# Towards Proactive Information Retrieval in Noisy Text with Wikipedia Concepts

arXiv: https://arxiv.org/abs/2210.09877 · HTML: https://mlsanity.com/paper/2210.09877/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Declarative Experimentation in Information Retrieval using PyTerrier](https://mlsanity.com/paper/2007.14271/) (2020)
- [TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources](https://mlsanity.com/paper/1911.09471/) (2019)
- [Goal-based Course Recommendation](https://mlsanity.com/paper/1812.10078/) (2018)
- [TREC 2020 Podcasts Track Overview](https://mlsanity.com/paper/2103.15953/) (2021)
- [PEEK: A Large Dataset of Learner Engagement with Educational Videos](https://mlsanity.com/paper/2109.03154/) (2021)
- [iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration](https://mlsanity.com/paper/2109.11621/) (2021)
- [Towards an Integrative Educational Recommender for Lifelong Learners](https://mlsanity.com/paper/1912.01592/) (2019)
- [Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution](https://mlsanity.com/paper/2112.02034/) (2021)
- [On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering](https://mlsanity.com/paper/2209.12944/) (2022)
- [A new approach for query expansion using Wikipedia and WordNet](https://mlsanity.com/paper/1901.10197/) (2019)
- [Personalized Entity Search by Sparse and Scrutable User Profiles](https://mlsanity.com/paper/2109.04713/) (2021)

## Built on by (most cited first, 1 of 1)

- [Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection](https://mlsanity.com/paper/2307.16816/) (2023)

## Abstract (arXiv)

Extracting useful information from the user history to clearly understand informational needs is a crucial feature of a proactive information retrieval system. Regarding understanding information and relevance, Wikipedia can provide the background knowledge that an intelligent system needs. This work explores how exploiting the context of a query using Wikipedia concepts can improve proactive information retrieval on noisy text. We formulate two models that use entity linking to associate Wikipedia topics with the relevance model. Our experiments around a podcast segment retrieval task demonstrate that there is a clear signal of relevance in Wikipedia concepts while a ranking model can improve precision by incorporating them. We also find Wikifying the background context of a query can help disambiguate the meaning of the query, further helping proactive information retrieval.