Retrieval cues fail to influence contextualized evaluations

Contextualized PACRR for Complex Answer Retrieval

In this work, we present our submission to the TREC Complex Answer Retrieval (CAR) task. Our approach uses a variation of the Position-Aware Convolutional Recurrent Relevance Matching (PACRR) [2] deep neural model to re-rank passages. Modifications include an expanded convolutional kernel size, and contextual vectors to capture heading type (e.g. title), heading frequency, and query term occurr...

Why Evaluations Fail: The Importance of Good Monitoring

Myriad: An Architecture for Contextualized Information Retrieval and Delivery

Users’ information needs are largely driven by the context in which they make their decisions. This context is dynamic. It includes the users’ characteristics, their current domain of application, the tasks they commonly perform and the device they are currently using. This context is also evolving. When one information need is satisfied, another is likely to emerge. An information access syste...

Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries

This paper presents a procedure to retrieve subsets of relevant documents from large text collections for Content Analysis, e.g. in social sciences. Document retrieval for this purpose needs to take account of the fact that analysts often cannot describe their research objective with a small set of key terms, especially when dealing with theoretical or rather abstract research interests. Instea...

Query Refinement and User Relevance Feedback for Contextualized Image Retrieval

The motivation of this paper is to increase the user perceived precision of results of Content Based Information Retrieval (CBIR) systems with Query Refinement (QR), Visual Analysis (VA) and Relevance Feedback (RF) algorithms. The proposed algorithms were implemented as modules into K-Space CBIR system. The QR module discovers hypernyms for the given query from a free text corpus (Wikipedia) an...