Shifting NER into High Gear: The Auto-AdvER Approach

This paper presents a case study on the development of Auto-AdvER, a specialised named entity recognition schema and dataset for text in the car advertisement genre. Developed with industry needs in mind, Auto-AdvER is designed to enhance text mining analytics in this domain and contributes a linguistically unique NER dataset. We present a schema consisting of three labels: "Condition", "Historic" and "Sales Options". We outline the guiding principles for annotation, describe the methodology for schema development, and show the results of an annotation study demonstrating inter-annotator agreement of 92% F1-Score. Furthermore, we compare the performance by using encoder-only models: BERT, DeBERTaV3 and decoder-only open and closed source Large Language Models (LLMs): Llama, Qwen, GPT-4 and Gemini. Our results show that the class of LLMs outperforms the smaller encoder-only models. However, the LLMs are costly and far from perfect for this task. We present this work as a stepping stone toward more fine-grained analysis and discuss Auto-AdvER's potential impact on advertisement analytics and customer insights, including applications such as the analysis of market dynamics and data-driven predictive maintenance. Our schema, as well as our associated findings, are suitable for both private and public entities considering named entity recognition in the automotive domain, or other specialist domains.

Towards End-User Development for IoT: A Case Study on Semantic Parsing of Cooking Recipes for Programming Kitchen Devices

Semantic parsing of user-generated instructional text, in the way of enabling end-users to program the Internet of Things (IoT), is an underexplored area. In this study, we provide a unique annotated corpus which aims to support the transformation of cooking recipe instructions to machine-understandable commands for IoT devices in the kitchen. Each of these commands is a tuple capturing the semantics of an instruction involving a kitchen device in terms of "What", "Where", "Why" and "How". Based on this corpus, we developed machine learning-based sequence labelling methods, namely conditional random fields (CRF) and a neural network model, in order to parse recipe instructions and extract our tuples of interest from them. Our results show that while it is feasible to train semantic parsers based on our annotations, most natural-language instructions are incomplete, and thus transforming them into formal meaning representation, is not straightforward.