Felice Antonio Merra
ABSTRACT
Product search engines (PSEs) play an essential role in retail websites as they make it easier for users to retrieve relevant products within large catalogs. Despite the continuous progress that has led to increasingly accurate search engines, a limited focus has been given to their performance on queries with negations. Indeed, while we would expect to retrieve different products for the queries “iPhone 13 cover with ring” and “iPhone 13 cover without ring”, this does not happen in popular PSEs with the latter query containing results with the unwanted ring component. The limitation of modern PSEs in understanding negations motivates the need for further investigation.
In this work, we start by defining the negation intent in users queries. Then, we design a transformer-based model, named Negation Detector for Queries (ND4Q), that reaches optimal performance in negation detection (+95% on accuracy metrics). Finally, having built the first negation detector for product search queries, we propose a negation-aware filtering strategy, named Filtering Irrelevant Products (FIP). The promising experimental results in improve the PSE relevance performance using FIP (+9.41% on nDCG@16 for queries where the negation starts with "without") pave the way to additional research effort towards negation-aware PSEs.
- Ali Ahmadvand, Surya Kallumadi, Faizan Javed, and Eugene Agichtein. 2020. JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search. In SIGIR. ACM, 1509–1512.Google Scholar
- Mordechai Averbuch, Tom H. Karson, Benjamin Ben-Ami, Oded Maimon, and Lior Rokach. 2004. Context-Sensitive Medical Information Retrieval. In MedInfo(Studies in Health Technology and Informatics, Vol. 107). IOS Press, 282–286.Google Scholar
- Brian E. Chapman, Sean Lee, Hyunseok Peter Kang, and Wendy Webber Chapman. 2011. Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm. J. Biomed. Inf. 44, 5 (2011), 728–737.Google ScholarDigital Library
- Wendy Webber Chapman, Will Bridewell, Paul Hanbury, Gregory F. Cooper, and Bruce G. Buchanan. 2001. A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. J. Biomed. Inf. 34, 5 (2001), 301–310.Google ScholarCross Ref
- Isaac G. Councill, Ryan T. McDonald, and Leonid Velikovich. 2010. What’s great and what’s not: learning to classify the scope of negation for improved sentiment analysis. In NeSp-NLP@ACL. University of Antwerp, 51–59.Google Scholar
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT (1). Association for Computational Linguistics, 4171–4186.Google Scholar
- Noa P. Cruz Díaz, Maite Taboada, and Ruslan Mitkov. 2016. A machine-learning approach to negation and speculation detection for sentiment analysis. J. Assoc. Inf. Sci. Technol. 67, 9 (2016), 2118–2136.Google ScholarDigital Library
- Alex Graves. 2012. Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence, Vol. 385. Springer.Google Scholar
- Aditya Khandelwal and Suraj Sawant. 2020. NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution. In LREC. European Language Resources Association, 5739–5748.Google Scholar
- Bevan Koopman and Guido Zuccon. 2014. Understanding negation and family history to improve clinical information retrieval. In SIGIR. ACM, 971–974.Google Scholar
- Lydia Lazib, Yanyan Zhao, Bing Qin, and Ting Liu. 2016. Negation Scope Detection with Recurrent Neural Networks Models in Review Texts. In ICYCSEE (1), Vol. 623. Springer, 494–508.Google Scholar
- Nut Limsopatham, Craig Macdonald, Richard McCreadie, and Iadh Ounis. 2012. Exploiting term dependence while handling negation in medical search. In SIGIR. ACM, 1065–1066.Google Scholar
- Aritra Mandal, Ishita K. Khan, and Prathyusha Senthil Kumar. 2019. Query Rewriting using Automatic Synonym Extraction for E-commerce Search. In eCOM@SIGIR(CEUR Workshop Proceedings, Vol. 2410). CEUR-WS.org.Google Scholar
- Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to information retrieval. Cambridge University Press.Google Scholar
- Saeed Mehrabi, Anand Krishnan, Sunghwan Sohn, Alexandra M. Roch, Heidi Schmidt, Joe Kesterson, Chris Beesley, Paul R. Dexter, C. Max Schmidt, Hongfang Liu, and Mathew J. Palakal. 2015. DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx. J. Biomed. Inf. (2015).Google Scholar
- Roser Morante and Eduardo Blanco. 2012. *SEM 2012 Shared Task: Resolving the Scope and Focus of Negation. In *SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012). Association for Computational Linguistics, Montréal, Canada, 265–274. https://aclanthology.org/S12-1035Google Scholar
- Priyanka Nigam, Yiwei Song, Vijai Mohan, Vihan Lakshman, Weitian Allen Ding, Ankit Shingavi, Choon Hui Teo, Hao Gu, and Bing Yin. 2019. Semantic Product Search. In KDD. ACM, 2876–2885.Google Scholar
- Zhong Qian, Peifeng Li, Qiaoming Zhu, Guodong Zhou, Zhunchen Luo, and Wei Luo. 2016. Speculation and Negation Scope Detection via Convolutional Neural Networks. In EMNLP. The Association for Computational Linguistics, 815–825.Google Scholar
- Lance A. Ramshaw and Mitch Marcus. 1995. Text Chunking using Transformation-Based Learning. In VLC@ACL.Google Scholar
- Stephen E. Robertson and Hugo Zaragoza. 2009. The Probabilistic Relevance Framework: BM25 and Beyond. Found. Trends Inf. Retr. 3, 4 (2009), 333–389.Google ScholarDigital Library
- Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs/1910.01108 (2019).Google Scholar
- Daria Sorokina and Erick Cantú-Paz. 2016. Amazon Search: The Joy of Ranking Products. In SIGIR. ACM, 459–460.Google ScholarDigital Library
- Andrew Trotman, Jon Degenhardt, and Surya Kallumadi. 2017. The Architecture of eBay Search. In eCOM@SIGIR(CEUR Workshop Proceedings, Vol. 2311).Google Scholar
- Quoc-Tuan Truong, Tong Zhao, Changhe Yuan, Jin Li, Jim Chan, Soo-Min Pantel, and Hady W. Lauw. 2022. AmpSum: Adaptive Multiple-Product Summarization towards Improving Recommendation Captions. In WWW. ACM, 2978–2988.Google Scholar
- Peter D. Turney and Michael L. Littman. 2002. Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus. CoRR cs.LG/0212012 (2002).Google Scholar
- Jun Yan, Nasser Zalmout, Yan Liang, Christan Grant, Xiang Ren, and Xin Luna Dong. 2021. AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding. In ACL/IJCNLP (1). ACL, 4694–4705.Google Scholar
Index Terms
- Improving the Relevance of Product Search for Queries with Negations
Recommendations
Paraconsistent Double Negations as Classical and Intuitionistic Negations
A classical paraconsistent logic (CP), which is regarded as a modified extension of first-degree entailment logic, is introduced as a Gentzen-type sequent calculus. This logic can simulate the classical negation in classical logic by paraconsistent ...
Integrating collaborative filtering and matching-based search for product recommendations
Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good ...
Diversifying product search results
SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information RetrievalIn recent years, online shopping is becoming more and more popular. Users type keyword queries on product search systems to find relevant products, accessories, and even related products. However, existing product search systems always return very ...
Comments