BERT-QE: Contextualized Query Expansion for Document Re-ranking (Research Paper Walkthrough)

May 17, 2021

|

43 views

prakharmishra137

Details

#bert #informationretrieval #nlp Query expansion ( QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding. This paper introduces BERT to find relevant chunks from the documents as possible augmentations to the given query. ✔️ Automatic Python code generation from Spreadsheet - Mito - https://bit.ly/3eo4Wwi ⏩ Abstract: Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant information when expanding the query. To bridge this gap, inspired by recent advances in applying contextualized models like BERT to the document retrieval task, this paper proposes a novel query expansion model that leverages the strength of the BERT model to select relevant document chunks for expansion. In evaluation on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models. Please feel free to share out the content and subscribe to my channel :) ⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1 ⏩ OUTLINE: 0:00 - Background and Abstract 1:35 - Method Overview 2:30 - Step 1 in BERT-QE 4:32 - Step 2 in BERT-QE 6:40 - Step 3 in BERT-QE 8:15 - Entire Pipeline Summary ⏩ Paper Title: BERT-QE: Contextualized Query Expansion for Document Re-ranking ⏩ Paper: https://arxiv.org/pdf/2009.07258.pdf ⏩ Code: https://github.com/zh-zheng/BERT-QE ⏩ Author: Zhi Zheng, Kai Hui, Ben He, Xianpei Han, Le Sun, Andrew Yates ⏩ Organisation: University of Chinese Academy of Sciences, Amazon Alexa, Institute of Software, Chinese Academy of Sciences, Max Planck Institute for Informatics ⏩ IMPORTANT LINKS Full Playlist on BERT usecases in NLP: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f Full Playlist on Text Data Augmentation Techniques: https://www.youtube.com/watch?v=9O9scQb4sNo&list=PLsAqq9lZFOtUg63g_95OuV-R2GhV1UiIZ Full Playlist on Text Summarization: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f Full Playlist on Machine Learning with Graphs: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf Full Playlist on Evaluating NLG Systems: https://www.youtube.com/watch?v=-CIlz-5um7U&list=PLsAqq9lZFOtXlzg5RNyV00ueE89PwnCbu ********************************************* ⏩ Youtube - https://www.youtube.com/c/TechVizTheDataScienceGuy ⏩ Blog - https://prakhartechviz.blogspot.com ⏩ LinkedIn - https://linkedin.com/in/prakhar21 ⏩ Medium - https://medium.com/@prakhar.mishra ⏩ GitHub - https://github.com/prakhar21 ⏩ Twitter - https://twitter.com/rattller ********************************************* Please feel free to share out the content and subscribe to my channel :) ⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1 Tools I use for making videos :) ⏩ iPad - https://tinyurl.com/y39p6pwc ⏩ Apple Pencil - https://tinyurl.com/y5rk8txn ⏩ GoodNotes - https://tinyurl.com/y627cfsa #techviz #datascienceguy #ai #researchpaper #naturallanguageprocessing #ranking #ir

0:00 Background and Abstract 1:35 Method Overview 2:30 Step 1 in BERT-QE 4:32 Step 2 in BERT-QE 6:40 Step 3 in BERT-QE 8:15 Entire Pipeline Summary

Category: Research Paper

Comments

loading...

Reactions (0) | Note

📝 No reactions yet

Be the first one to share your thoughts!

Reactions(0)

Note

loading...

Recommended

ICAPS 2014: Christopher Expósito-Izquierdo on "Optimization Model and Heuristic Approach for ..."

ICAPS | Apr 16, 2015

ICAPS 2014: Iman Awaad on "Finding Ways to Get the Job Done: An Affordance-Based Approach"

ICAPS | Apr 20, 2015

ICAPS 2014 Invited Talk by Malik Ghallab

ICAPS | May 4, 2015

ICAPS 2014: Steven Levine on "Concurrent Plan Recognition and Execution for Human-Robot Teams"

ICAPS | May 4, 2015

ICAPS 2014: Peng Yu on "Resolving Uncontrollable Conditional Temporal Problems Using ..."

ICAPS | May 4, 2015