Information Retrieval

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Evaluation as a Service for Information Retrieval

Evaluation as a Service for Information Retrieval

How can we run large-scale, community-wide evaluations of information retrieval systems if we lack the ability to distribute the document collection on which the task is based? This was the challenge we faced in the TREC Microblog tracks over the past few years. In this paper, we present a novel evaluation methodology we dub “evaluation as a service”, which was implemented at TREC 2013 to address restrictions on data redistribution. The basic idea is that instead of distributing the document collection, we (the track organizers) provided a service API “in the cloud” with which participants could accomplish the evaluation task. We outline advantages as well as disadvantages of this evaluation methodology, and discuss how the approach might be extended to other evaluation scenarios.
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Methods for Distributed Information Retrieval

Methods for Distributed Information Retrieval

Published methods for distributed information retrieval generally rely on cooperation from search servers. But most real servers, particularly the tens of thousands available on the Web, are not engineered for such cooperation. This means that the majority of methods proposed, and evaluated in simulated environments of homogeneous coop- erating servers, are never applied in practice.

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Effective Information Retrieval System

Effective Information Retrieval System

Data retrieval, in the context of an IR system, consists mainly of determining which documents of a collection contain the keywords in the user query which, most frequently, is not enough to satisfy the user information need. In fact, the user of an IR system is concerned more with retrieving information about a subject than with retrieving data which satisfies a given query. A data retrieval language aims at retrieving all objects which satisfy clearly defined conditions such as those in a regular expression or in a relational algebra expression. Thus, for a data retrieval system, a single erroneous object among a thousand retrieved objects means total failure. For an information retrieval system, however, the retrieved objects might be inaccurate and small errors are likely to go unnoticed. The main reason for this difference is that information retrieval usually deals with natural language text which is not always well structured and could be semantically ambiguous. On the other hand, a data retrieval system (such as a relational database) deals with data that has a well defined structure and semantics. One may want to criticise this dichotomy on the grounds that the boundary between the two is a vague one.
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Robust Information-Theoretic Private Information Retrieval

Robust Information-Theoretic Private Information Retrieval

A Private Information Retrieval (PIR) protocol allows a user to retrieve a data item of its choice from a database, such that the servers storing the database do not gain information on the identity of the item being retrieved. PIR protocols were studied in depth since the subject was introduced in Chor, Goldreich, Kushilevitz, and Sudan 1995. The standard definition of PIR protocols raises a simple question – what happens if some of the servers crash during the operation? How can we devise a protocol which still works in the presence of crashing servers? Current systems do not guarantee availability of servers at all times for many reasons, e.g., crash of server or communication problems. Our purpose is to design robust PIR protocols, i.e., protocols which still work correctly even if only k out of ` servers are available during the protocols’ operation (the user does not know in advance which servers are available).
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A Modern Information Retrieval at a Glance

A Modern Information Retrieval at a Glance

Abstract - Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. Our proposed research work based upon the concept of web data mining. This is the core domain of proposed work. The Information retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on non- full-text. Information Aggregation is a service that gathers relevant information from multiple sources to provide convenience and add value by analyzing the aggregated information for specific objective using Internet Technologies. We call the providers of these services aggregators. In proposed work try to implement a new retrieval methods. In which work on the problem for comparative analysis of two or multiple websites on same desktop screen. That facility cannot provide by the existing search engines. We have try to create framework for open number of websites at a time for easy access of contents readings from that multiple websites.
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Transfer learning for information retrieval

Transfer learning for information retrieval

Ranking has laid the foundations of many fields, for example, Information Retrieval (IR) and Recommender Systems, as well as Question Answering (QA). For IR applications like search engines, the ranking system looks to return a permutation of documents ordered by their relevance to an information request, expressed in queries, submitted to the system. However, the relevance of a document to an information need is not straightforwardly expressed in the document. Instead, various ranking models, which include the BM25 [1] and language models [2, 3], have been developed to predict the relevance via a set of signals extracted from both the document and the query. However, it has repeatedly been demonstrated that the ranking e↵ectiveness of ranking models varies across di↵erent test collections [4–6]. The majority of existing ranking models have been developed based on empirical studies and require parameter tuning for specific corpus. Recent research on Learning to Ranking (L2R) [7] has made significant strides towards training ranking models via machine learning techniques. Note that L2R is not learning to optimise the parameters for existing models, but to train a ranking model that can achieve optimised ranking function for a specific task.
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Image based Information Retrieval

