Robyn Loughnane


2024

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Explicit Attribute Extraction in e-Commerce Search
Robyn Loughnane | Jiaxin Liu | Zhilin Chen | Zhiqi Wang | Joseph Giroux | Tianchuan Du | Benjamin Schroeder | Weiyi Sun
Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024

This paper presents a model architecture and training pipeline for attribute value extraction from search queries. The model uses weak labels generated from customer interactions to train a transformer-based NER model. A two-stage normalization process is then applied to deal with the problem of a large label space: first, the model output is normalized onto common generic attribute values, then it is mapped onto a larger range of actual product attribute values. This approach lets us successfully apply a transformer-based NER model to the extraction of a broad range of attribute values in a real-time production environment for e-commerce applications, contrary to previous research. In an online test, we demonstrate business value by integrating the model into a system for semantic product retrieval and ranking.

2017

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Linked Data for Language-Learning Applications
Robyn Loughnane | Kate McCurdy | Peter Kolb | Stefan Selent
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

The use of linked data within language-learning applications is an open research question. A research prototype is presented that applies linked-data principles to store linguistic annotation generated from language-learning content using a variety of NLP tools. The result is a database that links learning content, linguistic annotation and open-source resources, on top of which a diverse range of tools for language-learning applications can be built.