Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evaluating Elements of Web-based Data Enrichment for Pseudo-Relevance
  Feedback Retrieval   In this work, we analyze a pseudo-relevance retrieval method based on the
results of web search engines. By enriching topics with text data from web
search engine result pages and linked contents, we train topic-specific and
cost-efficient classifiers that can be used to search test collections for
relevant documents. Building upon attempts initially made at TREC Common Core
2018 by Grossman and Cormack, we address questions of system performance over
time considering different search engines, queries, and test collections. Our
experimental results show how and to which extent the considered components
affect the retrieval performance. Overall, the analyzed method is robust in
terms of average retrieval performance and a promising way to use web content
for the data enrichment of relevance feedback methods.

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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: q-alg
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The categories of this paper are: physics
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat