Existing zero-shot product attribute value (aspect) extraction approaches in e-Commerce industry rely on uni-modal or multi-modal models, where the sellers are asked to provide detailed textual inputs (product descriptions) for the products. However, manually providing (typing) the product descriptions is time-consuming and frustrating for the sellers. Thus, we propose a cross-modal zero-shot attribute value generation framework (ViOC-AG) based on CLIP, which only requires product images as the inputs. ViOC-AG follows a text-only training process, where a task-customized text decoder is trained with the frozen CLIP text encoder to alleviate the modality gap and task disconnection. During the zero-shot inference, product aspects are generated by the frozen CLIP image encoder connected with the trained task-customized text decoder. OCR tokens and outputs from a frozen prompt-based LLM correct the decoded outputs for out-of-domain attribute values. Experiments show that ViOC-AG significantly outperforms other fine-tuned vision-language models for zero-shot attribute value extraction.

This paper presents a method for constructing a specific type of language resources that are conveniently applicable to analysis of trending topics in time-annotated textual data. More specifically, the method consists of building a co-occurrence network from the on-line content (such as New York Times articles) that conform to key words selected by users (e.g., ‘Arab Spring’). Within the network, burstiness of the particular nodes (key words) and edges (co-occurrence relations) is computed. A service deployed on the network then facilitates exploration of the underlying text in order to identify trending topics. Using the graph structure of the network, one can assess also a broader context of the trending events. To limit the information overload of users, we filter the edges and nodes displayed by their burstiness scores to show only the presumably more important ones. The paper gives details on the proposed method, including a step-by-step walk through with plenty of real data examples. We report on a specific application of our method to the topic of ‘Arab Spring’ and make the language resource applied therein publicly available for experimentation. Last but not least, we outline a methodology of an ongoing evaluation of our method.