57 citations · 64 across the 4 of their papers we have counts for
5 papers
Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing
Kai Hui, Klaus Berberich
Preference judgments have been demonstrated as a better alternative to graded judgments to assess the relevance of documents relative to queries. Existing work has verified transit…
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
Kai Hui, Andrew Yates, Klaus Berberich +1
Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals. We argue that the context of these matching signals is…
PACRR: A Position-Aware Neural IR Model for Relevance Matching
Kai Hui, Andrew Yates, Klaus Berberich +1
In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given use…
Knowledge Questions from Knowledge Graphs
Dominic Seyler, Mohamed Yahya, Klaus Berberich
We address the novel problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Questions of this kind have ample applications, for…
Computing n-Gram Statistics in MapReduce
Klaus Berberich, Srikanta Bedathur
Statistics about n-grams (i.e., sequences of contiguous words or other tokens in text documents or other string data) are an important building block in information retrieval and n…