Combining multiple evidence from different types of thesaurus for query expansion

Mandala, Rila; Tokunaga, Takenobu; Hozumi, Takeshi

doi:10.1145/312624.312677

Cited by 102 publications

(69 citation statements)

References 21 publications

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“…Mandalas performed an in depth comparison between a variety of term expansion methods and combination approaches in [16] and found co-occurrence to be the most effective. There seems to be little doubt that generally speaking co-occurrence is the most effective way of increasing query relevance through term expansion.…”

Section: Term Expansionmentioning

confidence: 99%

A semiotic approach for the generation of themed photo narratives

Hargood

Millard

Weal

2010

Proceedings of the 21st ACM Conference on Hypertext and Hypermedia

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A wide variety of systems could be considered 'narrative systems', either directly working towards generating rich narratives or, more frequently, because they present or handle information in a narrative context. These narratives, generated or otherwise handled, may contain themes; an essential part of the subtext of narrative communicating important concepts outside the capabilities of the literal meaning of the content and forming the thematic cohesion that aids the flow of the presented narrative. However despite this very little work has been undertaken to understand of take advantage of these themes, particularly in narrative generation where the presence of well defined themes may improve the richness of those generated narratives. In this paper we evaluate the performance of a system utilising a thematic model in order to generate simple narratives in the form of photo montages compared to a keyword based system that does not. The experiment demonstrates that the system utilising the thematic model is capable of successfully connoting themes within these narratives. It also shows that the relevance of the resulting narratives to the titles used to generate them is higher in the thematic system than those generated by the other system.

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Section: Term Expansionmentioning

confidence: 99%

A semiotic approach for the generation of themed photo narratives

Hargood

Millard

Weal

2010

Proceedings of the 21st ACM Conference on Hypertext and Hypermedia

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“…Mandala et al [31] proposed a method to combine three different thesaurus types for query expansion. Bodner et al [27] combined WordNet and co-occurrence based thesauri for query expansion.…”

Section: Related Workmentioning

confidence: 99%

Using Dempster-Shafer’s Evidence Theory for Query Expansion Based on Freebase Knowledge

Pan

Zhang

et al. 2013

Information Retrieval Technology

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Abstract. Query expansion is generally a useful technique in improving search performance. However, some expanded query terms obtained by traditional statistical methods (e.g., pseudo-relevance feedback) may not be relevant to the user's information need, while some relevant terms may not be contained in the feedback documents at all. Recent studies utilize external resources to detect terms that are related to the query, and then adopt these terms in query expansion. In this paper, we present a study in the use of Freebase [6], which is an open source general-purpose ontology, as a source for deriving expansion terms. FreeBase provides a graphbased model of human knowledge, from which a rich and multi-step structure of instances related to the query concept can be extracted, as a complement to the traditional statistical approaches to query expansion. We propose a novel method, based on the well-principled DempsterShafer's (D-S) evidence theory, to measure the certainty of expansion terms from the Freebase structure. The expanded query model is then combined with a state of the art statistical query expansion model -the Relevance Model (RM3). Experiments show that the proposed method achieves significant improvements over RM3.

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“…ఴ హనమఽ(vAhanamu)/vehicle etc. It's observed that the thesaurus-based QE techniques have not been as successful [8].…”

Section: A Thesaurusmentioning

confidence: 99%

Pseudo Relevance Feedback by linking WordNet for Expanding Queries in Information Retrieval Process

Kolikipogu¹,

Rani²,

Swapna³

2013

IJMO

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Abstract-Vocabulary and Word mismatches are common problems in Information Retrieval Systems. Query Expansion(QE) gives a solution to these problems. Selection of key terms to expand the root query is a challenging task. Semantic relationships of a particular term in WordNet will improve the performance. Pseudo Relevance Feedback is one of the proven methods of QE which consider relevant terms to be expanded. Initial retrieved Items are considered as relevant to the query and further used to expand the query. Whenever the sense of key term is found, its lexical relationship synonym is considered for QE. The specific terms to be included in the initial query are selected automatically and the terms selected for the initial query are connected before expansion with OR Boolean operator. We found there is no Telugu WordNet available hence the proposed system is aimed to test on hand-crafted WordNet on limited text collection for Telugu language.Index Terms-Query expansion, pseudo relevance feedback, wordnet, synset, word sense disambiguation, parts of speech, normalization.

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Combining multiple evidence from different types of thesaurus for query expansion

Cited by 102 publications

References 21 publications

A semiotic approach for the generation of themed photo narratives

A semiotic approach for the generation of themed photo narratives

Using Dempster-Shafer’s Evidence Theory for Query Expansion Based on Freebase Knowledge

Pseudo Relevance Feedback by linking WordNet for Expanding Queries in Information Retrieval Process

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