A Recursive Approach to Entity Ranking and List Completion Using Entity Determining Terms, Qualifiers and Prominent n-Grams

Ramanathan, Madhu; Rajagopal, Srikant; Karthik, V; Murugeshan, Meenakshi Sundaram; Mukherjee, Saswati

doi:10.1007/978-3-642-14556-8_29

Cited by 2 publications

(4 citation statements)

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“…Many of them (Balog, Bron, and de Rijke 2010; 2011; Kaptein and Kamps 2013) model category matching using generative language models. Solutions (Balog et al 2009a;Ramanathan et al 2009) achieving top results in INEX 2009 entity track (Demartini, Iofciu, and de Vries 2009) all treat category matching as an important component in their ranking functions. In particular, a linear combination of term-based model and category-based model is proposed in (Balog et al 2009a;Balog, Bron, and de Rijke 2011).…”

Section: Related Workmentioning

confidence: 99%

“…A category (either in C or C e ) is assumed as a phrase specifying a type of entities. It can be further interpreted as a head word plus some additional qualifiers (Ramanathan et al 2009). The head word typically specifies a general category, and the qualifiers restrict the general category so that it can be more specific.…”

Section: Category Matchingmentioning

confidence: 99%

“…However, such a way of hybrid may not be effective enough because of the instinctive distinction between the two models. Their reported results show that the achieved precision is still not good enough (Balog et al 2009a;Raviv, Carmel, and Kurland 2012), when fairly compared with an alternative (Ramanathan et al 2009) of the INEX 2009 entity ranking task.…”

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

Improving Context and Category Matching for Entity Search

Chen

Gao

Shi

et al. 2014

AAAI

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Entity search is to retrieve a ranked list of named entities of target types to a given query. In this paper, we propose an approach of entity search by formalizing both context matching and category matching. In addition, we propose a result re-ranking strategy that can be easily adapted to achieve a hybrid of two context matching strategies. Experiments on the INEX 2009 entity ranking task show that the proposed approach achieves a significant improvement of the entity search performance (xinfAP from 0.27 to 0.39) over the existing solutions.

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Section: Related Workmentioning

confidence: 99%

Section: Category Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Improving Context and Category Matching for Entity Search

Chen

Gao

Shi

et al. 2014

AAAI

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“…One approach used the relevance assessments available from prior years, promoting documents previously assessed as relevant, achieving xin-fAP scores up to 0.517 (Balog et al, 2010b). Ramanathan et al (2010) combine a number of expansion and matching techniques based on the page titles, categories and extracted entities and n-grams. An initial set of relevant documents is recursively expanded using the document titles, category information, proximity information and the prominent n-grams.…”

Section: Comparison To Other Approachesmentioning

confidence: 99%

Effective focused retrieval by exploiting query context and document structure

Kaptein

2012

SIGIR Forum

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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:

show abstract

A Recursive Approach to Entity Ranking and List Completion Using Entity Determining Terms, Qualifiers and Prominent n-Grams

Cited by 2 publications

References 5 publications

Improving Context and Category Matching for Entity Search

Improving Context and Category Matching for Entity Search

Effective focused retrieval by exploiting query context and document structure

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