraction from scientific articles is covered. This study also consolidates evolving datasets as well as various toolkits and code-bases that can be used for information extraction from scientific articles.

Information extraction from scientific articles: a survey

This study examines the relationship between information and communication technology (ICT), infrastructure, and tourism development in Africa between 1996 and 2016 using a dynamic panel gravity mo...

https://journals.sagepub.com/doi/pdf/10.1177/1354816619827712

ICT, infrastructure, and tourism development in Africa:

This paper explores the use of machine learning approaches, or more specifically, four supervised learning Methods, namely Decision Tree(C 4.5), K-Nearest Neighbour (KNN), Naive Bays (NB), and Support Vector Machine (SVM) for categorization of Bangla web documents. This is a task of automatically sorting a set of documents into categories from a predefined set. Whereas a wide range of methods have been applied to English text categorization, relatively few studies have been conducted on Bangla language text categorization. Hence, we attempt to analyze the efficiency of those four methods for categorization of Bangla documents. In order to validate, Bangla corpus from various websites has been developed and used as examples for the experiment. For Bangla, empirical results support that all four methods produce satisfactory performance with SVM attaining good result in terms of high dimensional and relatively noisy document feature vectors.

Supervised learning Methods for Bangla Web Document Categorization

 Abstract— Subject based web directories like Open Directory Project's (ODP) Directory Mozilla (DMOZ), Yahoo etc., consists of web pages classified into various categories. The proper classification has made these directories popular among the web users. The exponential growth of the web has made it difficult to manage human edited subject based web directories. The World Wide Web (WWW) lacks a comprehensive web site directory. Web site classification using machine learning techniques is therefore an emerging possibility to automatically maintain directory services for the web. Home page of a web site is a distinguished page and it acts as an entry point by providing links to the rest of the web site. The information contained in the title, meta keyword, description and in the labels of the anchor (A HREF) tags along with the other content is a very rich source of features required for classification. Compared to the other pages of the website, webmasters take more care to design the homepage and its content to give it an aesthetic look and at the same time attempt to precisely summarize the organization to which the site belongs. This expression power of the home page of a website can be exploited to identify the nature of the organization. In this paper we attempt to classify web sites based on the content of their home pages using the Naive Bayesian machine learning algorithm.

/pdf/automated-classification-of-web-sites-using-naive-bayesian-3ly43q3m40.pdf

Automated Classification of Web Sites using Naive Bayesian Algorithm

Named Entity Recognition (NER) is sub task of Information Extraction that includes identification of named entities and classification of them into named entity classes such as person, location and organization etc. NER can be used to preprocess textual information and convert it into structured form that can be useful for Information Retrieval, Machine Translation, Question Answering System and Text Summarization. This paper presents a survey regarding NER research done for various Indian and non Indian languages. The study and observations related to approaches, techniques and features required to implement NER for various languages especially for Indian languages is reported. General Terms NER (Named Entity Recognition), HMM (Hidden Markov Model), CRF (Conditional Random Fields), SVM (Support Vector Machine)

/pdf/survey-of-named-entity-recognition-systems-with-respect-to-52vt1io7px.pdf

Survey of Named Entity Recognition Systems with respect to Indian and Foreign Languages

Part-of-speech tagging in Marathi language is a very complex task as Marathi is highly inflectional in nature & free word order language. In this paper we have demonstrated a rulebased Part-of-Speech tagger for Marathi Language. The hand– constructed rules that are learned from corpus and some manual addition after studying the grammar of Marathi language are added and that are used for developing the tagger. Disambiguation is done by analyzing the linguistic feature of the word, its preceding word, its following word, etc. After testing the system with three data sets we got encouraging results. The accuracy of our system is of an average 78.82% after testing it on three different data sets. General Terms Natural Language Processing.

/pdf/part-of-speech-tagger-for-marathi-language-using-limited-2fc1l95bh1.pdf

Part-of-Speech Tagger for Marathi Language using Limited Training Corpora

Named Entity Recognition using Conditional Random Fields

Information Extraction (IE) is a sub discipline of Artificial Intelligence. IE identifies information in unstructured information source that adheres to predefined semantics i.e. people, location etc. Recognition of named entities (NEs) from computer readable natural language text is significant task of IE and natural language processing (NLP). Named entity (NE) extraction is important step for processing unstructured content. Unstructured data is computationally opaque. Computers require computationally transparent data for processing. IE adds meaning to raw data so that it can be easily processed by computers. There are various different approaches that are applied for extraction of entities from text. This paper elaborates need of NE recognition for Marathi and discusses issues and challenges involved in NE recognition tasks for Marathi language. It also explores various methods and techniques that are useful for creation of learning resources and lexicons that are important for extraction of NEs from natural language unstructured text.

/pdf/issues-and-challenges-in-marathi-named-entity-recognition-idzs4f36ul.pdf

B. V. Pawar

Papers

Automated Classification of Web Sites using Naive Bayesian Algorithm

Survey of Named Entity Recognition Systems with respect to Indian and Foreign Languages

Part-of-Speech Tagger for Marathi Language using Limited Training Corpora

Named Entity Recognition using Conditional Random Fields

Issues and challenges in marathi named entity recognition