Improving weak ad-hoc queries using wikipedia asexternal corpus

Li, Yinghao; Luk, Robert W. P.; Ho, Kei Shiu; Chung, Fu-Lai

doi:10.1145/1277741.1277914

Cited by 74 publications

(68 citation statements)

References 9 publications

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“…Data sources like Wikipedia (Li et al, 2007;Suchanek et al, 2007), WordNet (Liu et al, 2004;Suchanek et al, 2007), news corpora or even the Web itself (Diaz, Metzler, 2006) were used separately for enhancing search performances. Diaz and Metzler investigated in (Diaz, Metzler, 2006) the use of large and general external resources.…”

Section: Related Workmentioning

confidence: 99%

Accurate and effective latent concept modeling for ad hoc information retrieval

Deveaud¹,

SanJuan²,

Bellot³

2014

Document numérique

396

253

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ABSTRACT. A keyword query is the representation of the information need of a user, and is the result of a complex cognitive process which often results in under-specification. We propose an unsupervised method namely Latent Concept Modeling (LCM) for mining and modeling latent search concepts in order to recreate the conceptual view of the original information need. We use Latent Dirichlet Allocation (LDA) to exhibit highly-specific query-related topics from pseudo-relevant feedback documents. We define these topics as the latent concepts of the user query. We perform a thorough evaluation of our approach over two large ad-hoc TREC collections. Our findings reveal that the proposed method accurately models latent concepts, while being very effective in a query expansion retrieval setting.RÉSUMÉ. Une requête est la représentation du besoin d'information d'un utilisateur, et est le résultat d'un processus cognitif complexe qui mène souvent à un mauvais choix de mots-clés. Nous proposons une méthode non supervisée pour la modélisation de concepts implicites d'une requête, dans le but de recréer la représentation conceptuelle du besoin d'information initial. Nous utilisons l'allocation de Dirichlet latente (LDA) pour détecter les concepts implicites de la requête en utilisant des documents pseudo-pertinents. Nous évaluons cette méthode en profondeur en utilisant deux collections de test de TREC. Nous trouvons notamment que notre approche permet de modéliser précisément les concepts implicites de la requête, tout en obtenant de bonnes performances dans le cadre d'une recherche de documents.

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Section: Related Workmentioning

confidence: 99%

Accurate and effective latent concept modeling for ad hoc information retrieval

Deveaud¹,

SanJuan²,

Bellot³

2014

Document numérique

396

253

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“…In [6], authors explored Wikipedia as an external corpus for expanding the original query to improve weak queries. It gave some good results over PRF method.…”

Section: Related Workmentioning

confidence: 99%

Advances in IR Query Expansion

Singh

Ragha

2018

IJARCSSE

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Abstract-As the internet is becoming rich with huge information, retrieving the wanted information is a huge challenge. Due to huge variation in framing a query by the individuals, matching of the terms with the document to be searched are facing many challenges. Researchers have worked on problem of identifying the correct essence of the user query by adding additional useful terms. They are currently working on this kind of Query Expansion (QE I. INTRODUCTIONIn our day to day life, fetching significant amount of data for academic, administrative and commercial purposes have increased. Therefore the need of the query is required to get some information for solving problems from which user can reach a certain conclusion. The role of the query is same like asking the question to search engine like google, yahoo, bing, etc. The query can be related to government, academic, commercial or personal from which doubts can be get cleared or suggestions with deep knowledge can be gained. In short, the query is something which comes in human mind. The search engine is designed to search for information where search results are the number of pages consisting of different forms of files. Among these pages, the aim is to get query related documents only.Information Retrieval [1] as IR is an activity of finding useful matter of an unstructured nature usually text within the large collection of documents stored on the computer which satisfies users need. The aim is to achieve query related documents only. Unstructured data is not organized in a pre-defined manner. The query given by a user to information retrieval can be ambiguous which may result in unrelated or irrelevant documents. The query given by user can be ambiguous due to various reasons like User lack of knowledge, Word with more than one sense, forming query in an inappropriate way, Vocabulary Mismatch problem(VMP) [2]. It is faced while matching a query with documents which is the central problem in IR which occurs when the term of the query doesn't match with the terms in relevant documents.Information storage and retrieval is a systematic storage of data collection which should be displayed on request. Relevance is a fundamental concept in Information Retrieval. Relevance is something which is related to the user query. There are many factors that go into an individual's selection as to whether a particular document is relevant or nonrelevant. It sounds simple but just by comparing and matching the keywords of a query with documents is not sufficient in terms of relevance and yields poor results. Thus considering all these factors one may design an algorithm to achieve more suitable results for the requested query.Section II describes the research work in the area of Query Expansion (QE). Different techniques are discussed in section III. An endeavour to compare different techniques briefly in section IV. A proposed method in section V. Section VI concludes the survey of different methods.

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“…The ones most relevant to our work are [18,76,88,107,134,138]. For a thorough overview of the area of Wikipedia mining, we refer to the survey by Medelyan et al [86].…”

Section: Related Workmentioning

confidence: 99%

“…There is some work that exploits Wikipedia for query expansion. In [76], they proposed to improve the retrieval effectiveness of ad-hoc queries using a local repository of Wikipedia as an external corpus. They analyzed the categorical information in each Wikipedia article, and select terms from top-k articles to expand a query.…”

Section: Related Workmentioning

confidence: 99%

Time-aware approaches to information retrieval

Kanhabua

2012

SIGIR Forum

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In this thesis, we address major challenges in searching temporal document collections. In such collections, documents are created and/or edited over time. Examples of temporal document collections are web archives, news archives, blogs, personal emails and enterprise documents. Unfortunately, traditional IR approaches based on term-matching only can give unsatisfactory results when searching temporal document collections. The reason for this is twofold: the contents of documents are strongly time-dependent, i.e., documents are about events happened at particular time periods, and a query representing an information need can be time-dependent as well, i.e., a temporal query.Our contributions in this thesis are different time-aware approaches within three topics in IR: content analysis, query analysis, and retrieval and ranking models. In particular, we aim at improving the retrieval effectiveness by 1) analyzing the contents of temporal document collections, 2) performing an analysis of temporal queries, and 3) explicitly modeling the time dimension into retrieval and ranking.Leveraging the time dimension in ranking can improve the retrieval effectiveness if information about the creation or publication time of documents is available. In this thesis, we analyze the contents of documents in order to determine the time of non-timestamped documents using temporal language models. We subsequently employ the temporal language models for determining the time of implicit temporal queries, and the determined time is used for re-ranking search results in order to improve the retrieval effectiveness.We study the effect of terminology changes over time and propose an approach to handling terminology changes using time-based synonyms. In addition, we propose different methods for predicting the effectiveness of temporal queries, so that a particular query enhancement technique can be performed to improve the overall performance. When the time dimension is incorporated into ranking, documents will be ranked according to both textual and temporal similarity. In this case, time uncertainty should also be taken into account. Thus, we propose a ranking model that considers the time uncertainty, and improve ranking by combining multiple features using learning-to-rank techniques.Through extensive evaluation, we show that our proposed time-aware approaches outperform traditional retrieval methods and improve the retrieval effectiveness in searching temporal document collections.

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Improving weak ad-hoc queries using wikipedia asexternal corpus

Cited by 74 publications

References 9 publications

Accurate and effective latent concept modeling for ad hoc information retrieval

Accurate and effective latent concept modeling for ad hoc information retrieval

Advances in IR Query Expansion

Time-aware approaches to information retrieval

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