Information access in context

Budzik, Jay; Hammond, Kristian J.; Birnbaum, Larry

doi:10.1016/s0950-7051(00)00105-2

Cited by 76 publications

(42 citation statements)

References 13 publications

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“…• Unsupervised Feedback: It applies blind relevance feedback, and typically assumes that the top k documents returned by a search process are relevant (e.g., [6]). This also known as pseudo-relevance feedback.…”

Section: Query Refinement and Context-based Retrievalmentioning

confidence: 99%

“…Automatic topical search refers to automatically formulating queries with terms extracted from a thematic context. The resources collected by the formulation of topical queries can be used in different scenarios, such as responding to contextualized information needs [24,7], fulfilling long term information needs [36], collecting resources for topical Web portals [10], or accessing the Deep Web [20], among others.…”

Section: Introductionmentioning

confidence: 99%

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Evolving Disjunctive and Conjunctive Topical Queries based on Multi-objective Optimization Criteria

Cecchini¹,

Lorenzetti²,

Maguitman³

2010

Int. Artif.

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In this work we propose techniques based on single-and multi-objective evolutionary algorithms to automatically evolve a population of topical queries. The developed techniques can be applied in the implementation of a topical search system. We report on the results of different strategies that attempt to evolve conjunctive and disjunctive queries. Our analysis reveals the limitations of the single-objective approach and highlights the advantages of applying multi-objective evolutionary algorithms for the problem at hand. In addition, we observe that disjunctive queries have the potential to achieve better retrieval performance than conjunctive queries. Finally, we show that the multi-objective evolutionary approach results in better performance than a baseline and other state-of-the-art techniques for query refinement.

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Section: Query Refinement and Context-based Retrievalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolving Disjunctive and Conjunctive Topical Queries based on Multi-objective Optimization Criteria

Cecchini¹,

Lorenzetti²,

Maguitman³

2010

Int. Artif.

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show abstract

“…Another system similar in purpose to our own is Watson [4], which suggests web pages to a computer user based on the documents currently open in a word processor or web browser. Watson uses a variety of heuristics to construct queries from the text of the documents, then sends these queries to the AltaVista search engine.…”

Section: Query-free Searchmentioning

confidence: 99%

Query-Free News Search

et al. 2005

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Many daily activities present information in the form of a stream of text, and often people can benefit from additional information on the topic discussed. TV broadcast news can be treated as one such stream of text; in this paper we discuss finding news articles on the web that are relevant to news currently being broadcast.We evaluated a variety of algorithms for this problem, looking at the impact of inverse document frequency, stemming, compounds, history, and query length on the relevance and coverage of news articles returned in real time during a broadcast. We also evaluated several postprocessing techniques for improving the precision, including reranking using additional terms, reranking by document similarity, and filtering on document similarity. For the best algorithm, 84-91% of the articles found were relevant, with at least 64% of the articles being on the exact topic of the broadcast. In addition, a relevant article was found for at least 70% of the topics.

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“…A person interested in finding out about the Benedictine order would use the document in a different context from a person interested in cooking, and in each context, there would be a different answer to the question "What is this document about?" Information retrieval agents generally retrieve based on document content [3,6,7,14,16], and commonly treat a document as an independent entity, taking into account its relationship to the entire corpus but not to the immediate group of recently-consulted documents within which it was accessed. For example, in TFIDF-based indexing, an index vector is created for each document; the magnitude of each element indicates how well that term represents the contents of the document, based on its frequency within the document and the inverse of the frequency of documents in the corpus containing that term.…”

Section: Content or Context?mentioning

confidence: 99%

Word Sieve: A Method for Real-Time Context Extraction

Bauer

Leake

2001

Modeling and Using Context

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Abstract. In order to be useful, intelligent information retrieval agents must provide their users with context-relevant information. This paper presents WordSieve, an algorithm for automatically extracting information about the context in which documents are consulted during web browsing. Using information extracted from the stream of documents consulted by the user, WordSieve automatically builds context profiles which differentiate sets of documents that users tend to access in groups. These profiles are used in a research-aiding system to index documents consulted in the current context and pro-actively suggest them to users in similar future contexts. In initial experiments on the capability to match documents to the task contexts in which they were consulted, WordSieve indexing outperformed indexing based on Term Frequency/Inverse Document Frequency, a common document indexing approach for intelligent agents in information retrieval.

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Information access in context

Cited by 76 publications

References 13 publications

Evolving Disjunctive and Conjunctive Topical Queries based on Multi-objective Optimization Criteria

Evolving Disjunctive and Conjunctive Topical Queries based on Multi-objective Optimization Criteria

Query-Free News Search

Word Sieve: A Method for Real-Time Context Extraction

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