This paper details the participation of the Australian eHealth Research Centre (AEHRC) in the ShARe/CLEF 2013 eHealth Evaluation Lab – Task 3. This task aims to evaluate the use of information retrieval (IR) systems to aid consumers (e.g. patients and their relatives) in seeking health advice on the Web. Our submissions to the ShARe/CLEF challenge are based on language models generated from the...

The task in Document Understanding Conferences (DUC) 2005 is to generate fixed length, user oriented, multi document summary. Our approach to address this task is primarily motivated by the observation that metrics based on key concepts overlap give better results when compared to metrics based on n-gram and sentence overlap. In this paper, we present a sentence extraction based summarization s...

This paper presents a framework for relevance-based belief change in propositional Horn logic. We firstly establish a parallel interpolation theorem for Horn logic and show that Parikh’s Finest Splitting Theorem holds with Horn formulae. By reformulating Parikh’s relevance criterion in the setting of Horn belief change, we construct a relevance-based partial meet Horn contraction operator and p...

A maximum likelihood approach to relevance feedback is introduced. This approach has the additional benefit of resolving document-query duality. The main idea is that in order to know whether and how much to modify a document and/or query in response to relevance feedback data, we need to have error models for documents, queries, and relevance feedback data. A maximum likelihood approach can th...

In this paper, we propose to use a subfield of machine learning –grammatical inference– to measure linguistic complexity from a developmental point of view. We focus on relative complexity by considering a child learner in the process of first language acquisition. The relevance of grammatical inference models for measuring linguistic complexity from a developmental point of view is based on th...

This paper proposes a model to learn word embeddings with weighted contexts based on part-of-speech (POS) relevance weights. POS is a fundamental element in natural language. However, state-of-the-art word embedding models fail to consider it. This paper proposes to use position-dependent POS relevance weighting matrices to model the inherent syntactic relationship among words within a context ...

We extend relevance modeling to the link detection task of Topic Detection and Tracking (TDT) and show that it substantially improves performance. Relevance modeling, a statistical language modeling technique related to query expansion, is used to enhance the topic model estimate associated with a news story, boosting the probability of words that are associated with the story even when they do...

This paper presents the result of the team of the University of North Texas in the ImageCLEF 2011 Wikipedia and Medical Image Retrieval tasks. For Wikipedia image retrieval we compare the two query expansion methods: relevance models and query expansion using Wikipedia and flicker as external sources. The relevance models use a classic relevance feedback mechanism for Language models as propose...

We present a novel approach to re-ranking documents using language modeling (LM) and manual relevance feedback (RF). The documents returned by an initial search algorithm, called the Local Set, is reranked based on manual relevance feedback using a ranking function modified to perform at the local set level. Instead of using the query independent collection model, which is too general, we use t...

In this paper, we develop a hybrid system for pair language recognition using Gaussian mixture model (GMM) supervector connecting to support vector machine (SVM). The adaptation of relevance factor in maximum a posteriori (MAP) adaptation of GMM from universal background model (UBM) is studied. In conventional MAP, relevance factor is empirically given by a constant value. It has been proven th...