Mind the Gap: Another look at the problem of the semantic gap in image retrieval

This paper attempts to review and characterise the problem of the semantic gap in image retrieval and the attempts being made to bridge it. In particular, we draw from our own experience in user queries, automatic annotation and ontological techniques. The first section of the paper describes a characterisation of the semantic gap as a hierarchy between the raw media and full semantic understanding of the media's content. The second section discusses real users' queries with respect to the semantic gap. The final sections of the paper describe our own experience in attempting to bridge the semantic gap. In particular we discuss our work on auto-annotation and semantic-space models of image retrieval in order to bridge the gap from the bottom up, and the use of ontologies, which capture more semantics than keyword object labels alone, as a technique for bridging the gap from the top down

What is an Analogue for the Semantic Web and Why is Having One Important?

This paper postulates that for the Semantic Web to grow and gain input from fields that will surely benefit it, it needs to develop an analogue that will help people not only understand what it is, but what the potential opportunities are that are enabled by these new protocols. The model proposed in the paper takes the way that Web interaction has been framed as a baseline to inform a similar analogue for the Semantic Web. While the Web has been represented as a Page + Links, the paper presents the argument that the Semantic Web can be conceptualized as a Notebook + Memex. The argument considers how this model also presents new challenges for fundamental human interaction with computing, and that hypertext models have much to contribute to this new understanding for distributed information systems

Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision

Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpora, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and cross-lingual attention to further reduce noises. We conducted a series of experiments on three tasks: word translation, entity relatedness, and cross-lingual entity linking. The results, both qualitatively and quantitatively, demonstrate the significance of our method.Comment: 11 pages, EMNLP201