Data Mining Practical Machine Learning Tools and Techniques

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

In microblogging services such as Twitter, the users may become overwhelmed by the raw data One solution to this problem is the classification of short text messages As short texts do not provide sufficient word occurrences, traditional classification methods such as "Bag-Of-Words" have limitations To address this problem, we propose to use a small set of domain-specific features extracted from the author's profile and text The proposed approach effectively classifies the text to a predefined set of generic classes such as News, Events, Opinions, Deals, and Private Messages

/pdf/short-text-classification-in-twitter-to-improve-information-3s8z7bfqxf.pdf

Short text classification in twitter to improve information filtering

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.

Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data, text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modeling, which Latent Dirichlet allocation (LDA) is one of the most popular methods in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper can be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated scholarly articles highly (between 2003 to 2016) related to Topic Modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. Also, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.

Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey

Web search engines serve millions of query requests per day. Caching query results is one of the most crucial mechanisms to cope with such a demanding load. In this paper, we propose an efficient storage model to cache document identifiers of query results. Essentially, we first cluster queries that have common result documents. Next, for each cluster, we attempt to store those common document identifiers in a more compact manner. Experimental results reveal that the proposed storage model achieves space reduction of up to 4%. The proposed model is envisioned to improve the cache hit rate and system throughput as it allows storing more query results within a particular cache space, in return to a negligible increase in the cost of preparing the final query result page.

/pdf/space-efficient-caching-of-query-results-in-search-engines-2vn8hyemxq.pdf

Space efficient caching of query results in search engines

We show that a cluster-skipping inverted index (CS-IIS) is a practical and efficient file structure to support category-restricted queries for searching Web directories. The query processing strategy with CS-IIS improves CPU time efficiency without imposing any limitations on the directory size.

/pdf/efficient-processing-of-category-restricted-queries-for-web-1ctbct547g.pdf

Efficient processing of category-restricted queries for web directories

One of the search tasks in Web search is repeat search behaviour to find out documents that users once visited, which is called re-finding. Although there have been several works in the context of general-purpose Web search addressing the latter phenomena, the problem is usually overlooked for vertical search engines. In this work, we report re-finding and newfinding behaviours of users in an educational search context and compare results with the findings in the literature for general-purpose web search. Our analysis shows that re-finding pattern of students differs from web search drastically as only \(26\%\) of all queries indicate re-finding behaviour compared to \(40\%\) in Web.

/pdf/re-finding-behaviour-in-educational-search-1nd1coyp74.pdf

Re-finding Behaviour in Educational Search

We show that a full element-index can be as space-efficient as a direct index with Dewey ids, after compression using typical techniques.

/pdf/on-the-size-of-full-element-indexes-for-xml-keyword-search-3zl9vebp18.pdf

On the size of full element-indexes for XML keyword search

We adopt a supervised learning framework, namely R-LTR [17], to diversify image search results, and extend it in various ways. Our experiments show that the adopted and proposed variants are superior to two well-known baselines, with relative gains up to 11.4%.

https://link.springer.com/content/pdf/10.1007%2F978-3-030-45442-5_20.pdf

Ismail Sengor Altingovde

Papers

Space efficient caching of query results in search engines

Efficient processing of category-restricted queries for web directories

Re-finding Behaviour in Educational Search

On the size of full element-indexes for XML keyword search

Supervised Learning Methods for Diversification of Image Search Results