ECNLP 3 the Workshop on E-Commerce and NLP Proceedings
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ECNLP 3 The Workshop on e-Commerce and NLP Proceedings of the Third Workshop July 10, 2020 c 2020 The Association for Computational Linguistics Order copies of this and other ACL proceedings from: Association for Computational Linguistics (ACL) 209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 [email protected] ISBN 978-1-952148-21-7 ii Introduction It is our great pleasure to welcome you to the Third International Workshop on e-Commerce and NLP (ECNLP). This workshop focuses on intersection of Natural Language Processing (NLP) and e-Commerce. NLP and information retrieval (IR) have been powering e-Commerce applications since the early days of the fields. Today, NLP and IR already play a significant role in e-Commerce tasks, including product search, recommender systems, product question answering, machine translation, sentiment analysis, product description and review summarization, and customer review processing. With the exploding popularity of chatbots and shopping assistants – both text- and voice-based – NLP, IR, question answering, and dialogue systems research is poised to transform e-Commerce once again. The ECNLP workshop series was designed to provide a venue for the dissemination of late-breaking research results and ideas related to e-commerce and online shopping, as well as a forum where new and unfinished ideas could be discussed. After the successful ECNLP workshops at The Web Conference in 2019 and 2020, we are happy to host ECNLP 3 at ACL 2020 and once again bring together researchers from both academia and industry. We have received a larger number of submissions than we could accept for presentation. ECNLP 3 received 21 submissions of long and short research papers. In total, ECNLP 3 featured 13 accepted papers (62% acceptance rate). The selection process was competitive and we believe it resulted in a balanced and varied program that is appealing to audiences from the various sub-areas of e-Commerce. We would like to thank everyone who submitted a paper to the workshop. We would also like to express our gratitude to the members of the Program Committee for their timely reviews, and for supporting the tight schedule by providing reviews at short notice. We hope that you enjoy the workshop! The ECNLP Organizers May 2020 iii Organizers: Shervin Malmasi (Amazon, USA) Surya Kallumadi (The Home Depot, USA) Nicola Ueffing (eBay Inc, Germany) Oleg Rokhlenko (Amazon, USA) Eugene Agichtein (Emory University, USA) Ido Guy (eBay Inc, Israel) Program Committee: Amita Misra (IBM) Arushi Prakash (Zulily) Besnik Fetahu (L3S Research Center / Leibniz University of Hannover) Cheng Wang (NEC Laboratories Europe) David Martins de Matos (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa) Debanjan Mahata (Bloomberg) Emerson Paraiso (Pontificia Universidade Catolica do Parana) Giuseppe Castellucci (Amazon) Gregor Leusch (eBay) Irina Temnikova (Sofia University, Bulgaria) Jacopo Tagliabue (Coveo) Lesly Miculicich (École polytechnique fédérale de Lausanne - Idiap) Mladen Karan (TakeLab, University of Zagreb) Raksha Sharma (IIT Roorkee) Simone Filice (Amazon) Wai Lam (The Chinese University of Hong Kong) Xiaoting Zhao (Etsy) Yitong Li (The University of Melbourne) v Table of Contents Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning Hanchu Zhang, Leonhard Hennig, Christoph Alt, Changjian Hu, Yao Meng and Chao Wang. .1 How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead Jacopo Tagliabue, Bingqing Yu and Marie Beaulieu . .7 Deep Learning-based Online Alternative Product Recommendations at Scale Mingming Guo, Nian Yan, Xiquan Cui, San He Wu, Unaiza Ahsan, Rebecca West and Khalifeh Al Jadda.......................................................................................19 A Deep Learning System for Sentiment Analysis of Service Calls YananJia.............................................................................. 24 Using Large Pretrained Language Models for Answering User Queries from Product Specifications Kalyani Roy, Smit Shah, Nithish Pai, Jaidam Ramtej, Prajit Nadkarni, Jyotirmoy Banerjee, Pawan Goyal and Surender Kumar . 35 Improving Intent Classification in an E-commerce Voice Assistant by Using Inter-Utterance Context Arpit Sharma . 40 Semi-Supervised Iterative Approach for Domain-Specific Complaint Detection in Social Media Akash Gautam, Debanjan Mahata, Rakesh Gosangi and Rajiv Ratn Shah . 46 Item-based Collaborative Filtering with BERT Tian Wang and Yuyangzi Fu . 54 Semi-supervised Category-specific Review Tagging on Indonesian E-Commerce Product Reviews Meng Sun, Marie Stephen Leo, Eram Munawwar, Paul C. Condylis, Sheng-yi Kong, Seong Per Lee, Albert Hidayat and Muhamad Danang Kerianto . 