Image based Information Retrieval

2.2 Zobeir Raisi, Farahnaz Mohanna, Mehdi Rezaei ” A Journal on Content Based Information Retrieval on Tourism Applications ” In this paper they have discussed the techniques to retrieve images using all the three features shape , color, texture one by one. From this paper we have picked the study material for shape feature as our research paper is based on shape feature computation. Edge histogram descriptor (EHD) is bought in use. EHD represents the distribution of five types of edges in any local sub area of an image. The whole image is divided in 16 sub images and dominant edge is found out in each in sub image block. The five types of edges are horizontal, vertical, 45 degree diagonal, 135 degree diagonal and non- directional. If the maximum value among five edge magnitudes exceeds a particular threshold value then that sub image block is said to have that particular edge as its dominant edge, otherwise that block has no edge.
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Survey: Temporal Information Retrieval

Survey: Temporal Information Retrieval

Information retrieval is defined as “process of providing most relevant documents to the users from an existing collection”. Users request for data in the form of query typically in short textual form. In recent years, time has been acquiring increasing importance within search contexts, constructing to a new research area known as temporal information retrieval (TIR) that contains a number of different challenges. In recent years many researchers has taken interest in temporal information retrieval. Its aim is to improve the effectiveness of information retrieval methods by exploiting temporal information in documents and queries. T-IR aims to fulfil search needs by merging the traditional belief of document relevance with temporal relevance. For example, users may request for documents that contains the past information (e.g., information about historical figures); documents having the most new, up-to-date information (e.g., information about weather forecasts or currency rates); or even future-related information (e.g., information about planned events to be held in a certain area).
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Information Retrieval on the Internet

Information Retrieval on the Internet

The main components of a search engine are the Web crawler which has the task of collecting webpages and the Information Retrieval system which has the task of retrieving text documents that answer a user query. In this chapter we present approached to Web crawling, Information Retrieval models, and methods used to evaluate the retrieval performance. Practical considerations include information about existing IR systems and a detailed example of a large- scale search engine (Google), including the idea of ranking webpages by their importance (the Hubs an Authorities algorithm, and Google’s PageRank algorithm). Then we discuss the Invisible Web, the part of the Web that is not indexed by search engines. We briefly present other types of IR systems: digital libraries, multimedia retrieval systems (music, video, etc.), and distributed IR systems. We conclude with a discussion of the Semantic Web and future trends in visualizing search results and inputting queries in natural language.
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Sense Disambiguation in Information Retrieval

Sense Disambiguation in Information Retrieval

Due to global exchange of information, there has been a rapid expansion in availability of online texts. It has been a great deal to manage such vast repository of text and provide access to end user for accessing this repository. User always expects to get the most appropriate results. The work of searching is done by the search engines. Search engines help the user to get appropriate results according to user needs. For this purpose they adopt various methods and algorithms to rank the results. But what when the search string is ambiguous? For example check can refer to term check mate and check can also refer to verification of something. The task of Information Retrieval (IR) becomes quite complicated; also user may not get what he/she actually wants. Hence it becomes important to resolve the ambiguity for the user to get accurate results. In this paper we will discuss about the ambiguity problem faced by the search engines and propose an algorithm to resolve such an ambiguity.
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Effective Information Retrieval System

Effective Information Retrieval System

Abstract-- This paper provides some perspective on the effectiveness of information retrieval that had its beginnings long before the creation of the Internet and provides some enlightened predictions on possible future directions of the field. The field of Information Retrieval (IR) was born in the 1950s out of this necessity. Over the last forty years, the field has matured considerably. This paper presents an outline of ‘Effective Information Retrieval Systems’ seeking and searching, other aspects of information conflicting, showing the relationship between communication and information extraction in general with information seeking and information searching in information retrieval systems. It is also suggested that, within both information seeking research and information searching research, alternative information eliciting address similar issues in related ways and that the systems are complementary rather than conflicting. Finally, an alternative, problem-solving issues is presented, which, it is suggested, provides a basis for relating the design issues in appropriate research strategies. As we have learned how to handle text, through to the early adoption of computers to search for items that are relevant to a user’s query. The advances achieved by information retrieval researchers from the 1950s through to the present day are detailed next, focusing on the process of locating relevant information. This paper closes with speculation on where the future of information retrieval lies.
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Cross-lingual Information Retrieval

