Focused retrieval and result aggregation with political data

Kaptein, Rianne; Marx, Maarten

doi:10.1007/s10791-010-9130-z

Cited by 16 publications

(7 citation statements)

References 25 publications

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“…At the same time, we began to find a greater number of authors who used multi-term tags. 12,13 Kim et al 14 built a graph in which both the nodes and the links between them, were formed by tag clouds and some authors such as Zubiaga 15 experimented by extracting tag clouds from a database with the articles from the collaborative encyclopaedia Wikipedia , showing that the use of a tag cloud improved navigation and search on this site.…”

Section: A Bit Of Historymentioning

confidence: 99%

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A survey of tag clouds as tools for information retrieval and content representation

Torres-Parejo

Campaña

Vila

et al. 2020

Information Visualization

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Tag clouds are tools that have been widely used on the Internet since their conception. The main applications of these textual visualizations are information retrieval, content representation and browsing of the original text from which the tags are generated. Despite the extensive use of tag clouds, their enormous popularity and the amount of research related to different aspects of them, few studies have summarized their most important features when they work as tools for information retrieval and content representation. In this paper we present a summary of the main characteristics of tag clouds found in the literature, such as their different functions, designs and negative aspects. We also present a summary of the most popular metrics used to capture the structural properties of a tag cloud generated from the query results, as well as other measures for evaluating the goodness of the tag cloud when it works as a tool for content representation. The different methods for tagging and the semantic association processes in tag clouds are also considered. Finally we give a list of alternative for visual interfaces, which makes this study a useful first help for researchers who want to study the content representation and information retrieval interfaces in greater depth.

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Section: A Bit Of Historymentioning

confidence: 99%

“…The exclusive use of single-terms in tag clouds limit their utility and effectiveness, 12,13,48 because they:…”

Section: The Multi-term Tag Cloud Versus the Single-term Tag Cloudmentioning

confidence: 99%

A survey of tag clouds as tools for information retrieval and content representation

Torres-Parejo

Campaña

Vila

et al. 2020

Information Visualization

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“…These approaches can sometimes be too specific, but they remain interesting to study because some of the work can be generalized to larger use. Kaptein and Marx [2010] investigate focused retrieval and result aggregation on political data. Their documents are long (50-80 pages), containing transcripts of the meetings of the Dutch Parliament.…”

Section: Domain-specific Applicationsmentioning

confidence: 99%

Aggregated search

2014

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Traditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents, and assemble information that fulfill his/her information need. Aggregated search represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, etc.) and relational content (similar entities, features) are included in search results. In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation, and question answering. Then we focus on more recent trends, namely cross vertical aggregated search and relational aggregated search, which are already present in current Web search.

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“…These meeting notes are long and well structured documents, and are therefore suitable for summarisation in the form of a word cloud. This chapter is based on work published in (Kaptein et al, 2010a;Kaptein and Marx, 2010). In this chapter we want to answer the following research question:…”

Section: Chapter 4: Wikipedia As a Pivot For Entity Rankingmentioning

confidence: 99%

Effective focused retrieval by exploiting query context and document structure

Kaptein

2012

SIGIR Forum

Self Cite

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The classic IR model of the search process consists of three elements: query, documents and search results. A user looking to fulfil an information need formulates a query usually consisting of a small set of keywords summarising the information need. The goal of an IR system is to retrieve documents containing information which might be useful or relevant to the user. Throughout the search process there is a loss of focus, because keyword queries entered by users often do not suitably summarise their complex information needs, and IR systems do not sufficiently interpret the contents of documents, leading to result lists containing irrelevant and redundant information. The main research objective of this thesis is to exploit query context and document structure to provide for more focused retrieval.The short keyword query used as input to the retrieval system can be supplemented with topic categories from structured Web resources such as DMOZ and Wikipedia. Topic categories can be used as query context to retrieve documents that are not only relevant to the query but also belong to a relevant topic category. Category information is especially useful for the task of entity ranking where the user is searching for a certain type of entity such as companies or persons. Category information can help to improve the search results by promoting in the ranking pages belonging to relevant topic categories, or categories similar to the relevant categories. By following external links and searching for the retrieved Wikipedia entities in a general Web collection, we can also exploit the structure of Wikipedia to rank entities on the general Web. Wikipedia, in contrast to the general Web, does not contain much redundant information. This absence of redundant information can be exploited by using Wikipedia as a pivot to search the general Web.A typical query returns thousands or millions of documents, but searchers hardly ever look beyond the first result page. Since space on the result page is limited, we can show only a few documents in the result list. Word clouds can be used to summarise groups of documents into a set of keywords which allows users to quickly get a grasp on the underlying data. Instead of using user-assigned tags we generate word clouds from the textual contents of documents themselves as well as the anchor text of Web documents. Improvements over word clouds that are created using simple term frequency counting include using a parsimonious term weighting scheme, including bigrams and biasing the word cloud towards the query. We find that word clouds can to a certain degree quickly convey the topic and relevance of a set of search results. Available online at:

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Focused retrieval and result aggregation with political data

Cited by 16 publications

References 25 publications

A survey of tag clouds as tools for information retrieval and content representation

A survey of tag clouds as tools for information retrieval and content representation

Aggregated search

Effective focused retrieval by exploiting query context and document structure

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