L. Azzopardi

Publications290
h-index36
Citations5,167
Highly Influential Citations321
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Formal models for expert finding in enterprise corpora
TLDR
This work presents two general strategies to expert searching given a document collection which are formalized using generative probabilistic models, and shows that the second strategy consistently outperforms the first.
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A language modeling framework for expert finding
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Building simulated queries for known-item topics: an analysis using six european languages
TLDR
A model with improved document and term selection properties is proposed, showing that simulated known- item topics can be generated that are comparable to real known-item topics.
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Retrievability: an evaluation measure for higher order information access tasks
TLDR
This paper provides the foundations for the evaluation of higher order access related tasks and performs an extensive analysis on two TREC collections showing how the measures can be applied to evaluate different information access questions.
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Integrating and Evaluating Neural Word Embeddings in Information Retrieval
TLDR
This paper uses neural word embeddings within the well known translation language model for information retrieval, which captures implicit semantic relations between the words in queries and those in relevant documents, thus producing more accurate estimations of document relevance.
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Broad expertise retrieval in sparse data environments
TLDR
Two main expertise retrieval tasks are presented, along with a set of baseline approaches based on generative language modeling, aimed at finding expertise relations between topics and people, and current techniques appear to be generalizable to other settings.
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Using the Quantum Probability Ranking Principle to Rank Interdependent Documents
TLDR
It is shown that the application of quantum theory to problems within information retrieval can lead to significant improvements, and the QPRP outperforms other ranking strategies for subtopic retrieval.
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CLEF 2018 Technologically Assisted Reviews in Empirical Medicine Overview
TLDR
A benchmark collection of fifty systematic reviews and the corresponding relevant and irrelevant articles found by the original Boolean query is constructed, using information retrieval and machine learning algorithms over a variety of text representations.
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The Combination and Evaluation of Query Performance Prediction Methods
TLDR
The current evaluation methodology is critically examined and it is shown how using linear correlation coefficients do not provide an intuitive measure indicative of a method's quality, can provide a misleading indication of performance, and overstate the performance of combined methods.
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Conceptualizing agent-human interactions during the conversational search process
TLDR
This paper develops a conceptual framework of the actions and intents of users and agents explaining how these actions enable the user to explore the search space and resolve their information need.
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