59 Deep Hierarchical Classification for Category Prediction in E-commerce System DehongGao........................................................................... 64 SimsterQ: A Similarity based Clustering Approach to Opinion Question Answering Aishwarya Ashok, Ganapathy Natarajan, Ramez Elmasri and Laurel Smith-Stvan . 69 e-Commerce and Sentiment Analysis: Predicting Outcomes of Class Action Lawsuits Stacey Taylor and Vlado Keselj . 77 On Application of Bayesian Parametric and Non-parametric Methods for User Cohorting in Product Search Shashank Gupta . 86 vii Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning Hanchu Zhang1 Leonhard Hennig2 Christoph Alt2 Changjian Hu1 Yao Meng1 Chao Wang1 1Lenovo Research Artificial Intelligence Lab 2German Research Center for Artificial Intelligence (DFKI) zhanghc9,hucj1,mengyao1,wangchao31 @lenovo.com { leonhard.hennig,christoph.alt @dfki.de} { } Abstract pressed as complex noun phrases instead of proper names. Product and component category terms Named Entity Recognition (NER) in domains are often combined with brand names, model num- like e-commerce is an understudied prob- bers, and attributes (“hard drive” “SSD hard lem due to the lack of annotated datasets. → drive” “WD Blue 500 GB SSD hard drive”), Recognizing novel entity types in this do- → main, such as products, components, and at- which are almost impossible to enumerate exhaus- tributes, is challenging because of their lin- tively. In such a low-coverage setting, employing a guistic complexity and the low coverage of ex- simple dictionary-based approach would result in isting knowledge resources. To address this very low recall, and yield very noisy labels when problem, we present a bootstrapped positive- used as a source of labels for a supervised machine unlabeled learning algorithm that integrates learning algorithm. To address the drawbacks of domain-specific linguistic features to quickly and efficiently expand the seed dictionary. The dictionary-based labeling, Peng et al.(2019) pro- model achieves an average F1 score of 72.02% pose a positive-unlabeled (PU) NER approach that on a novel dataset of product descriptions, an labels positive instances using a seed dictionary, improvement of 3.63% over a baseline BiL- but makes no label assumptions for the remain- STM classifier, and in particular exhibits better ing tokens (Bekker and Davis, 2018). The authors recall (4.96% on average). validate their approach on the CoNLL, MUC and Twitter datasets for standard entity types, but it 1 Introduction is unclear how their approach transfers to the e- The vast majority of existing named entity recogni- commerce domain and its entity types. tion (NER) methods focus on a small set of promi- nent entity types, such as persons, organizations, Contributions We adopt the PU algorithm of diseases, and genes, for which labeled datasets are Peng et al.(2019) to the domain of consumer elec- readily available (Tjong Kim Sang and De Meulder, tronic product descriptions, and evaluate its effec- 2003; Smith et al., 2008; Weischedel et al., 2011; tiveness on four entity types: Product, Component, Li et al., 2016). There is a marked lack of studies in Brand and Attribute. Our algorithm bootstraps many other domains, such as e-commerce, and for NER with a seed dictionary, iteratively labels more novel entity types, e.g. products and components. data and expands the dictionary, while account- The lack of annotated datasets in the e- ing for accumulated errors from model predictions. commerce domain makes it hard to apply super- During labeling, we utilize dependency parsing to vised NER methods. An alternative approach is efficiently expand dictionary matches in text. Our to use dictionaries (Nadeau et al., 2006; Yang experiments on a novel dataset of product descrip- et al., 2018), but freely available knowledge re- tions show that this labeling mechanism, combined sources, e.g. Wikidata (Vrandecicˇ and Krotzsch¨ , with a PU learning strategy, consistently improves 2014) or YAGO (Suchanek et al., 2007), contain F1 scores over a standard BiLSTM classifier. Iter- only very limited information about e-commerce ative learning quickly expands the dictionary, and entities. Manually creating a dictionary of suffi- further improves model performance. The pro- cient quality and coverage would be prohibitively posed approach exhibits much better recall than expensive. This is amplified by the fact that in the baseline model, and generalizes better to un- the e-commerce domain, entities are frequently ex- seen entities. 1 Proceedings of the 3rd Workshop on e-Commerce and NLP (ECNLP 3), pages 1–6 Online, July 10, 2020. c 2020 Association for Computational Linguistics Algorithm 1: Iterative Bootstrapping NER Input: Dictionary