RUCIR at TREC 2019: Conversational Assistance Track
28th Text REtrieval Conference, TREC 2019 - Proceedings, ISSN: 2573-0142, Vol: 5, Issue: CSCW1
2019
- 59Citations
- 642Usage
- 3,032Captures
- 2Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations59
- Citation Indexes40
- CrossRef40
- 32
- Patent Family Citations16
- Patent Families16
- Policy Citations3
- Policy Citation3
- Usage642
- Downloads569
- Abstract Views73
- Captures3,032
- Readers3,032
- Mentions2
- News Mentions2
- News2
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Conference Paper Description
In this paper we give a brief overview of the RUCIR group's participation in the TREC 2019 Conversational Assistance Track. All our runs for the Conversational Assistance Track are on the full MS MARCO Conversational Search Sessions dataset and use the online Indri retrieval system hosted at CMU. For the Conversational Assistance Track, our runs try to solve conversational retrieval problems from two directions: One is to improve the search results by modifying the user's current query, including query reference resolution and incorporate the information from user's history queries in the same session. Run 1, Run 2 and Run 4 use this method. The other direction is to design a neural network to model user's global search intent and current search intent to get the retrieval results and run3 uses this method.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85180123124&origin=inward; http://dx.doi.org/10.1145/1122445.1122456; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128542047&origin=inward; https://dl.acm.org/doi/10.1145/1122445.1122456; https://ecommons.luc.edu/cs_facpubs/289; https://ecommons.luc.edu/cgi/viewcontent.cgi?article=1291&context=cs_facpubs; https://pearl.plymouth.ac.uk/ada-research/124; https://pearl.plymouth.ac.uk/cgi/viewcontent.cgi?article=1123&context=ada-research; https://zenodo.org/record/4081819
Association for Computing Machinery (ACM)
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