Merging Special Collections with GIS Technology to Enhance the User Experience

This analysis evaluates how PhillyHistory.org merged their unique special collection materials with geospatial-based progressive technology to challenge and educate the global community. A new generation of technologically savvy researchers has emerged that expect a more enhanced user experience than earlier generations. To meet these needs, collection managers are collaborating with community and local institutions to increase online access to materials; mixing best metadata practices with custom elements to create map mashups; and merging progressive GIS technology and geospatial based applications with their collections to enhance the user experience. The PhillyHistory.org website was analyzed to explore how they used various geospatial technology to create a new type of digital content management system based on geographical information and make their collections accessible via online software and mobile applications

A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape