In this paper we present a bootstrapping approach for attribute value extraction that minimizes the need for human intervention. Our approach automatically extracts attribute names and values from semi-structured text, generates a small labelled dataset, and bootstraps it by extracting new values from unstructured text. It is domain/language-independent, relying only on existing semi-structured text to create the initial labeled dataset. We assess the impact of different machine learning approaches to increase precision of the core approach without compromising coverage. We perform an extensive evaluation using e-commerce product data across different categories in two languages and hundreds of thousands of product pages. We show that our approach provides high precision and good coverage. In addition, we study the impact of different methods that address specific sources of error. With error analysis we highlight how these methods complement each other, obtaining insights about the individual methods and the ensamble as a whole.

Language and vision provide complementary information. Integrating both modalities in a single multimodal representation is an unsolved problem with wide-reaching applications to both natural language processing and computer vision. In this paper, we present a simple and effective method that learns a language-to-vision mapping and uses its output visual predictions to build multimodal representations. In this sense, our method provides a cognitively plausible way of building representations, consistent with the inherently re-constructive and associative nature of human memory. Using seven benchmark concept similarity tests we show that the mapped (or imagined) vectors not only help to fuse multimodal information, but also outperform strong unimodal baselines and state-of-the-art multimodal methods, thus exhibiting more human-like judgments. Ultimately, the present work sheds light on fundamental questions of natural language understanding concerning the fusion of vision and language such as the plausibility of more associative and re-constructive approaches.