Abstract
AbstractIn the early days of web search, a study by Craswell et al. [11] showed that anchor texts are particularly helpful ranking features for navigational queries and a study by Eiron and McCurley [24] showed that anchor texts closely resemble the characteristics of queries and that retrieval against anchor texts yields more homogeneous results than against documents. In this reproducibility study, we analyze to what extent these observations still hold in the web search scenario of the current MS MARCO dataset, including the paradigm shift caused by pre-trained transformers. Our results show that anchor texts still are particularly helpful for navigational queries, but also that they only very roughly resemble the characteristics of queries and that they now yield less homogeneous results than the content of documents. As for retrieval effectiveness, we also evaluate anchor texts from different time frames and include modern baselines in a comparison on the TREC 2019 and 2020 Deep Learning tracks. Our code and the newly created Webis MS MARCO Anchor Texts 2022 datasets are freely available.KeywordsAnchor textMS MARCOORCASTREC Deep Learning track
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