Cross-lingual Information Retrieval

SYSTRAN is one of the oldest machine translation companies. US government funded SYSTRAN in 1995 for developing a cross-language information retrieval system based on its natural language parsing and machine translation technology. SYSTRAN’s software which combines the rule- based and statistical machine translation delivers high quality translation for any domain [13]. SYSTRAN’s engine reduces the amount of data required to train the software and the size of the statistical models. The statistical technique learns from existing monolingual and bilingual collection to improve the translation process. PROMPT is another provider of machine translation technology. It provides some translation solutions such as the machine translation systems and services, dictionaries, translation memory systems, and also data mining systems [16]. PROMPT machine translation provides solutions for issues of dictionary volume and translation modules, and offers additional software tools for creating and editing for dictionaries, linguistic editor, interface, and post- editing tools.
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Teaching and learning in information retrieval

Teaching and learning in information retrieval

One particular issue is the extent to which the LIS and CS disciplines share ideas for curricula, to or to put it another way, how interdisciplinary is information retrieval? There are conflicting views on this issue. Rennie (1986) suggest that CS is “computer based researches” whereas LIS is the “mechanisation of library routines using computers” which influences the particular world view, e.g., CS curricula would refer to automatic indexing, whereas LIS would refer to manual indexing via thesauri. Poulter & Brunt (2007) would agree with this view in that the curricula focus in LIS is core skills for librarians, whereas CS looks for understanding of methods for ranking such as tf/idf. There are tensions between these approaches which come about because of the effect of new technologies which cause disintermediation, but which require more IS&R in curricula as a result. Salton (1969) argued that information science concepts in CS are useful – the focus was very much on processing at this stage (Atchison et al, 1968). More recently Croft (2003) in a keynote speech noted that IR has a very strong relationship with LIS, but the CS field is more dynamic and fast moving, which suggests that CS curricula would have to change more often. Saracevic & Dalbello (2001) use an interesting analogy, e.g. Venus vs. Mars – in the same planetary system but moving in different orbits. Their basic argument is that “educational needs differ significantly from education for LIS proper and CS proper” which infers less interdisciplinarity and more specialisation. An alternative view is put forward by Spink & Cool (1999) who argue that the “demand of digital librarians” …”may warrant restructuring of LIS and CS curricula” in order to provide development opportunities in both ‘technical and user aspects’ – perhaps ‘moons’ moving around the same planet. Coleman (2002) makes a very strong argument for interdisciplinarity in digital libraries. Further work includes Yang et al (2006) which takes the CS view (IEEE & ACM, 2001), but does include LIS elements e.g. search and evaluation, relevance in context of digital libraries as an important part of DL education, and Riesthuis (2002) who describes an interdisciplinary approach in an LIS department, but with a focus on CS and LIS issues.
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NOVEL APPROACH FOR INFORMATION RETRIEVAL

NOVEL APPROACH FOR INFORMATION RETRIEVAL

to users query. So this papers presents the collaboration of all these techniques that better works for retrieving the relevant information. It gives the technique for semantic search that is capable of exploiting concepts and relation between these concepts. Differential Adaptive Pointwise Mutual Information Retrieval that computes the semantic similarity between the querywords, keywords and content words differentially withheterogeneous thresholds is used to enhance the power of search system. It also includes the method that analyses user's behavior based on search intentions for information retrieval using the context search. Personalization is another tool that can be used to improve accuracy of search results based on user‟s search histories, user‟s interest and user‟s profile.
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Private  Stateful  Information  Retrieval

Private Stateful Information Retrieval

In this paper, we introduce the concept of a Private Stateful Information Retrieval (PSIR), that extends the concept of a PIR without losing any of the three desirable properties of PIR described above. We give implementations of PSIR that drastically reduce communication and the server’s computational overhead compared to PIR. Our main modification of PIR and PSIR lies in the fact that clients are stateful. That is, a client may store information between queries. The server of PSIR is stateless except for the database like in PIR. Despite having a state, clients of PSIR execute retrieval operations independently. Each retrieval only affects the state of the client performing the retrieval and does not impact other clients in any way. Together with the fact that the server is stateless, PSIR supports parallel access for multiple client without the need to deal with concurrency issues. Furthermore, the initial state of a client is obtained by the client by interacting solely with the server. Therefore, new clients (as well as crashed clients that lose their state) can enter a PSIR scheme at any time without affecting existing clients. For security, we wish to provide privacy for the client against both the server as well as other possibly adversarial clients. Formally, PSIR ensures that the identity of any records retrieved by any honest client remains private from the adversary even if the server is colluding with all other clients. Overall, PSIR maintains the three desirable properties of PIR while the availability of a client state in PSIR will significantly improve the efficiency of private retrievals as our constructions will show.
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INFORMATION RETRIEVAL

INFORMATION RETRIEVAL

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 Sibson taught me about the theory of automatic classification. Cyril Cleverdon is responsible for forcing me to think about evaluation. Mike Keen helped by providing data. Gerry Salton has influenced my thinking about IR considerably, mainly through his published work. Ken Moody had the knack of bailing me out when the going was rough and encouraging me to continue experimenting. Juliet Gundry is responsible for making the text more readable and clear. Bruce Croft, who read the final draft, made many useful comments. Ness Barry takes all the credit for preparing the manuscript. Finally, I am grateful to the Office of Scientific and Technical Information for funding most of the early experimental work on which the book is based; to the Kings College Research Centre for providing me with an environment in which I could think, and to the Department of Information Science at Monash University for providing me with the facilities for writing.
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Survey Paper on Information Retrieval Algorithms and Personalized Information Retrieval Concept

Survey Paper on Information Retrieval Algorithms and Personalized Information Retrieval Concept

With increasing data the need of its retrieval also arrived. To solve the need of retrieving the saved data a system was introduced called “information retrieval systems” (IRS). When given a query, the system gives results related to any word present in the Query. With time the system got evolved & undergone many changes. New methodologies were introduced to improve the system. But the biggest drawback of information retrieval system was that it gives thousands of results for a certain Query out of which only few are relevant and required by the user. This imprecision cause wastage of time and gives irrelevant data. The presence of that extra data can lead to skipping of the useful data.
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Learning representations for Information Retrieval

Learning representations for Information Retrieval

Information retrieval is generally concerned with answering questions such as: is this document relevant to this query? How similar are two queries or two doc- uments? How query and document similarity can be used to enhance relevance estimation? In order to answer these questions, it is necessary to access computa- tional representations of documents and queries. For example, similarities between documents and queries may correspond to a distance or a divergence defined on the representation space. It is generally assumed that the quality of the representation has a direct impact on the bias with respect to the true similarity, estimated by means of human intervention. Building useful representations for documents and queries has always been central to information retrieval research. The goal of this thesis is to provide new ways of estimating such representations and the relevance relationship between them. We present four articles that have been published in international conferences and one published in an information retrieval evaluation forum. The first two articles can be categorized as feature engineering approaches, which transduce a priori knowledge about the domain into the features of the rep- resentation. We present a novel retrieval model that compares favorably to existing models in terms of both theoretical originality and experimental effectiveness. The remaining two articles mark a significant change in our vision and originate from the widespread interest in deep learning research that took place during the time they were written. Therefore, they naturally belong to the category of representa- tion learning approaches, also known as feature learning. Differently from previous approaches, the learning model discovers alone the most important features for the task at hand, given a considerable amount of labeled data. We propose to model the semantic relationships between documents and queries and between queries them- selves. The models presented have also shown improved effectiveness on standard test collections. These last articles are amongst the first applications of representa- tion learning with neural networks for information retrieval. This series of research leads to the following observation: future improvements of information retrieval effectiveness has to rely on representation learning techniques instead of manually defining the representation space.
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Inferencing in Information Retrieval

Inferencing in Information Retrieval

INFERENCING IN INFORMATION RETRIEVAL I N F E R E N C I N G IN I N F O R M A T I O N RETRIEVAL A l e x a T M c C r a y N a t i o n a l L i b r a r y o f M e d i c i n e B e t h e s d a , M a r y l a n[.]

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The Study of Information Retrieval

The Study of Information Retrieval

The meaning of the term information retrieval can be very broad. Just getting a credit card out of your wallet so that you can type in the card number is a form of information retrieval. An information retrieval is a system where the end users extract information from www. However, as an academic field of study information retrieval might be defined as Information retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers).
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