DutchHatTrick: semantic query modeling, ConText, section detection, and match score maximization

This report discusses the collaborative work of the ErasmusMC, University of Twente, and the University of Amsterdam on the TREC 2011 Medical track. Here, the task is to retrieve patient visits from the University of Pittsburgh NLP Repository for 35 topics. The repository consists of 101,711 patient reports, and a patient visit was recorded in one or more reports

University of Twente at GeoCLEF 2006: geofiltered document retrieval

In this report we describe the approach of the University of Twente to the 2006 Geo-CLEF task. It is based on retrieval by content and the subsequent filtering by geographical relevance utilizing a gazetteer. The results do not show an improvement inretrieval performance when taking geographical information into account

Peer to Peer Information Retrieval: An Overview

Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom

Onebox: Free-Text Interfaces as an Alternative to Complex Web Forms

This paper investigates the problem of translating free-text\ud queries into key-value pairs as an alternative means for searching `behind' web forms. We introduce a novel specication language for specifying free-text interfaces, and report the results of a user study where we evaluated our prototype in a travel planner scenario. Our results show that users prefer this free-text interface over the original web form and that they are about 9% faster on average at completing their search tasks

Learning to extract folktale keywords

Manually assigned keywords provide a valuable means for accessing large document collections. They can serve as a shallow document summary and enable more efficient retrieval and aggregation of information. In this paper we investigate keywords in the context of the Dutch Folktale Database, a large collection of stories including fairy tales, jokes and urban legends. We carry out a quantitative and qualitative analysis of the keywords in the collection. Up to 80% of the assigned keywords (or a minor variation) appear in the text itself. Human annotators show moderate to substantial agreement in their judgment of keywords. Finally, we evaluate a learning to rank approach to extract and rank keyword candidates. We conclude that this is a promising approach to automate this time intensive task

An exploration of language identification techniques for the Dutch folktale database

The Dutch Folktale Database contains fairy tales, traditional legends, urban legends, and jokes written in a large variety and combination of languages including (Middle and 17th century) Dutch, Frisian and a number of Dutch dialects. In this work we compare a number of approaches to automatic language identification for this collection. We show that in comparison to typical language identification tasks, classification performance for highly similar languages with little training data is low. The studied dataset consisting of over 39,000 documents in 16 languages and dialects is available on request for followup research